Jekyll2024-02-06T15:23:09+00:00http://jack-kelly.com/feed.xmlJack KellyI try to mitigate climate change using computer science. I co-founded [Open Climate Fix](http://openclimatefix.org), a non-profit research lab focused on reducing greenhouse gas emissions. Previously, I was a Research Engineer at [DeepMind](https://deepmind.com), where I used machine learning to predict wind power.Helping to speed up Zarr2023-07-28T00:00:00+01:002023-07-28T00:00:00+01:00http://jack-kelly.com/blog/Speeding-up-Zarr<p><strong>TL;DR:</strong> To enable many more people to train machine learning models on large, dense, multidimensional datasets (like weather and satellite datasets), I’m planning to dedicate several days a week (starting in September) to helping to speed up <a href="https://zarr.dev">Zarr</a>. This blog post describes my motivations for helping to speed up Zarr. And goes into a little detail on <em>how</em> I hope to speed up Zarr.</p>
<p>My ultimate goal remains the same as it’s been for the last few years: To help to mitigate climate change by substantially improving energy forecasting. Crucially, the goal is to help as many organisations as possible: To help <em>other</em> people who forecast renewable energy generation (because that’s the fastest way to climate impact).</p>
<p>This is a long-game. We need to lay some important foundations first. The idea is to take a “big swing”: and for me to be laser-focused on this work for the next few years. As such, starting in September, my aim is to spend several days a week writing code to lay these foundations.</p>
<p>I’m more excited than ever about applying cutting-edge machine learning (especially <a href="https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)">transformers</a>) to energy forecasting. The tools & data exist to do incredible things. I think the results could be really impressive. I want to move beyond ML models which “just” convert numerical weather predictions to power. I want to train ML models which learn atmospheric physics (like <a href="https://arxiv.org/abs/2306.06079">MetNet-3</a> from Google) from the data and, crucially, learn to use the large amount of real-time data that’s available from sensors on the ground (such as solar systems). What does it take to do that? Training on enormous numbers of training examples, as fast as possible! (As well as many other requirements!)</p>
<p>Let’s take a step back and ask what has enabled the recent and very dramatic improvement in large language models (LLMs) (such as <a href="https://en.wikipedia.org/wiki/GPT-4">OpenAI’s GPT-4</a>)?</p>
<p>One thing that enabled LLMs to make a huge leap forwards is the ability to train efficiently on enormous quantities of text. It’d be great to do the same for the weather. Weather observations are represented as multidimensional datasets, and to train models effectively we need to be able to sample freely and quickly from these multidimensional datasets (without spending a fortune!)</p>
<p>At their core, LLMs are transformers. Arguably, one of the main advantages that transformers have over previous sequence-to-sequence models is that transformers can be trained <em>very</em> efficiently on a significant proportion of all the text on the Internet. (Recurrent neural networks (RNNs), in contrast, are slow to train on GPUs). OpenAI put an enormous amount of engineering effort into getting data quickly into GPT-4 during training, and on developing huge datasets (including some synthetic data).</p>
<h3 id="the-challenge-of-sampling-from-multidimensional-datasets">The challenge of sampling from multidimensional datasets</h3>
<p>To train our solar-forecasting ML models, we need to sample arbitrary slices from large multidimensional datasets. For example, each ML training example might consist of a “slice” of satellite data that consists of 4 timesteps, 128 pixels high x 128 pixel wides, and 10 spectral channels. Each “slice” is sampled from a random location from the entire dataset, which might be on the order of 100 TBytes in size.</p>
<p>If there’s no rush, then reading these “random slices” is perfectly do-able with existing tools. The challenge we face is that we need to read these random slices <em>really</em> quickly in order to keep the GPU fed with data during ML training. In practice, we find that we need to load data at a few gigabytes per second (per machine) in order to keep the GPUs fed.</p>
<p>Unfortunately, sampling quickly from multidimensional arrays is harder than sampling quickly from text. The fundamental challenge with multidimensional arrays is that computer data storage systems (RAM, spinning disks, SSDs, etc.) all store data as a 1-dimensional sequence of bytes. Which is a great fit for text (which is also 1D). But not such a great fit for multidimensional arrays.</p>
<p>To store a multidimensional array in a computer, the array has to be “serialised” into a 1D sequence of bytes. But, once the elements are in 1D, elements which <em>were</em> close together in <em>n</em> dimensions may now be a long distance apart on disk. This is a problem because most computer storage systems perform best when data is read <em>sequentially</em> (rather than sparsely). And, to make matters worse, if you compress the data on disk then it’s no longer obvious where each element of the array lives on disk. The end result is that reading a single multidimensional slice (which is contiguous in <em>n</em> dimensions) may require many sparse reads from disk. And sparse reads tend to be slow (to be more specific: sparse reads will always be slow from HDDs because it takes time for the HDD’s read-head to move to a new location. SSDs, in contrast, can read from random locations very quickly, but only if the SSD is given a very long list of random reads at once, so it can fully saturate its internal parallel data paths).</p>
<p>The solution that many people use is to break their dataset into multidimensional chunks, compress each chunk individually, and store those chunks on disk. This is what Zarr does (and TileDB, and c-blosc2, and others). Then we read entire chunks from disk (which is fast because each chunk should be stored sequentially on disk). Now the issue is that, to minimise the amount of “wasted” data read when the requested slice doesn’t precisely match the size of the chunks on disk, we want the chunks to be as small as possible. But that, again, requires very high performance for random reads.</p>
<p>Since OCF’s beginning in 2019, we’ve been using Zarr-Python to store almost all of our data. Zarr-Python is a great tool! But it’s not especially well-tuned for loading data into ML models during training because Zarr-Python is not particularly fast (yet!). In OCF, we’ve tried lots of complicated work-arounds to allow us to load data into our ML models fast enough, but all these workarounds have serious limitations (for example, pre-preparing training batches is an obvious solution, but it takes many days to pre-prepare the batches, which is a surprisingly large drag on the rate at which we can try new experiments. And, of course, it takes up a lot of storage space, which in turn limits us to train on a relatively small set of batches.)</p>
<p>While the rest of the OCF team will still be working on the same great ML research which has allowed us to keep pushing the boundaries of forecast performance over the last 18 months, I will be spending most of my time forging ahead with this longer arc problem.</p>
<p>My plan is to spend several days a week for up to a few years laser-focused on implementing this plan, and one day a week working with the OCF team on the current models and plans.</p>
<h3 id="speeding-up-reading-arbitrary-slices-from-multidimensional-datasets">Speeding up reading arbitrary slices from multidimensional datasets</h3>
<p>It’s <em>possible</em> that there are existing tools which will do what we need. So - after discussion with the lead author of <a href="https://google.github.io/tensorstore/">TensorStore</a> and other folks in the Zarr community - my first task is to benchmark existing implementations of Zarr. The benchmarking code will be open-source, and I plan to write a detailed blog about the results. I’ll selfishly focus on my main use-case: reading random crops of lightly compressed multidimensional data from a local SSD, using Linux (mimicking what our ML training code does).</p>
<p>If the benchmarking suggests that no existing tool fully saturates IO for our use-case, then we can probably conclude that we’ve identified a bit of a gap in the existing tooling: It’s currently hard to cheaply read random slices from multidimensional datasets at the speed required to train ML models (multiple gigabytes per second). This is a problem for multiple domains. For example, neuroscientists are busy building petabyte-scale 3D imagery datasets (by slicing brains very thinly, and scanning those slices at very high resolution). Climate scientists have been using enormous multidimensional datasets for years.</p>
<p>My hope is that we can enable many more people to do this sort of work by building a super-performant Zarr library (or perhaps by re-implementing just Zarr-Python’s performance bottlenecks). Being able to sample freely from training data would have multiple benefits for OCF and hopefully the climate: We’d be able to generate <em>trillions</em> of examples on-the-fly, using much simpler data processing code, and without having to pre-prepare batches. To put this another way: We’d be able to train our models faster, on more examples, and we’d be able to test new ideas much faster. And these benefits would apply for any project that requires large multidimensional datasets (not <em>just</em> solar forecasting).</p>
<p>To quote a comment on a draft version of this blog post from <a href="http://stephanhoyer.com/">Stephan Hoyer</a> (research lead at Google, and the creator of <a href="https://github.com/pydata/xarray">Xarray</a>):</p>
<blockquote>
<p>“The advantage of a fast Zarr reading is that you can do more preprocessing of data on the fly with minimal performance consequences (e.g., choice of variables and/or time slicing), and you don’t necessarily need to store duplicated shuffled copies of your data…</p>
<p>There are also two other big advantages of fast random access to Zarr in my opinion:</p>
<ol>
<li>Preserving metadata. It’s much easier to debug bad interactions between models and data when you have the full metadata to identify where each bit of data came from, rather than just knowing that it’s a “random batch.”</li>
<li>Reproducible & deterministic training. For debugging purposes, it’s invaluable to have deterministic training scripts. This can get tricky when combined when using cloud infrastructure that may be pre-empted at any time. It’s way easier to write pre-emption robust code if you can quickly index into arbitrary locations in the training data, with indices that only depend on the training step number.”</li>
</ol>
</blockquote>
<p>There’s a “democratisation” argument too: A fast Zarr library should make it easier for more people to experiment with training ML models on multidimensional datasets.</p>
<p>And, at OCF, we’ve always wanted to contribute open-source tools that help lots of people. (Personally, I feel we actually have a moral obligation to contribute back to the open-source community, given how much open-source code we use!)</p>
<p>So, my proposal is that I build a new Zarr reader, which is heavily optimised for read-speed (by implementing in the Rust programming language, and using tools like <a href="https://tokio.rs/">tokio</a> and <a href="https://news.ycombinator.com/item?id=23133040">io_uring</a> for asynchronous IO). The basic idea is to build something that is as efficient as possible at using the hardware, whilst also exposing two simple Python APIs: one which mimics the existing Zarr-Python API (to make it as easy as possible for folks to use the new code), and a new async Python API. Or, perhaps, it will be sufficient “just” to re-implement parts of Zarr-Python in Rust. I will also get much more involved in the Zarr community. (See the <a href="#appendix">Appendix</a> for more details, and a brief discussion of other Zarr implementations)</p>
<h3 id="timeline">Timeline</h3>
<p>It’ll be a while before anything concrete happens. I need to finish off some stuff at OCF. And then I need to get better at Rust :). And I’m on a family holiday for 3 weeks in Aug. But hopefully, from about September onwards, I’ll be able to spend about several days a week on Zarr-Rust. Then, after I get a functional MVP of Zarr-Rust running, I’ll probably transition to spending about half my time on ML, and half on Zarr-Rust. I honestly have no idea how long this will take, but I’d estimate that it’ll take something like a year to get an MVP Zarr-Rust up-and-running. (Please see the section on <a href="#mvp">When will we know if this approach is worth pursuing?</a> in the Appendix for more discussion of what counts as the MVP).</p>
<p>But please shout if you think this is no longer necessary! Or if you have any feedback at all!</p>
<p>And, of course, I’d do everything in the open, and discuss as much as possible with the Zarr community.</p>
<h2 id="appendix"><a name="appendix"></a>Appendix</h2>
<h3 id="tell-me-more-about-this-super-performant-library-for-reading-zarr">Tell me more about this “super-performant library for reading Zarr”!</h3>
<p>The idea is to write a new Zarr library in Rust. With Python bindings. The idea would be to expose two Python APIs: one which is as similar to zarr-python’s API as possible (to enable folks to use zarr-rust without changing any of their code); and another API that is fully async.</p>
<p>Crucial to this project will be building Zarr-Rust out-in-the-open, and in collaboration with the wonderful existing Zarr community!</p>
<p>The use-case I have in mind would involve trying to read & decompress thousands of small chunks per second (and maybe orders of magnitude more). Those small chunks would live on disk in shards.</p>
<p><a href="https://zarr.dev/zeps/draft/ZEP0002.html">Zarr’s sharding proposal</a> changes the “Zarr paradigm” from reading a small number of large chunks to a large number of small chunks. This fundamentally changes the data access pattern. It becomes much more important to have performant async IO. We might want to read on the order of a <em>million</em> chunks per second.</p>
<h3 id="what-makes-you-think-that-its-possible-to-go-faster-than-existing-zarr-implementations">What makes you think that it’s possible to go faster than existing Zarr implementations?</h3>
<p>I must admit that I can’t be <em>certain</em> that it’s possible to go any faster.</p>
<p>That said, there are a few theoretical reasons to believe that it <em>might</em> be possible to go faster. (But - all too often - theoretical benefits don’t necessarily materialise!):</p>
<ul>
<li>I don’t think any existing Zarr implementation uses <a href="https://news.ycombinator.com/item?id=23133040">io_uring</a> (a newish feature of the Linux kernel that allows for async IO - including file operations and network operations - without any memory copying, and with minimal system calls). <a href="https://www.scylladb.com/2020/05/05/how-io_uring-and-ebpf-will-revolutionize-programming-in-linux/">Some database folks</a> seem pretty excited about io_uring. <a href="https://www.phoronix.com/news/Linux-5.6-IO-uring-Tests">Some benchmarks</a> show that io_uring can deliver almost 20x more IOPs for random reads than the previous approach.</li>
<li>Zarr-Python is entirely single-threaded. Some other Zarr implementations use thread pools. But I believe that, theoretically, an async runtime like <a href="https://tokio.rs/">tokio</a> (with a work-stealing scheduler) should be able to process more tasks per second, with less CPU & memory overhead. OS threads are surprisingly expensive: both in terms of RAM, and the cost of context switching. If we are serious about reading and decompressing a million chunks per second, then it would be madness to try to spawn a million OS threads! Or course, tokio uses threads under the hood. But it’s at least possible that tokio will use threads more effectively than a hand-crafted scheduler.</li>
<li>I’m also interested in doing some processing (downsampling, normalisation, etc.) on each chunk, in parallel, whilst the chunk is still in the CPU’s cache after decompression (like <a href="https://github.com/Blosc/c-blosc2">c-blosc2</a>). But I think <a href="https://github.com/google/tensorstore/blob/0eee84336eb33ada9dc9e546072221a597678380/tensorstore/downsample.h#L48">TensorStore already does that</a>.</li>
</ul>
<h3 id="i-may-write-two-rust-crates-a-general-purpose-high-performance-async-file-io-library-and-a-zarr-front-end">I may write two Rust crates: A general-purpose high-performance async file IO library; and a Zarr front-end.</h3>
<p>Zarr isn’t the only thing around that could benefit from fast, async file IO for huge numbers of files per second. So I may actually write <em>two</em> Rust crates: a general purpose, high-performance library for async file IO operating concurrently on huge numbers of files (or chunks of files). And a “Zarr front-end”.</p>
<p>The IO library would use all the CPU cores to saturate the disk / network interface card, and may use different strategies depending on the performance characteristics of the storage medium. And perhaps the Rust IO package would optionally decompress each chunk, and glue the resulting chunks together into a single array, ready for passing back to Python land.</p>
<p>In a sense, this would be a bit like <a href="https://filesystem-spec.readthedocs.io/en/latest/">fsspec</a> (and would owe a lot to fsspec!), in that the Rust IO library would provide a common interface to local disks and cloud object storage, and the IO library wouldn’t be specific to Zarr. But, unlike fsspec, this library would be laser-focused on high performance async IO for huge numbers of files (or parts of files) at once. And might include multi-core compression / decompression, and glueing the chunks together into a single array.</p>
<p>And this “high speed IO library” would also be useful as the “backend” for a new high-performance tool for reading huge numbers of GRIB files or EUMETSAT .nat files. So we could convert from GRIB and .nat files to Zarr as efficiently as possible.</p>
<p>Please let me know in the comments if you can think of a good name for this “high speed IO library”!</p>
<h3 id="when-will-we-know-if-this-approach-is-worth-pursuing"><a name="mvp"></a>When will we know if this approach is worth pursuing?</h3>
<p>In general, I’m interested in exploiting platform-specific performance features (like io_uring in Linux). I’d plan to start with a proof of concept that only works on Linux, and only knows how to read from local files.</p>
<p>If it’s not possible to go faster than TensorStore by “cheating” (using platform-specific features) then I’ll give up :) (and that’s fine - I will have benefitted from learning Rust, and from gaining much more hands-on experience with how Zarr behaves. So it wouldn’t have been a waste of time.). If it looks promising then I’d add the ability to read from cloud buckets (maybe also using io_uring for network IO). And then extend to other operating systems.</p>TL;DR: To enable many more people to train machine learning models on large, dense, multidimensional datasets (like weather and satellite datasets), I’m planning to dedicate several days a week (starting in September) to helping to speed up Zarr. This blog post describes my motivations for helping to speed up Zarr. And goes into a little detail on how I hope to speed up Zarr. My ultimate goal remains the same as it’s been for the last few years: To help to mitigate climate change by substantially improving energy forecasting. Crucially, the goal is to help as many organisations as possible: To help other people who forecast renewable energy generation (because that’s the fastest way to climate impact). This is a long-game. We need to lay some important foundations first. The idea is to take a “big swing”: and for me to be laser-focused on this work for the next few years. As such, starting in September, my aim is to spend several days a week writing code to lay these foundations. I’m more excited than ever about applying cutting-edge machine learning (especially transformers) to energy forecasting. The tools & data exist to do incredible things. I think the results could be really impressive. I want to move beyond ML models which “just” convert numerical weather predictions to power. I want to train ML models which learn atmospheric physics (like MetNet-3 from Google) from the data and, crucially, learn to use the large amount of real-time data that’s available from sensors on the ground (such as solar systems). What does it take to do that? Training on enormous numbers of training examples, as fast as possible! (As well as many other requirements!) Let’s take a step back and ask what has enabled the recent and very dramatic improvement in large language models (LLMs) (such as OpenAI’s GPT-4)? One thing that enabled LLMs to make a huge leap forwards is the ability to train efficiently on enormous quantities of text. It’d be great to do the same for the weather. Weather observations are represented as multidimensional datasets, and to train models effectively we need to be able to sample freely and quickly from these multidimensional datasets (without spending a fortune!) At their core, LLMs are transformers. Arguably, one of the main advantages that transformers have over previous sequence-to-sequence models is that transformers can be trained very efficiently on a significant proportion of all the text on the Internet. (Recurrent neural networks (RNNs), in contrast, are slow to train on GPUs). OpenAI put an enormous amount of engineering effort into getting data quickly into GPT-4 during training, and on developing huge datasets (including some synthetic data). The challenge of sampling from multidimensional datasets To train our solar-forecasting ML models, we need to sample arbitrary slices from large multidimensional datasets. For example, each ML training example might consist of a “slice” of satellite data that consists of 4 timesteps, 128 pixels high x 128 pixel wides, and 10 spectral channels. Each “slice” is sampled from a random location from the entire dataset, which might be on the order of 100 TBytes in size. If there’s no rush, then reading these “random slices” is perfectly do-able with existing tools. The challenge we face is that we need to read these random slices really quickly in order to keep the GPU fed with data during ML training. In practice, we find that we need to load data at a few gigabytes per second (per machine) in order to keep the GPUs fed. Unfortunately, sampling quickly from multidimensional arrays is harder than sampling quickly from text. The fundamental challenge with multidimensional arrays is that computer data storage systems (RAM, spinning disks, SSDs, etc.) all store data as a 1-dimensional sequence of bytes. Which is a great fit for text (which is also 1D). But not such a great fit for multidimensional arrays. To store a multidimensional array in a computer, the array has to be “serialised” into a 1D sequence of bytes. But, once the elements are in 1D, elements which were close together in n dimensions may now be a long distance apart on disk. This is a problem because most computer storage systems perform best when data is read sequentially (rather than sparsely). And, to make matters worse, if you compress the data on disk then it’s no longer obvious where each element of the array lives on disk. The end result is that reading a single multidimensional slice (which is contiguous in n dimensions) may require many sparse reads from disk. And sparse reads tend to be slow (to be more specific: sparse reads will always be slow from HDDs because it takes time for the HDD’s read-head to move to a new location. SSDs, in contrast, can read from random locations very quickly, but only if the SSD is given a very long list of random reads at once, so it can fully saturate its internal parallel data paths). The solution that many people use is to break their dataset into multidimensional chunks, compress each chunk individually, and store those chunks on disk. This is what Zarr does (and TileDB, and c-blosc2, and others). Then we read entire chunks from disk (which is fast because each chunk should be stored sequentially on disk). Now the issue is that, to minimise the amount of “wasted” data read when the requested slice doesn’t precisely match the size of the chunks on disk, we want the chunks to be as small as possible. But that, again, requires very high performance for random reads. Since OCF’s beginning in 2019, we’ve been using Zarr-Python to store almost all of our data. Zarr-Python is a great tool! But it’s not especially well-tuned for loading data into ML models during training because Zarr-Python is not particularly fast (yet!). In OCF, we’ve tried lots of complicated work-arounds to allow us to load data into our ML models fast enough, but all these workarounds have serious limitations (for example, pre-preparing training batches is an obvious solution, but it takes many days to pre-prepare the batches, which is a surprisingly large drag on the rate at which we can try new experiments. And, of course, it takes up a lot of storage space, which in turn limits us to train on a relatively small set of batches.) While the rest of the OCF team will still be working on the same great ML research which has allowed us to keep pushing the boundaries of forecast performance over the last 18 months, I will be spending most of my time forging ahead with this longer arc problem. My plan is to spend several days a week for up to a few years laser-focused on implementing this plan, and one day a week working with the OCF team on the current models and plans. Speeding up reading arbitrary slices from multidimensional datasets It’s possible that there are existing tools which will do what we need. So - after discussion with the lead author of TensorStore and other folks in the Zarr community - my first task is to benchmark existing implementations of Zarr. The benchmarking code will be open-source, and I plan to write a detailed blog about the results. I’ll selfishly focus on my main use-case: reading random crops of lightly compressed multidimensional data from a local SSD, using Linux (mimicking what our ML training code does). If the benchmarking suggests that no existing tool fully saturates IO for our use-case, then we can probably conclude that we’ve identified a bit of a gap in the existing tooling: It’s currently hard to cheaply read random slices from multidimensional datasets at the speed required to train ML models (multiple gigabytes per second). This is a problem for multiple domains. For example, neuroscientists are busy building petabyte-scale 3D imagery datasets (by slicing brains very thinly, and scanning those slices at very high resolution). Climate scientists have been using enormous multidimensional datasets for years. My hope is that we can enable many more people to do this sort of work by building a super-performant Zarr library (or perhaps by re-implementing just Zarr-Python’s performance bottlenecks). Being able to sample freely from training data would have multiple benefits for OCF and hopefully the climate: We’d be able to generate trillions of examples on-the-fly, using much simpler data processing code, and without having to pre-prepare batches. To put this another way: We’d be able to train our models faster, on more examples, and we’d be able to test new ideas much faster. And these benefits would apply for any project that requires large multidimensional datasets (not just solar forecasting). To quote a comment on a draft version of this blog post from Stephan Hoyer (research lead at Google, and the creator of Xarray): “The advantage of a fast Zarr reading is that you can do more preprocessing of data on the fly with minimal performance consequences (e.g., choice of variables and/or time slicing), and you don’t necessarily need to store duplicated shuffled copies of your data… There are also two other big advantages of fast random access to Zarr in my opinion: Preserving metadata. It’s much easier to debug bad interactions between models and data when you have the full metadata to identify where each bit of data came from, rather than just knowing that it’s a “random batch.” Reproducible & deterministic training. For debugging purposes, it’s invaluable to have deterministic training scripts. This can get tricky when combined when using cloud infrastructure that may be pre-empted at any time. It’s way easier to write pre-emption robust code if you can quickly index into arbitrary locations in the training data, with indices that only depend on the training step number.” There’s a “democratisation” argument too: A fast Zarr library should make it easier for more people to experiment with training ML models on multidimensional datasets. And, at OCF, we’ve always wanted to contribute open-source tools that help lots of people. (Personally, I feel we actually have a moral obligation to contribute back to the open-source community, given how much open-source code we use!) So, my proposal is that I build a new Zarr reader, which is heavily optimised for read-speed (by implementing in the Rust programming language, and using tools like tokio and io_uring for asynchronous IO). The basic idea is to build something that is as efficient as possible at using the hardware, whilst also exposing two simple Python APIs: one which mimics the existing Zarr-Python API (to make it as easy as possible for folks to use the new code), and a new async Python API. Or, perhaps, it will be sufficient “just” to re-implement parts of Zarr-Python in Rust. I will also get much more involved in the Zarr community. (See the Appendix for more details, and a brief discussion of other Zarr implementations) Timeline It’ll be a while before anything concrete happens. I need to finish off some stuff at OCF. And then I need to get better at Rust :). And I’m on a family holiday for 3 weeks in Aug. But hopefully, from about September onwards, I’ll be able to spend about several days a week on Zarr-Rust. Then, after I get a functional MVP of Zarr-Rust running, I’ll probably transition to spending about half my time on ML, and half on Zarr-Rust. I honestly have no idea how long this will take, but I’d estimate that it’ll take something like a year to get an MVP Zarr-Rust up-and-running. (Please see the section on When will we know if this approach is worth pursuing? in the Appendix for more discussion of what counts as the MVP). But please shout if you think this is no longer necessary! Or if you have any feedback at all! And, of course, I’d do everything in the open, and discuss as much as possible with the Zarr community. Appendix Tell me more about this “super-performant library for reading Zarr”! The idea is to write a new Zarr library in Rust. With Python bindings. The idea would be to expose two Python APIs: one which is as similar to zarr-python’s API as possible (to enable folks to use zarr-rust without changing any of their code); and another API that is fully async. Crucial to this project will be building Zarr-Rust out-in-the-open, and in collaboration with the wonderful existing Zarr community! The use-case I have in mind would involve trying to read & decompress thousands of small chunks per second (and maybe orders of magnitude more). Those small chunks would live on disk in shards. Zarr’s sharding proposal changes the “Zarr paradigm” from reading a small number of large chunks to a large number of small chunks. This fundamentally changes the data access pattern. It becomes much more important to have performant async IO. We might want to read on the order of a million chunks per second. What makes you think that it’s possible to go faster than existing Zarr implementations? I must admit that I can’t be certain that it’s possible to go any faster. That said, there are a few theoretical reasons to believe that it might be possible to go faster. (But - all too often - theoretical benefits don’t necessarily materialise!): I don’t think any existing Zarr implementation uses io_uring (a newish feature of the Linux kernel that allows for async IO - including file operations and network operations - without any memory copying, and with minimal system calls). Some database folks seem pretty excited about io_uring. Some benchmarks show that io_uring can deliver almost 20x more IOPs for random reads than the previous approach. Zarr-Python is entirely single-threaded. Some other Zarr implementations use thread pools. But I believe that, theoretically, an async runtime like tokio (with a work-stealing scheduler) should be able to process more tasks per second, with less CPU & memory overhead. OS threads are surprisingly expensive: both in terms of RAM, and the cost of context switching. If we are serious about reading and decompressing a million chunks per second, then it would be madness to try to spawn a million OS threads! Or course, tokio uses threads under the hood. But it’s at least possible that tokio will use threads more effectively than a hand-crafted scheduler. I’m also interested in doing some processing (downsampling, normalisation, etc.) on each chunk, in parallel, whilst the chunk is still in the CPU’s cache after decompression (like c-blosc2). But I think TensorStore already does that. I may write two Rust crates: A general-purpose high-performance async file IO library; and a Zarr front-end. Zarr isn’t the only thing around that could benefit from fast, async file IO for huge numbers of files per second. So I may actually write two Rust crates: a general purpose, high-performance library for async file IO operating concurrently on huge numbers of files (or chunks of files). And a “Zarr front-end”. The IO library would use all the CPU cores to saturate the disk / network interface card, and may use different strategies depending on the performance characteristics of the storage medium. And perhaps the Rust IO package would optionally decompress each chunk, and glue the resulting chunks together into a single array, ready for passing back to Python land. In a sense, this would be a bit like fsspec (and would owe a lot to fsspec!), in that the Rust IO library would provide a common interface to local disks and cloud object storage, and the IO library wouldn’t be specific to Zarr. But, unlike fsspec, this library would be laser-focused on high performance async IO for huge numbers of files (or parts of files) at once. And might include multi-core compression / decompression, and glueing the chunks together into a single array. And this “high speed IO library” would also be useful as the “backend” for a new high-performance tool for reading huge numbers of GRIB files or EUMETSAT .nat files. So we could convert from GRIB and .nat files to Zarr as efficiently as possible. Please let me know in the comments if you can think of a good name for this “high speed IO library”! When will we know if this approach is worth pursuing? In general, I’m interested in exploiting platform-specific performance features (like io_uring in Linux). I’d plan to start with a proof of concept that only works on Linux, and only knows how to read from local files. If it’s not possible to go faster than TensorStore by “cheating” (using platform-specific features) then I’ll give up :) (and that’s fine - I will have benefitted from learning Rust, and from gaining much more hands-on experience with how Zarr behaves. So it wouldn’t have been a waste of time.). If it looks promising then I’d add the ability to read from cloud buckets (maybe also using io_uring for network IO). And then extend to other operating systems.Two levels of applying machine learning to solar power forecasting2023-05-25T00:00:00+01:002023-05-25T00:00:00+01:00http://jack-kelly.com/blog/Two-levels-of-applying-machine-learning-to-solar-power-forecasting<p>I’d like to propose that there are two broad “levels” of applying machine learning to solar power forecasting.
The first level is relatively easy, and everyone is doing it: I don’t know for sure, but I’m almost certain that
“level one ML” is what almost all power forecasting providers mean when they say they’re “using AI”
in their glossy sales brochures! The second level is <em>much</em> harder to achieve, but may provide
better forecasting skill (especially at forecast horizons up to a few hours ahead).</p>
<p>“Level one” approaches learn a “power curve” which maps from numerical weather predictions (NWPs)
to solar power. The simplest approach would be to use linear regression to map from NWP irradiance to solar power,
using NWP data from the NWP grid box closest to the solar PV site. (And this works surprisingly well!)
Or, to get a little fancier, folks train something like an XGBoost model that includes features such as:</p>
<ul>
<li>statistics from the last few days of solar power data,</li>
<li>NWPs from a handful of NWP grid boxes near to the target site,</li>
<li>NWPs from multiple NWP providers,</li>
<li>tilt & azimuth of the PV panels,</li>
<li>PV power forecasts from a physical model (e.g. from <a href="https://pvlib-python.readthedocs.io/en/stable/">pvlib</a>).</li>
</ul>
<p>These “level one” ML models don’t attempt to learn atmospheric physics, or how clouds move, or how to interpret satellite images.
They’re just trying to learn relatively simple relationships between a handful of input features, and PV power.
So it makes sense to use relatively simple models (linear regression, multilayer perceptrons, XGBoost, etc.).
But “level one” approaches are always limited by the forecasting skill and resolution of the NWPs.
And there are good reasons to believe that operational NWPs aren’t great at forecasting irradiance.</p>
<p>“Level two” approaches try to use machine learning to out-perform NWPs.
Here, we <em>do</em> want our ML model to learn atmospheric physics, and to interpret rich data sources.
For example, the ML model might try to predict how clouds evolve over the next few hours,
given satellite imagery of the last few hours of clouds (this was the task in
<a href="https://twitter.com/OpenClimateFix/status/1509510989213020160">the ML competition that Open Climate Fix helped to organise</a>). Or, for forecasts which extend days
into the future, you could imagine learning to infer irradiance (at the Earth’s surface)
from NWP humidity and temperature at the altitude of clouds (because operational NWPs are pretty good at predicting humidity and temperature,
but take a bunch of short cuts when predicting clouds and irradiance.)
Papers like <a href="https://ai.googleblog.com/2021/11/metnet-2-deep-learning-for-12-hour.html">Google Research’s MetNet-2 paper</a> (2021)
and <a href="https://www.deepmind.com/blog/nowcasting-the-next-hour-of-rain">DeepMind’s Precipitation Nowcasting paper</a> (2021)
demonstrate that this class of approach is <em>possible</em>: it is possible to train an ML model to predict weather variables,
and the ML predictions are often better than NWPs (especially at short forecast horizons).
But it takes a <em>lot</em> of data, compute, skill, time, and effort to pull this off.</p>
<p>Personally, I’m most interested in the “level two” approaches. Although, I’ll admit, it’s far harder to achieve than I originally thought!
And I’ll also admit that, for many users, a good “level one” approach is probably sufficient. But, I do still cling to the hope that good “level two”
approaches could be really helpful for some folks (especially electricity grid system operators).</p>
<p>At Open Climate Fix, we’re doing both “level one” and “level two”.</p>I’d like to propose that there are two broad “levels” of applying machine learning to solar power forecasting. The first level is relatively easy, and everyone is doing it: I don’t know for sure, but I’m almost certain that “level one ML” is what almost all power forecasting providers mean when they say they’re “using AI” in their glossy sales brochures! The second level is much harder to achieve, but may provide better forecasting skill (especially at forecast horizons up to a few hours ahead). “Level one” approaches learn a “power curve” which maps from numerical weather predictions (NWPs) to solar power. The simplest approach would be to use linear regression to map from NWP irradiance to solar power, using NWP data from the NWP grid box closest to the solar PV site. (And this works surprisingly well!) Or, to get a little fancier, folks train something like an XGBoost model that includes features such as: statistics from the last few days of solar power data, NWPs from a handful of NWP grid boxes near to the target site, NWPs from multiple NWP providers, tilt & azimuth of the PV panels, PV power forecasts from a physical model (e.g. from pvlib). These “level one” ML models don’t attempt to learn atmospheric physics, or how clouds move, or how to interpret satellite images. They’re just trying to learn relatively simple relationships between a handful of input features, and PV power. So it makes sense to use relatively simple models (linear regression, multilayer perceptrons, XGBoost, etc.). But “level one” approaches are always limited by the forecasting skill and resolution of the NWPs. And there are good reasons to believe that operational NWPs aren’t great at forecasting irradiance. “Level two” approaches try to use machine learning to out-perform NWPs. Here, we do want our ML model to learn atmospheric physics, and to interpret rich data sources. For example, the ML model might try to predict how clouds evolve over the next few hours, given satellite imagery of the last few hours of clouds (this was the task in the ML competition that Open Climate Fix helped to organise). Or, for forecasts which extend days into the future, you could imagine learning to infer irradiance (at the Earth’s surface) from NWP humidity and temperature at the altitude of clouds (because operational NWPs are pretty good at predicting humidity and temperature, but take a bunch of short cuts when predicting clouds and irradiance.) Papers like Google Research’s MetNet-2 paper (2021) and DeepMind’s Precipitation Nowcasting paper (2021) demonstrate that this class of approach is possible: it is possible to train an ML model to predict weather variables, and the ML predictions are often better than NWPs (especially at short forecast horizons). But it takes a lot of data, compute, skill, time, and effort to pull this off. Personally, I’m most interested in the “level two” approaches. Although, I’ll admit, it’s far harder to achieve than I originally thought! And I’ll also admit that, for many users, a good “level one” approach is probably sufficient. But, I do still cling to the hope that good “level two” approaches could be really helpful for some folks (especially electricity grid system operators). At Open Climate Fix, we’re doing both “level one” and “level two”.Using computing to fix climate change2019-10-03T00:00:00+01:002019-10-03T00:00:00+01:00http://jack-kelly.com/blog/using-computing-to-fix-climate-change<p>If you’d like to use computer science to help mitigate climate change, then check out these resources:</p>
<ul>
<li><a href="http://climatechange.ai/">ClimateChange.AI</a> - All about using AI to mitigate and adapt to climate change. Check out their excellent <a href="https://arxiv.org/abs/1906.05433">2019 paper</a>; <a href="https://www.climatechange.ai/mailing_list.html">sign up</a> to their newsletter, and <a href="https://community.climatechange.ai">join their forum</a>! Also check out Paul Strobel’s six-part illustrated <a href="https://blog.codecentric.de/en/2019/09/how-to-tackle-climate-change-with-machine-learning-electricity-systems/#post-69396">blog series</a> about the paper. And <a href="https://community.climatechange.ai/c/jobs/list-climate-change-ml-job-resources">here’s their list of climate change & ML job resources</a>.</li>
<li><a href="https://climatebase.org/">CLIMATEBASE</a> - “Make climate your career - Discover jobs at thousands of exciting climate tech companies and nonprofits around the world.”</li>
<li><a href="https://workonclimate.org/">Work on Climate</a> - “an action-oriented Slack community for people serious about climate work. Find climate jobs. Build climate companies. Find your people.”</li>
<li><a href="http://openclimatefix.org">Open Climate Fix</a> - OCF is the non-profit that I co-founded. Entirely focused on using open science (especially machine learning) to mitigate climate change. Please <a href="https://eepurl.com/guCjvH">sign up</a> to our newsletter and consider <a href="https://airtable.com/shrl59GJ96csVF4WB">adding your name to our list of volunteers</a>; and <a href="https://openclimatefix.discourse.group/">join our discussion forum</a>!</li>
<li><a href="https://sliced.org/">Sliced</a> - ‘We help you find opportunities that have a direct impact on reversing climate change.’</li>
<li><a href="https://climateaction.tech/">ClimateAction.Tech</a> - ‘A global community of tech professionals using our skills, expertise and platforms to support solutions to the climate crisis.’</li>
<li><a href="http://worrydream.com/ClimateChange/#">Bret Victor’s blog: What can a technologist do about climate change - 2015</a></li>
<li><a href="https://dynamicallytyped.com/">Dynamically Typed newsletter</a> - bi-weekly newsletter about AI-powered products and ML research that also features new projects and resources for using AI to tackle the climate crisis in every edition.</li>
</ul>
<p>If you know of other resources, then please add a comment below or, even better, <a href="https://github.com/JackKelly/JackKelly.github.io/blob/master/_posts/2019-10-03-using-computing-to-fix-climate-change.md">submit a pull request</a> to improve this list!</p>
<h2 id="the-challenge-of-reducing-emissions-at-scale">The challenge of reducing emissions at scale</h2>
<p>One quick rant: All the climate cares about is the scale of emissions reductions. For the climate to ‘notice’, we need to reduce emissions by <em>at least</em> a million tonnes of CO2e per year, and ideally <em>billions</em> of tonnes per year.</p>
<p>To achieve scale we need to jump over multiple hurdles:</p>
<ol>
<li>Have a great research idea.</li>
<li>Talk to industry to check that your idea solves a real problem. (I don’t know about you, but I find it all to easy to fixate on solving ‘toy’ problems. It’s essential that we focus on solving ‘real’ problems.)</li>
<li>Build a proof-of-concept that demonstrates emission reductions in a controlled environment.</li>
<li>Build a product (or get your idea implemented into an existing product) that reduces emissions <em>and</em> solves problems that industry cares about, at a price they can afford, and in a way which fits easily into their existing systems.</li>
<li>Persuade industry to adopt the product / idea. (Marketing, support, iterating on the product after receiving critical feedback, etc.)</li>
<li>Become financially sustainable.</li>
<li>Reduce emissions! (Even if you succeed at all the previous steps, that’s no guarantee that you’ll reduce emissions at scale. Maybe there’s a rebound effect? Maybe there are some other unintended consequences?)</li>
</ol>
<p>Projects tend to attract most funding and media attention after completing a proof-of-concept. But we need to get all the way to the final step before we can be confident that a given intervention will actually reduce emissions at scale. The systems that we’re trying to change (such as the energy system) are enormously complex. Demonstrating success in a small research setting does not guarantee success at scale (unfortunately!).</p>
<p>Good luck! It’s gonna be tough. But we can’t fail. To paraphrase Greta: If we fail, future generations will never forgive us. (Sorry to be so gloomy!)</p>
<p>But it’s also technically exciting work (and actually quite good fun!) and there’s a great community of people using computer science to mitigate climate change! So, dive in - we need your help!</p>If you’d like to use computer science to help mitigate climate change, then check out these resources: ClimateChange.AI - All about using AI to mitigate and adapt to climate change. Check out their excellent 2019 paper; sign up to their newsletter, and join their forum! Also check out Paul Strobel’s six-part illustrated blog series about the paper. And here’s their list of climate change & ML job resources. CLIMATEBASE - “Make climate your career - Discover jobs at thousands of exciting climate tech companies and nonprofits around the world.” Work on Climate - “an action-oriented Slack community for people serious about climate work. Find climate jobs. Build climate companies. Find your people.” Open Climate Fix - OCF is the non-profit that I co-founded. Entirely focused on using open science (especially machine learning) to mitigate climate change. Please sign up to our newsletter and consider adding your name to our list of volunteers; and join our discussion forum! Sliced - ‘We help you find opportunities that have a direct impact on reversing climate change.’ ClimateAction.Tech - ‘A global community of tech professionals using our skills, expertise and platforms to support solutions to the climate crisis.’ Bret Victor’s blog: What can a technologist do about climate change - 2015 Dynamically Typed newsletter - bi-weekly newsletter about AI-powered products and ML research that also features new projects and resources for using AI to tackle the climate crisis in every edition. If you know of other resources, then please add a comment below or, even better, submit a pull request to improve this list! The challenge of reducing emissions at scale One quick rant: All the climate cares about is the scale of emissions reductions. For the climate to ‘notice’, we need to reduce emissions by at least a million tonnes of CO2e per year, and ideally billions of tonnes per year. To achieve scale we need to jump over multiple hurdles: Have a great research idea. Talk to industry to check that your idea solves a real problem. (I don’t know about you, but I find it all to easy to fixate on solving ‘toy’ problems. It’s essential that we focus on solving ‘real’ problems.) Build a proof-of-concept that demonstrates emission reductions in a controlled environment. Build a product (or get your idea implemented into an existing product) that reduces emissions and solves problems that industry cares about, at a price they can afford, and in a way which fits easily into their existing systems. Persuade industry to adopt the product / idea. (Marketing, support, iterating on the product after receiving critical feedback, etc.) Become financially sustainable. Reduce emissions! (Even if you succeed at all the previous steps, that’s no guarantee that you’ll reduce emissions at scale. Maybe there’s a rebound effect? Maybe there are some other unintended consequences?) Projects tend to attract most funding and media attention after completing a proof-of-concept. But we need to get all the way to the final step before we can be confident that a given intervention will actually reduce emissions at scale. The systems that we’re trying to change (such as the energy system) are enormously complex. Demonstrating success in a small research setting does not guarantee success at scale (unfortunately!). Good luck! It’s gonna be tough. But we can’t fail. To paraphrase Greta: If we fail, future generations will never forgive us. (Sorry to be so gloomy!) But it’s also technically exciting work (and actually quite good fun!) and there’s a great community of people using computer science to mitigate climate change! So, dive in - we need your help!UK PV map2019-08-16T00:00:00+01:002019-08-16T00:00:00+01:00http://jack-kelly.com/blog/UK-PV-map<p>Here’s an animated map of UK PV power generation on the day of the UK power cuts on 2019-08-09.</p>
<p>Hit the ‘play’ button on the bottom of the video.</p>
<p>The timestamp is shown at the top of the video.</p>
<p>White dots mean no data.</p>
<p>The power was normalised relative to the maximum power output per PV system on 2019-08-09 (not normalised relative to the nominal capacity of each system).</p>
<p>Data was pulled from PVOutput.org using our <a href="https://github.com/openclimatefix/pvoutput">pvoutput Python library</a>.</p>
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Your browser does not support the video tag.
</video>Here’s an animated map of UK PV power generation on the day of the UK power cuts on 2019-08-09. Hit the ‘play’ button on the bottom of the video. The timestamp is shown at the top of the video. White dots mean no data. The power was normalised relative to the maximum power output per PV system on 2019-08-09 (not normalised relative to the nominal capacity of each system). Data was pulled from PVOutput.org using our pvoutput Python library. Your browser does not support the video tag.Starting work on solar PV mapping2019-07-09T00:00:00+01:002019-07-09T00:00:00+01:00http://jack-kelly.com/blog/solar-PV-mapping<p>Solar PV mapping is a sub-project of our <a href="http://jack-kelly.com/blog/2019-07-01-starting-solar-electricity-nowcasting">PV nowcasting project</a>. Nowcasting will provide solar PV yield predictions for any location. To be able to arrive at an accurate forecast for solar power generation (out-turn), we need to know where all the PV panels are!</p>
<p>The OpenStreetMap (OSM) community has already done an incredible job mapping over 10,000 PV installations in the UK (out of over 1 million we believe to exist) and many more across the world. Our goal is to help grow the effort and drastically increase the amount of PV mapped in OSM.</p>
<p>Recent research (<a href="http://web.stanford.edu/group/deepsolar/home">DeepSolar</a> and <a href="https://arxiv.org/pdf/1902.10895.pdf">SolarMapper from DUKE</a> are just two examples) has shown the power of machine learning (ML) for identifying PV panels in satellite and aerial imagery at scale. We want to make it easy for anyone to start using ML to map PV across the world, and help pull together the datasets that are generated into a global open database (which can be used for updating OSM).</p>
<p>By creating a comprehensive open database of PV installations, we think that many other people will find interesting ways to use the data.</p>
<p>The UK is our initial focus for the nowcasting project, so we have also focused on mapping PV installations there initially. <a href="https://twitter.com/LaurenceWWatson">Laurence Watson</a> has taken up the challenge of mapping UK PV using high-resolution 25cm aerial imagery. Although licensing does not allow the aerial imagery to be shared, we will be open sourcing the methods and tools we use. We are using the excellent <a href="https://rastervision.io/">Raster Vision</a> framework, which will make it easier for anyone to reuse and contribute to the work.</p>
<p>If you are interested in doing a similar ML mapping project in your country (where we can find high resolution imagery), please reach out, as we will be happy to collaborate whilst we do the UK project.</p>
<p>There are other groups doing similar PV mapping projects, and we are excited about the prospect of growing the community, and providing a place for these PV datasets to live.</p>
<h2 id="how-can-you-contribute">How can you contribute?</h2>
<ol>
<li>Map PV installations manually from street level or satellite imagery directly in OSM. There are more details on the OSM wiki: <a href="https://wiki.openstreetmap.org/wiki/Renewable_energy_in_the_United_Kingdom/Rooftop_Solar_PV">https://wiki.openstreetmap.org/wiki/Renewable_energy_in_the_United_Kingdom/Rooftop_Solar_PV</a></li>
<li>Help us clean and organise PV location data. Could involve GIS work or building software to automate things.</li>
<li>Contribute to existing ML PV mapping projects. There is lots of work to be done improving accuracy of the models. Projects include:
<ol>
<li>UK small scale PV by <a href="https://twitter.com/LaurenceWWatson">Laurence Watson</a>.</li>
<li>Tyler Busby is mapping PV in the USA using Mapbox satellite imagery: <a href="http://openclimatefix.discourse.group/t/solarpaneldatawrangler/26">SolarPanelDataWrangler</a>.</li>
</ol>
</li>
<li>Start a new project to map small (residential and business) PV installations in your (or any) country.</li>
</ol>
<p>If there is a specific area of PV mapping you’d like to get involved in, feel free to reach out to <a href="https://twitter.com/dctanner">@dctanner</a>. Or more generally, if you’d like to get involved, please register your interest on <a href="https://airtable.com/shrl59GJ96csVF4WB">OCF’s volunteer register</a> and/or join the conversations on <a href="http://openclimatefix.discourse.group/">OCF’s forum</a>.</p>
<p>(This blog post was written by <a href="https://twitter.com/dctanner">Damien</a>, who is leading OCF’s PV mapping project!)</p>Solar PV mapping is a sub-project of our PV nowcasting project. Nowcasting will provide solar PV yield predictions for any location. To be able to arrive at an accurate forecast for solar power generation (out-turn), we need to know where all the PV panels are! The OpenStreetMap (OSM) community has already done an incredible job mapping over 10,000 PV installations in the UK (out of over 1 million we believe to exist) and many more across the world. Our goal is to help grow the effort and drastically increase the amount of PV mapped in OSM. Recent research (DeepSolar and SolarMapper from DUKE are just two examples) has shown the power of machine learning (ML) for identifying PV panels in satellite and aerial imagery at scale. We want to make it easy for anyone to start using ML to map PV across the world, and help pull together the datasets that are generated into a global open database (which can be used for updating OSM). By creating a comprehensive open database of PV installations, we think that many other people will find interesting ways to use the data. The UK is our initial focus for the nowcasting project, so we have also focused on mapping PV installations there initially. Laurence Watson has taken up the challenge of mapping UK PV using high-resolution 25cm aerial imagery. Although licensing does not allow the aerial imagery to be shared, we will be open sourcing the methods and tools we use. We are using the excellent Raster Vision framework, which will make it easier for anyone to reuse and contribute to the work. If you are interested in doing a similar ML mapping project in your country (where we can find high resolution imagery), please reach out, as we will be happy to collaborate whilst we do the UK project. There are other groups doing similar PV mapping projects, and we are excited about the prospect of growing the community, and providing a place for these PV datasets to live. How can you contribute? Map PV installations manually from street level or satellite imagery directly in OSM. There are more details on the OSM wiki: https://wiki.openstreetmap.org/wiki/Renewable_energy_in_the_United_Kingdom/Rooftop_Solar_PV Help us clean and organise PV location data. Could involve GIS work or building software to automate things. Contribute to existing ML PV mapping projects. There is lots of work to be done improving accuracy of the models. Projects include: UK small scale PV by Laurence Watson. Tyler Busby is mapping PV in the USA using Mapbox satellite imagery: SolarPanelDataWrangler. Start a new project to map small (residential and business) PV installations in your (or any) country. If there is a specific area of PV mapping you’d like to get involved in, feel free to reach out to @dctanner. Or more generally, if you’d like to get involved, please register your interest on OCF’s volunteer register and/or join the conversations on OCF’s forum. (This blog post was written by Damien, who is leading OCF’s PV mapping project!)Starting work on solar electricity nowcasting2019-07-01T00:00:00+01:002019-07-01T00:00:00+01:00http://jack-kelly.com/blog/starting-solar-electricity-nowcasting<p>At <a href="http://openclimatefix.org">Open Climate Fix</a>, we’re about to start work on our solar electricity (PV) nowcasting project. The aim of this project is to help reduce emissions (and costs) by forecasting solar electricity power production at high temporal and spatial resolution, but with a forecast time horizon of a few hours. We’ll use machine learning to combine satellite images, numerical weather predictions, and live solar PV data. All our research code will be open source; and we’ll blog about our results (including negative results); and we’re working hard to make it easy for other researchers to access the data necessary to do this research :)</p>
<p>To be kept up to date, please <a href="https://eepurl.com/guCjvH">sign up to OCF’s newsletter</a> (and/or <a href="https://twitter.com/jack_kelly">follow Jack on Twitter</a>!). If you’d like to get involved, please register your interest on <a href="https://airtable.com/shrl59GJ96csVF4WB">OCF’s volunteer register</a> and/or join the conversations on <a href="http://openclimatefix.discourse.group/">OCF’s forum</a>.</p>
<p>OCF is also working on mapping the location of PV panels. We’ll blog about the PV mapping project soon :)</p>
<h2 id="why-is-solar-electricity-nowcasting-useful">Why is solar electricity nowcasting useful?</h2>
<p>There are several uses for better solar forecasts. One use case that we’re focusing on is to help electricity system operators (like the UK National Grid) to schedule spinning reserve.</p>
<p>What does that mean?!</p>
<p>The physics of the electricity grid dictate that, at every moment, supply must exactly match demand. Small changes in demand are handled automatically: When you turn on your kettle, a generator somewhere on the grid will automatically produce a tiny bit more power. But what if a large cloud covers hundreds of megawatts of solar PV panels? The lost PV generation must be replaced within a few seconds. But it takes many minutes, or even hours, to spin up a natural-gas power plant from cold. So, whenever any solar PV power is on the system, electricity system operators schedule lots of ‘upwards spinning reserve’ (typically thermal generators which are operated at less than 100% capacity, and so can be ramped up quickly to replace lost PV generation).</p>
<p>At present, system operators take a fairly crude and conservative approach to deciding how much spinning reserve to schedule.</p>
<p>With better forecasts for solar PV generation, electricity system operators will be able to schedule spinning reserve more efficiently, which will reduce emissions from the spinning reserve, and reduce costs, and enable the electricity system to handle a greater penetration of solar PV. In particular, we need solar PV nowcasts which do a good job of quantifying <em>uncertainty</em>. If we’re confident the day will stay sunny, we can schedule less spinning reserve.</p>
<p>One of our aims is to run a pilot project with at least one electricity system operator, to test if probabilistic PV nowcasts really do help schedule the grid.</p>
<h2 id="conventional-forecasting-techniques-arent-well-suited-for-solar-nowcasting">Conventional forecasting techniques aren’t well suited for solar nowcasting</h2>
<p>Conventional forecasting techniques (‘numerical weather predictions’) are fantastic at forecasting things like the temperature and wind speeds for tomorrow. But numerical weather predictions aren’t so good at forecasting the amount of sunlight falling on the Earth’s surface (‘irradiance’), nor are they great at predicting the next few hours (‘nowcasting’). This is for several reasons:</p>
<ul>
<li>Numerical weather models take at least an hour to run and so, by the time they’ve finished executing, they are already an hour out of date with respect to the most recent observations. This isn’t a problem for forecasting temperature. But clouds can change quite dramatically over an hour!</li>
<li>Solar irradiance is greatly affected by clouds; yet operational numerical weather models can’t resolve clouds. Instead, numerical weather models ‘parameterise’ clouds.</li>
<li>The ‘radiative transfer codes’ in numerical weather models (the bits which simulate light bouncing around the atmosphere) are some of the most computationally expensive bits of numerical weather models, and so might be run infrequently, or might be run in quite a limited fashion.</li>
<li>Historically, there simply hasn’t been much demand for highly accurate forecasts for irradiance. And so not much research has been done on the topic (although that is changing: We’re certainly not the only folks working on solar PV nowcasting!)</li>
</ul>
<h2 id="our-planned-approach">Our planned approach</h2>
<p>We’ll explore if machine learning (ML) can help nowcast solar PV power production. There’s quite a lot of data available to train ML models; and there are lots of ML engineers who are keen to help.</p>
<p>In particular, we’re planning to work on several distinct ML tasks. In brief, we plan to split the problem into two parts: nowcast the movement of clouds; and then infer solar PV power for a given configuration of the atmosphere. In a little more detail, the steps are:</p>
<p>1) Infer solar PV yield (normalised by clear-sky PV yield) from satellite images of clouds, and numerical weather predictions. That is, don’t forecast the <em>future</em>, ‘just’ try to learn the ‘radiative transfer function’ of the atmosphere. Learn how different types of clouds and aerosols affect the passage of sunlight through the atmosphere. If this works, then we’ll end up with a system which can tell us the solar PV yield for a single site, given a satellite image of cloud cover over that site, and maybe some data about aerosols, ozone, and water vapour. We can ‘sweep’ this model across large geographical areas to produce a historical ‘solar PV reanalysis’ dataset.</p>
<p>2) Learn how clouds move. We’ll start simple, and use conventional ‘nowcasting’ techniques (Take the most recent satellite image of cloud cover. For each pixel of cloud, move that pixel according to the wind speed and direction at that pixel). Then we’ll try modelling different vertical cloud decks separately (so if high cloud moves over lower cloud, our model should understand that the low cloud probably still exists, even though it’s obscured by the higher cloud). Finally, we’ll try ‘pure ML’ approaches (such as video prediction).</p>
<p>Once we’ve built a proof-of-concept models for step 1 and 2, we’ll combine these to produce a simple PV nowcasting system.</p>
<p>3) Clouds don’t just <em>move</em>. Clouds condense out of ‘nothing’; and evaporate / precipitate away into ‘nothing’. Clouds change shape; grow vertically; merge; split; change opacity, etc. Can we train a machine learning model to predict how clouds will ‘evolve’ over a few hours?</p>
<p>A more detailed plan is available <a href="https://docs.google.com/document/d/1IS0h-W_GyRRUDV8Ur1jiWXYSGZn_Soq-H0UUdvmpXi0/edit?usp=sharing">here</a>.</p>
<h2 id="ultra-open-science-and-how-to-get-involved">Ultra-open science; and how to get involved</h2>
<p>All our code will be open (on GitHub); we’ll blog about results (including negative results); we’ll openly discuss ideas and concerns; and we’ll maintain a public ‘leaderboard’ to show how well different models have performed (hopefully, over time, many of those models will be built by other people!)</p>
<p>It’ll take us another few months to get the project off the ground but, once we do, we’d love people to get involved in the conversations; and to build software tools to help process and visualise the data :)</p>
<p>But, by far the best way to scale this research across multiple people is probably to enable you folks to try your own modelling approaches (i.e. to take Open Climate Fix out of the loop! You guys just download the data, and maybe use some open-source tools the community develops, and try to beat the current state-of-the-art, and then send your score to the leaderboard)</p>
<p>If we write a paper, then everyone who contributes ideas or code will be listed as an author :)</p>
<p>If you’d like to get involved, please register your interest on <a href="https://airtable.com/shrl59GJ96csVF4WB">OCF’s volunteer register</a> and/or join the conversations on <a href="http://openclimatefix.discourse.group/">OCF’s forum</a>.</p>
<h3 id="data">Data</h3>
<p>When we started Open Climate Fix, we hoped to be able to make life super-easy for researchers by collating all relevant datasets into one location. Unfortunately, we’re strictly not allowed to ‘redistribute’ the satellite data, so we can’t collate the satellite data. We’re working on fixing this! But this isn’t a deal-breaker, as each individual researcher can download the historical satellite data for free from the satellite data provider (EUMETSAT). We’ll try to make tools and documentation to make this as painless as possible.</p>
<p>We’re also working hard to get some solar PV power data released into the open.</p>At Open Climate Fix, we’re about to start work on our solar electricity (PV) nowcasting project. The aim of this project is to help reduce emissions (and costs) by forecasting solar electricity power production at high temporal and spatial resolution, but with a forecast time horizon of a few hours. We’ll use machine learning to combine satellite images, numerical weather predictions, and live solar PV data. All our research code will be open source; and we’ll blog about our results (including negative results); and we’re working hard to make it easy for other researchers to access the data necessary to do this research :) To be kept up to date, please sign up to OCF’s newsletter (and/or follow Jack on Twitter!). If you’d like to get involved, please register your interest on OCF’s volunteer register and/or join the conversations on OCF’s forum. OCF is also working on mapping the location of PV panels. We’ll blog about the PV mapping project soon :) Why is solar electricity nowcasting useful? There are several uses for better solar forecasts. One use case that we’re focusing on is to help electricity system operators (like the UK National Grid) to schedule spinning reserve. What does that mean?! The physics of the electricity grid dictate that, at every moment, supply must exactly match demand. Small changes in demand are handled automatically: When you turn on your kettle, a generator somewhere on the grid will automatically produce a tiny bit more power. But what if a large cloud covers hundreds of megawatts of solar PV panels? The lost PV generation must be replaced within a few seconds. But it takes many minutes, or even hours, to spin up a natural-gas power plant from cold. So, whenever any solar PV power is on the system, electricity system operators schedule lots of ‘upwards spinning reserve’ (typically thermal generators which are operated at less than 100% capacity, and so can be ramped up quickly to replace lost PV generation). At present, system operators take a fairly crude and conservative approach to deciding how much spinning reserve to schedule. With better forecasts for solar PV generation, electricity system operators will be able to schedule spinning reserve more efficiently, which will reduce emissions from the spinning reserve, and reduce costs, and enable the electricity system to handle a greater penetration of solar PV. In particular, we need solar PV nowcasts which do a good job of quantifying uncertainty. If we’re confident the day will stay sunny, we can schedule less spinning reserve. One of our aims is to run a pilot project with at least one electricity system operator, to test if probabilistic PV nowcasts really do help schedule the grid. Conventional forecasting techniques aren’t well suited for solar nowcasting Conventional forecasting techniques (‘numerical weather predictions’) are fantastic at forecasting things like the temperature and wind speeds for tomorrow. But numerical weather predictions aren’t so good at forecasting the amount of sunlight falling on the Earth’s surface (‘irradiance’), nor are they great at predicting the next few hours (‘nowcasting’). This is for several reasons: Numerical weather models take at least an hour to run and so, by the time they’ve finished executing, they are already an hour out of date with respect to the most recent observations. This isn’t a problem for forecasting temperature. But clouds can change quite dramatically over an hour! Solar irradiance is greatly affected by clouds; yet operational numerical weather models can’t resolve clouds. Instead, numerical weather models ‘parameterise’ clouds. The ‘radiative transfer codes’ in numerical weather models (the bits which simulate light bouncing around the atmosphere) are some of the most computationally expensive bits of numerical weather models, and so might be run infrequently, or might be run in quite a limited fashion. Historically, there simply hasn’t been much demand for highly accurate forecasts for irradiance. And so not much research has been done on the topic (although that is changing: We’re certainly not the only folks working on solar PV nowcasting!) Our planned approach We’ll explore if machine learning (ML) can help nowcast solar PV power production. There’s quite a lot of data available to train ML models; and there are lots of ML engineers who are keen to help. In particular, we’re planning to work on several distinct ML tasks. In brief, we plan to split the problem into two parts: nowcast the movement of clouds; and then infer solar PV power for a given configuration of the atmosphere. In a little more detail, the steps are: 1) Infer solar PV yield (normalised by clear-sky PV yield) from satellite images of clouds, and numerical weather predictions. That is, don’t forecast the future, ‘just’ try to learn the ‘radiative transfer function’ of the atmosphere. Learn how different types of clouds and aerosols affect the passage of sunlight through the atmosphere. If this works, then we’ll end up with a system which can tell us the solar PV yield for a single site, given a satellite image of cloud cover over that site, and maybe some data about aerosols, ozone, and water vapour. We can ‘sweep’ this model across large geographical areas to produce a historical ‘solar PV reanalysis’ dataset. 2) Learn how clouds move. We’ll start simple, and use conventional ‘nowcasting’ techniques (Take the most recent satellite image of cloud cover. For each pixel of cloud, move that pixel according to the wind speed and direction at that pixel). Then we’ll try modelling different vertical cloud decks separately (so if high cloud moves over lower cloud, our model should understand that the low cloud probably still exists, even though it’s obscured by the higher cloud). Finally, we’ll try ‘pure ML’ approaches (such as video prediction). Once we’ve built a proof-of-concept models for step 1 and 2, we’ll combine these to produce a simple PV nowcasting system. 3) Clouds don’t just move. Clouds condense out of ‘nothing’; and evaporate / precipitate away into ‘nothing’. Clouds change shape; grow vertically; merge; split; change opacity, etc. Can we train a machine learning model to predict how clouds will ‘evolve’ over a few hours? A more detailed plan is available here. Ultra-open science; and how to get involved All our code will be open (on GitHub); we’ll blog about results (including negative results); we’ll openly discuss ideas and concerns; and we’ll maintain a public ‘leaderboard’ to show how well different models have performed (hopefully, over time, many of those models will be built by other people!) It’ll take us another few months to get the project off the ground but, once we do, we’d love people to get involved in the conversations; and to build software tools to help process and visualise the data :) But, by far the best way to scale this research across multiple people is probably to enable you folks to try your own modelling approaches (i.e. to take Open Climate Fix out of the loop! You guys just download the data, and maybe use some open-source tools the community develops, and try to beat the current state-of-the-art, and then send your score to the leaderboard) If we write a paper, then everyone who contributes ideas or code will be listed as an author :) If you’d like to get involved, please register your interest on OCF’s volunteer register and/or join the conversations on OCF’s forum. Data When we started Open Climate Fix, we hoped to be able to make life super-easy for researchers by collating all relevant datasets into one location. Unfortunately, we’re strictly not allowed to ‘redistribute’ the satellite data, so we can’t collate the satellite data. We’re working on fixing this! But this isn’t a deal-breaker, as each individual researcher can download the historical satellite data for free from the satellite data provider (EUMETSAT). We’ll try to make tools and documentation to make this as painless as possible. We’re also working hard to get some solar PV power data released into the open.How to get involved with Open Climate Fix2019-03-06T00:00:00+00:002019-03-06T00:00:00+00:00http://jack-kelly.com/blog/how-to-get-involved<p>I’m building a non-profit called <a href="http://openclimatefix.org">Open Climate Fix</a>, entirely focused on using open-science to help fix climate change ASAP. We’d love help :) This blog post lists a few concrete things we need help with.</p>
<p>First, a quick disclaimer: OCF is still very, <em>very</em> young. <del>We don’t yet have funding, a bank account, or an online forum. (These things will come!)</del> I’m spending the majority of my time meeting people (I’ve had over 100 meetings over the last 7 weeks, and plenty more planned - I’m trying hard to focus on meetings relevant to solar PV now). So, please be patient with me! Once I actually start writing code and releasing data, then there will be much more for folks to get stuck into.</p>
<p>In the mean time, here are a few ideas for how to help!</p>
<ul>
<li>Please take a look at our list of <strong><a href="https://docs.google.com/document/d/1tk9cF4O539TzaMaUufn9Ay4f6qKKEyoNKmzP03kbSDo/edit?usp=sharing">Solar data & software</a></strong> and add any resources that you can find! e.g. we’d love help locating more sources of satellite data of cloud cover.</li>
<li>Take a look at the <strong><a href="https://docs.google.com/document/d/14UZd_qdAjD8P1VGNQThf3rZz9tBCXk2_ySZ_oNIEdSs/edit?usp=sharing">Solar Projects</a></strong> doc. Please comment on any projects that you’d like to help with. Heck, if you want, please go right ahead and take the lead on any of those projects :) For example, one great stand-alone project would be to build a simple website to allow people to enter the location of PV panels into Open Street Map. (Please comment on the doc if you do go ahead and start work on a project, so we don’t duplicate work.)
<ul>
<li>The Solar Projects doc includes some ‘research’ sections. These sections contain lists of ideas for self-contained <strong>research projects</strong>, many of which don’t require any coding (just spending some time googling stuff and/or contacting people). We’d people to take on these research projects!</li>
</ul>
</li>
<li>Please help with <strong><a href="http://openclimatefix.org">openclimatefix.org</a> website</strong>! (<a href="https://github.com/openclimatefix/openclimatefix.github.io/">Code is on GitHub</a>). Feel free to submit pull requests for small changes. For big changes, please describe your proposal in <a href="https://github.com/openclimatefix/openclimatefix.github.io/issues">the issue queue</a> before you start coding, just to make sure no one else is already working on something similar! I’m working on some more content, but I’d love help making that content look good :)
<ul>
<li>We also need a <strong>logo</strong> :)</li>
</ul>
</li>
<li>Join <a href="http://openclimatefix.discourse.group">the OCF web discussion forum</a>.</li>
<li><strong>Data!</strong> Do you work at an organisation with solar PV power timeseries data or PV location data or satellite imagery? (e.g. an inverter company, or a solar analytics company)? Or do you know someone who does?! If so, <em>please</em> get in touch. I’m very hungry for good PV data!
<ul>
<li>If you have PV power data, please either email it to me (jack@OpenClimateFix.org), or upload the power data to <a href="https://pvoutput.org/">PVOutput.org</a>.</li>
</ul>
</li>
<li><strong>Map the geographical location of PV systems in <a href="https://www.openstreetmap.org">Open Street Map</a></strong>, using <a href="https://wiki.openstreetmap.org/wiki/Tag:generator:source=solar">tag:generator:source=solar</a>.</li>
<li><strong>Domain expertise!</strong> If you know about cloud forecasting / aerosol forecasting / determining the toplogy of the electricity network / related things then please email me (jack@openclimatefix.org)!</li>
<li>Erm… it’s always awkward talking about <strong>money</strong> but, um, if you are into climate philanthropy - or if you know of anyone who’s into climate philanthropy - then please email me (jack@openclimatefix.org)! My intention is that Open Climate Fix will reduce emissions by millions of tonnes (at least), at a cost of a few dollars per tonne. (i.e. considerably cheaper than most other interventions).</li>
</ul>I’m building a non-profit called Open Climate Fix, entirely focused on using open-science to help fix climate change ASAP. We’d love help :) This blog post lists a few concrete things we need help with. First, a quick disclaimer: OCF is still very, very young. We don’t yet have funding, a bank account, or an online forum. (These things will come!) I’m spending the majority of my time meeting people (I’ve had over 100 meetings over the last 7 weeks, and plenty more planned - I’m trying hard to focus on meetings relevant to solar PV now). So, please be patient with me! Once I actually start writing code and releasing data, then there will be much more for folks to get stuck into. In the mean time, here are a few ideas for how to help! Please take a look at our list of Solar data & software and add any resources that you can find! e.g. we’d love help locating more sources of satellite data of cloud cover. Take a look at the Solar Projects doc. Please comment on any projects that you’d like to help with. Heck, if you want, please go right ahead and take the lead on any of those projects :) For example, one great stand-alone project would be to build a simple website to allow people to enter the location of PV panels into Open Street Map. (Please comment on the doc if you do go ahead and start work on a project, so we don’t duplicate work.) The Solar Projects doc includes some ‘research’ sections. These sections contain lists of ideas for self-contained research projects, many of which don’t require any coding (just spending some time googling stuff and/or contacting people). We’d people to take on these research projects! Please help with openclimatefix.org website! (Code is on GitHub). Feel free to submit pull requests for small changes. For big changes, please describe your proposal in the issue queue before you start coding, just to make sure no one else is already working on something similar! I’m working on some more content, but I’d love help making that content look good :) We also need a logo :) Join the OCF web discussion forum. Data! Do you work at an organisation with solar PV power timeseries data or PV location data or satellite imagery? (e.g. an inverter company, or a solar analytics company)? Or do you know someone who does?! If so, please get in touch. I’m very hungry for good PV data! If you have PV power data, please either email it to me (jack@OpenClimateFix.org), or upload the power data to PVOutput.org. Map the geographical location of PV systems in Open Street Map, using tag:generator:source=solar. Domain expertise! If you know about cloud forecasting / aerosol forecasting / determining the toplogy of the electricity network / related things then please email me (jack@openclimatefix.org)! Erm… it’s always awkward talking about money but, um, if you are into climate philanthropy - or if you know of anyone who’s into climate philanthropy - then please email me (jack@openclimatefix.org)! My intention is that Open Climate Fix will reduce emissions by millions of tonnes (at least), at a cost of a few dollars per tonne. (i.e. considerably cheaper than most other interventions).Magazine or community platform for people interested in practical action to limit climate change.2019-01-15T00:00:00+00:002019-01-15T00:00:00+00:00http://jack-kelly.com/blog/community-platform-for-climate-change-mitigation<p>Over the past week, it has <a href="https://twitter.com/jack_kelly/status/1082283333202128897">become clear</a> that there are lots of people out there who are highly skilled, and passionate about trying to fix climate change. This is awesome.</p>
<p>It’s also clear that we need somewhere for this community to gather, exchange ideas, connect, work on each other’s projects, and generally support each other. The message could be: “yes, fixing climate change is going to be very hard. But look at all these amazing people who are already doing impactful work. You can join in, and here’s how!”</p>
<p>Maybe this would be a blog and/or a YouTube channel and/or a podcast. Some content written by journalists. Some content written by the people who are already running projects. And/or a mailing list. Perhaps with a database of projects.</p>
<p>The focus would be on practical, scalable projects, happening right now. Lots of juicy detail where appropriate.</p>
<p>The target audience would be skilled people who can actually get stuck into projects - either just a few hours a month, or by leaving their current job and diving in head-first.</p>
<p>This wouldn’t be an academic journal - the focus would be on practical interventions, and the content would be attractive and fun to read.</p>
<p>There would also be a strong focus on the climate impact - if an intervention is, at best, only going to save a hundred tonnes of CO<sub>2</sub> then that needs to be called out.</p>
<p>Would this be useful?</p>
<p>Does anything similar already exist (if so, please link to existing projects in teh comments below!)</p>Over the past week, it has become clear that there are lots of people out there who are highly skilled, and passionate about trying to fix climate change. This is awesome. It’s also clear that we need somewhere for this community to gather, exchange ideas, connect, work on each other’s projects, and generally support each other. The message could be: “yes, fixing climate change is going to be very hard. But look at all these amazing people who are already doing impactful work. You can join in, and here’s how!” Maybe this would be a blog and/or a YouTube channel and/or a podcast. Some content written by journalists. Some content written by the people who are already running projects. And/or a mailing list. Perhaps with a database of projects. The focus would be on practical, scalable projects, happening right now. Lots of juicy detail where appropriate. The target audience would be skilled people who can actually get stuck into projects - either just a few hours a month, or by leaving their current job and diving in head-first. This wouldn’t be an academic journal - the focus would be on practical interventions, and the content would be attractive and fun to read. There would also be a strong focus on the climate impact - if an intervention is, at best, only going to save a hundred tonnes of CO2 then that needs to be called out. Would this be useful? Does anything similar already exist (if so, please link to existing projects in teh comments below!)Solar PV nowcasting2019-01-09T00:00:00+00:002019-01-09T00:00:00+00:00http://jack-kelly.com/blog/solar-PV-nowcasting<p>Solar PV is the single biggest source of uncertainty in the National Grid’s forecasts. To mitigate against the risk of a large cloud sweeping across the country (and hence the grid losing gigawatts of PV generation), the National Grid keep lots of natural gas generators idling (‘spinning reserve’). These gas turbines are kept idling because they take several hours to start up from cold, but they can be ramped up very quickly from idle; and the physics of the grid dictate that - at every instant - supply must precisely match demand. So, any loss in PV supply must be immediately replaced. Spinning reserve costs a lot of money and pumps out a lot of CO2. If the National Grid had better PV forecasts, even for the next few hours, they could reduce the amount of spinning reserve required, and hence reduce emissions (by about 100,000 tonnes per year) and reduce costs.</p>
<p>“Nowcasting” is a bit of an odd term. It means “forecasting for the next few hours”. In general, “nowcasting” doesn’t use the hugely complex and expensive numerical weather models that run on super computers and constitute the bread-and-butter for most forecasting agencies. Instead, “nowcasting” usually uses statistical techniques to take recent observations and ‘roll them forwards’.</p>
<p>It turns out that most nowcasting research up until now has been done on rainfall (because predicting floods saves lives). Relatively little research has been done on nowcasting sunlight.</p>
<p>I’m a machine learning researcher. So, my main interest is in trying to build machine learning models to nowcast solar PV (which basically boils down to trying to predict the movement and evolution of clouds). I’d like to spend the majority of the next year or two writing code to experiment with new ways to predict sunlight for the next few hours. Inputs to the model may include satellite images of clouds, numerical weather predictions, vertical cloud profiles, and geographical information. As quickly as possible, I’d like to get prototype PV nowcasts displayed in the National Grid control room, so we can start measuring the impact on emissions and cost.</p>
<p>Once I and my co-conspiriators got our heads round the datasets, we’d love to create an open dataset, so that <em>anyone</em> can try their hand at beating the state-of-the-art in PV nowcasting :)</p>
<p><strong>Update</strong> Please see <a href="/blog/2019-07-01-starting-solar-electricity-nowcasting">July 2019’s blog post</a> for more details about PV nowcasting.</p>Solar PV is the single biggest source of uncertainty in the National Grid’s forecasts. To mitigate against the risk of a large cloud sweeping across the country (and hence the grid losing gigawatts of PV generation), the National Grid keep lots of natural gas generators idling (‘spinning reserve’). These gas turbines are kept idling because they take several hours to start up from cold, but they can be ramped up very quickly from idle; and the physics of the grid dictate that - at every instant - supply must precisely match demand. So, any loss in PV supply must be immediately replaced. Spinning reserve costs a lot of money and pumps out a lot of CO2. If the National Grid had better PV forecasts, even for the next few hours, they could reduce the amount of spinning reserve required, and hence reduce emissions (by about 100,000 tonnes per year) and reduce costs. “Nowcasting” is a bit of an odd term. It means “forecasting for the next few hours”. In general, “nowcasting” doesn’t use the hugely complex and expensive numerical weather models that run on super computers and constitute the bread-and-butter for most forecasting agencies. Instead, “nowcasting” usually uses statistical techniques to take recent observations and ‘roll them forwards’. It turns out that most nowcasting research up until now has been done on rainfall (because predicting floods saves lives). Relatively little research has been done on nowcasting sunlight. I’m a machine learning researcher. So, my main interest is in trying to build machine learning models to nowcast solar PV (which basically boils down to trying to predict the movement and evolution of clouds). I’d like to spend the majority of the next year or two writing code to experiment with new ways to predict sunlight for the next few hours. Inputs to the model may include satellite images of clouds, numerical weather predictions, vertical cloud profiles, and geographical information. As quickly as possible, I’d like to get prototype PV nowcasts displayed in the National Grid control room, so we can start measuring the impact on emissions and cost. Once I and my co-conspiriators got our heads round the datasets, we’d love to create an open dataset, so that anyone can try their hand at beating the state-of-the-art in PV nowcasting :) Update Please see July 2019’s blog post for more details about PV nowcasting.Update & FAQ on non-profit research lab2019-01-09T00:00:00+00:002019-01-09T00:00:00+00:00http://jack-kelly.com/blog/update<p>Two days ago I <a href="https://twitter.com/jack_kelly/status/1082283333202128897">tweeted</a> a link to <a href="/blog/2019-01-07-non-profit">a blog post I wrote</a> stating that I’d left DeepMind to start a non-profit research lab, focused on reducing greenhouse gas emissions. I’ve been absolutely blown away by the response! I was expecting maybe a couple of re-tweets. At the time of writing, the tweet has been retweeted 470 times, liked 2.1 thousand times, and I’ve had over 200 messages! To be entirely honest, I’m slightly overwhelmed! (in a good way!)</p>
<p>I’d like to answer some of the great questions that people have asked over the last few days.</p>
<h2 id="what-stage-are-you-at">What stage are you at?</h2>
<p>I’m at the <em>very</em> beginning of setting up this non-profit! I haven’t settled on a name or precise company structure, I don’t have a website or office, and don’t yet have any funding secured!</p>
<p>I <em>do</em> have some friends with whom I’ve been discussion this non-profit for the last few months; some of these friends might get involved in the non-profit, although nothing is set-in-stone yet.</p>
<p>I’ve also been talking to the UK Met Office and National Grid for a little while, and - although nothing is certain yet - it looks very likely that my first project will be forecasting PV in collaboration with the UK Met Office and National Grid. I’m convinced that solar PV forecasting is likely to have significant climate impact, and it’s a good fit for my skills and interests.</p>
<h2 id="do-you-have-a-mailing-list">Do you have a mailing list?</h2>
<p>Not yet! But I will set one up ASAP. In the mean time, please keep an eye on <a href="https://twitter.com/jack_kelly">my twitter feed</a> :)</p>
<h2 id="where-are-you-based">Where are you based?</h2>
<p>Peckham, London, UK :)</p>
<h2 id="tell-me-more-about-this-solar-pv-nowcasting-project">Tell me more about this solar PV nowcasting project.</h2>
<p>OK! Please see <a href="/blog/2019-01-09-solar-pv-nowcasting">the Solar PV nowcasting blog post</a>.</p>
<h2 id="funding">Funding?</h2>
<p>I don’t have funding yet but I plan to apply to the Network Innovation Allowance soon. This will provide enough money for my time and some compute resources for a year or two; but the NIA unlikely to provide enough money to employ anyone else for the first year or so, I’m afraid - sorry!</p>
<h2 id="id-love-to-help">I’d love to help!</h2>
<p>The amazing response to my tweet made it pretty clear that there are loads (thousands?) of highly skilled folks out there who are really keen to help fix climate change. This is fantastic news!</p>
<p>For the solar PV nowcasting project, I’d love to talk to anyone who has an interest in PV, machine learning (especially video prediction), battery scheduling, grid optimisation, and weather forecasting.</p>
<p>I’ll probably spend most of January ‘just’ talking to people :) (That’s what you’re supposed to do at the start of a startup, right?!?)</p>
<p>At least until the end of January, I doubt I’ll get any significant code written. But, once I do start writing code, I’d love people to critique my preliminary results, and help bounce ideas around. I’ll blog regularly with results.</p>
<h3 id="pv-nowcasting-dataset-enable-anyone-to-try-to-beat-the-state-of-the-art-in-pv-nowcasting">PV nowcasting dataset: Enable anyone to try to beat the state-of-the-art in PV nowcasting</h3>
<p>In the medium term, we plan to put together a dataset and tool chain so <em>anyone</em> can try their hand at PV nowcasting. This is really important to me: I want to enable everyone from teenage ML hot-shots to multi-national corporations to try to improve on the state-of-the-art for PV nowcasting. If they succeed in beating the state-of-the-art then I’d like to help get that algorithm into production so it can start making the world better ASAP :)</p>
<h3 id="how-to-connect-skilled-people-with-projects-which-need-help">How to connect skilled people with projects which need help?</h3>
<p>Unfortunately, I probably won’t be able to provide meaningful projects for the majority of people who very kindly reached out to me. Which raises the question: How best to allow this awesome group of people to get stuck into helping build stuff which helps to fix climate change?</p>
<p>Would a database of climate mitigation projects be useful? If you’re looking to work on a project (either paid or unpaid), then you’d look through this database to see if anything takes your fancy. If you’re involved in a project, then you could ask for help. The emphasis would be on open-source projects which help to reduce emissions; but perhaps we’d also allow closed-source projects to advertise? Does that sound useful? If so, would a newsletter be best? Or a blog, which perhaps also publishes interviews etc. with people involved in climate change mitigation? Or a listing on github? Or a forum? Or all of the above? Or does this already exist?</p>
<h2 id="scaling-up-the-research-lab">Scaling up the research lab</h2>
<p>For the first year or two, I’d like to focus mostly on solar PV nowcasting; and I’d like to deliberately keep the lab quite small, so that we can be experimental and agile without jeopardising lots of peoples’ jobs!</p>
<p>If the solar PV nowcasting project goes well, and if the open-source approach has proved useful, then I’d like to scale up to a few other projects. This is when the lab would start raising significant amounts of money and employing people to work on a range of projects (all open source; all laser-focused on fixing climate change at scale).</p>
<h2 id="press">Press</h2>
<p>I’d like to hold off on any press coverage at least for the next few weeks, please! I’d at least like to have settled on a name and set up a mailing list before any articles are published!</p>
<p>To be honest, I’d feel much more comfortable waiting until this lab has actually had real-world impact before doing any significant publicity. All I’ve done so far is write some blog posts and had a few meetings - stuff that the climate totally doesn’t care about!</p>Two days ago I tweeted a link to a blog post I wrote stating that I’d left DeepMind to start a non-profit research lab, focused on reducing greenhouse gas emissions. I’ve been absolutely blown away by the response! I was expecting maybe a couple of re-tweets. At the time of writing, the tweet has been retweeted 470 times, liked 2.1 thousand times, and I’ve had over 200 messages! To be entirely honest, I’m slightly overwhelmed! (in a good way!) I’d like to answer some of the great questions that people have asked over the last few days. What stage are you at? I’m at the very beginning of setting up this non-profit! I haven’t settled on a name or precise company structure, I don’t have a website or office, and don’t yet have any funding secured! I do have some friends with whom I’ve been discussion this non-profit for the last few months; some of these friends might get involved in the non-profit, although nothing is set-in-stone yet. I’ve also been talking to the UK Met Office and National Grid for a little while, and - although nothing is certain yet - it looks very likely that my first project will be forecasting PV in collaboration with the UK Met Office and National Grid. I’m convinced that solar PV forecasting is likely to have significant climate impact, and it’s a good fit for my skills and interests. Do you have a mailing list? Not yet! But I will set one up ASAP. In the mean time, please keep an eye on my twitter feed :) Where are you based? Peckham, London, UK :) Tell me more about this solar PV nowcasting project. OK! Please see the Solar PV nowcasting blog post. Funding? I don’t have funding yet but I plan to apply to the Network Innovation Allowance soon. This will provide enough money for my time and some compute resources for a year or two; but the NIA unlikely to provide enough money to employ anyone else for the first year or so, I’m afraid - sorry! I’d love to help! The amazing response to my tweet made it pretty clear that there are loads (thousands?) of highly skilled folks out there who are really keen to help fix climate change. This is fantastic news! For the solar PV nowcasting project, I’d love to talk to anyone who has an interest in PV, machine learning (especially video prediction), battery scheduling, grid optimisation, and weather forecasting. I’ll probably spend most of January ‘just’ talking to people :) (That’s what you’re supposed to do at the start of a startup, right?!?) At least until the end of January, I doubt I’ll get any significant code written. But, once I do start writing code, I’d love people to critique my preliminary results, and help bounce ideas around. I’ll blog regularly with results. PV nowcasting dataset: Enable anyone to try to beat the state-of-the-art in PV nowcasting In the medium term, we plan to put together a dataset and tool chain so anyone can try their hand at PV nowcasting. This is really important to me: I want to enable everyone from teenage ML hot-shots to multi-national corporations to try to improve on the state-of-the-art for PV nowcasting. If they succeed in beating the state-of-the-art then I’d like to help get that algorithm into production so it can start making the world better ASAP :) How to connect skilled people with projects which need help? Unfortunately, I probably won’t be able to provide meaningful projects for the majority of people who very kindly reached out to me. Which raises the question: How best to allow this awesome group of people to get stuck into helping build stuff which helps to fix climate change? Would a database of climate mitigation projects be useful? If you’re looking to work on a project (either paid or unpaid), then you’d look through this database to see if anything takes your fancy. If you’re involved in a project, then you could ask for help. The emphasis would be on open-source projects which help to reduce emissions; but perhaps we’d also allow closed-source projects to advertise? Does that sound useful? If so, would a newsletter be best? Or a blog, which perhaps also publishes interviews etc. with people involved in climate change mitigation? Or a listing on github? Or a forum? Or all of the above? Or does this already exist? Scaling up the research lab For the first year or two, I’d like to focus mostly on solar PV nowcasting; and I’d like to deliberately keep the lab quite small, so that we can be experimental and agile without jeopardising lots of peoples’ jobs! If the solar PV nowcasting project goes well, and if the open-source approach has proved useful, then I’d like to scale up to a few other projects. This is when the lab would start raising significant amounts of money and employing people to work on a range of projects (all open source; all laser-focused on fixing climate change at scale). Press I’d like to hold off on any press coverage at least for the next few weeks, please! I’d at least like to have settled on a name and set up a mailing list before any articles are published! To be honest, I’d feel much more comfortable waiting until this lab has actually had real-world impact before doing any significant publicity. All I’ve done so far is write some blog posts and had a few meetings - stuff that the climate totally doesn’t care about!