Probabilistic Forecasting of Wind and Solar Farm Output

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John Boland discusses statistical forecasting tools to generate forecasts with prediction intervals, trailing them on one wind and one solar farm.

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Institution: University of South Australia

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John Boland discusses statistical forecasting tools to generate forecasts with prediction intervals, trailing them on one wind and one solar farm.

Image courtesy of interviewee. September 20, 2024

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This video video discusses the importance of probabilistic forecasting in energy generation from renewable sources like solar and wind farms, particularly in the context of the Australian energy market. It emphasizes that understanding the error bounds around energy forecasts is crucial for decision-making by both producers and grid operators. Narrow error bands signify confidence in predictions, allowing for more reliable integration into the grid, while wider bands may lead to the risk of penalties for misforecasting.

The speaker shares a scenario in which a solar or wind farm must balance the output to the grid with the possibility of underestimating or overestimating generation, which could incur costs. Therefore, accurate and sharp probabilistic forecasts can enhance the financial decisions made by generators.

This video video also highlights recent experiments using simple statistical models, which have proven effective for short-term forecasting, compared to more complex machine learning methods. These forecasts help evaluate the value and reliability of the predicted energy output, supporting operational flexibility and profitability.

Additionally, it mentions a research project funded by the Australian Renewable Energy Agency, focusing on improving five-minute forecasting for solar and wind outputs, and outlines a method for transforming forecasting errors into a more useful form, demonstrating a novel approach that outperforms traditional quantile regression.

Finally, the text concludes with thoughts on future developments in market mechanisms for renewables, suggesting that established prediction methodologies can facilitate financial decisions and operational efficiencies in an increasingly renewable-driven electricity market.

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Here are five key points from the video:

1. **Importance of Probabilistic Forecasting**: The video emphasizes the significance of knowing the bounds around energy generation forecasts (e.g., from solar and wind farms) in the convideo of the Australian energy market. Narrower bounds around forecasts lead to more confident decisions by both energy generators and grid operators, while wider bands may prompt generators to store energy in batteries to avoid penalties for misforecasting.

2. **Consequences of Misforecasting**: Incorrect forecasts can lead to financial penalties due to the "cause or pay" system in the Australian energy market. Under-forecasting can force grid operators to source additional power, while over-forecasting may disrupt other generators' supply, underlining the necessity for accurate and reliable forecasting methods.

3. **Efficiency of Simple Forecasting Tools**: Recent experiments showed that simplified statistical models can effectively match or even outperform complex machine learning tools for short-term forecasting (e.g., five-minute forecasts), particularly in environments where quick decision-making is crucial, such as in electric energy markets.

4. **Value of Forecasting Beyond Accuracy**: The exploration of forecasting focuses not just on the skill or accuracy of the forecasts but also on their practical utility. Work is being done to assess the value of forecasts in operational and financial terms, especially on Reunion Island as well as in the convideo of Australia's national electricity market, highlighting the importance of critically evaluating how useful forecasts are in real-world applications.

5. **Innovative Forecasting Techniques**: The video discusses an innovative approach using transformations of errors with ARCH and GARCH methods to forecast the variance of forecasts. This methodology suggests the possibility of achieving sharper prediction intervals, which is essential for effective energy market operations. The concept of quantile regression is also mentioned, detailing how it can be used to forecast different percentiles to provide a comprehensive view of potential forecasting errors.

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Here are key quotes extracted from the video you :

1. **Forecasting Importance**:
- "It's my belief that instead of just forecasting the expected... it's better if you know the bounds around that forecast."

2. **Confidence in Forecasting**:
- "If the bands are narrow... then you can be sort of more confident that you can send that off to the grid."

3. **Implications of Misforecasting**:
- "If your forecast is a hundred, but you produce 80 or 120, it may mean that the grid operator will actually penalize you for misforecasting."

4. **Energy Market Mechanics**:
- "There's a thing called cause or pays in the... energy market in Australia."

5. **Value of Probabilistic Forecasts**:
- "If you have probabilistic forecasts... you might have to be flexible in the operation of the grid."

6. **Simplicity in Forecasting Tools**:
- "...sometimes people... believe that they might have to use very powerful tools... they don't necessarily check to see if simple tools will work as well."

7. **Experimentation with Forecasting**:
- "...we've just run a recent experiment... using very simple statistical models for a short time span."

8. **Forecasting Skill vs. Usefulness**:
- "It's not just good enough to think about the skill of the forecasting method, but... how useful it is, is it actually of use."

9. **Impact of Innovation**:
- "The nice thing is that... recent papers talk about... how useful it is in the sense that we've just explained for the generator and the operator of the system as well."

10. **Future of Renewable Forecasting**:
- "By 2027, a hundred percent of electricity will come from renewables... over the year, the total generated will be a hundred percent of the supply."

11. **Probabilistic Forecasting Benefits**:
- "Putting air bounds on the forecast will help them make their decisions on the financial side of things better."

12. **Combining Approaches**:
- "One place... find that they were very useful was... blending those forecasts... trying to figure out what... different types of models might work better."

These quotes encapsulate the major themes regarding forecasting in the convideo of renewable energy generation, emphasizing the importance of accurate predictions, the balance between simple and complex tools, and the operational implications of forecasting for energy producers and grid operators.

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It's my belief that instead of just

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forecasting the expected, generation, shall we say,

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from a solar, wind farm in the next five minutes,

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which is the timeframe

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that the energy market works on in Australia,

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or even, you know, a longer period, it's better if you know

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the bounds around that forecast.

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I think are they, are they narrow?

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Is your forecast really sharp or are they wide?

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Is your forecast, you know, a bit loose, shall we say?

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Uh, because that can help the decision making both on,

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the generator's side and on the operator of the system

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or the grid, for instance.

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I think with the, on the generator side, let's,

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have a little scenario.

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For instance, say you've got a, a solar farm

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and you have a battery, attached to it as well,

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which is becoming more and more common,

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or a wind farm as well.

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Okay? So you predict in the next five minutes,

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you're going to output a hundred megawatts

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from the, from the farm.

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Okay? Now, if the

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bands are, the bands around,

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or the, the possible error around that is small, okay,

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then you can be sort of more confident

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that you can send that off to the grid.

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Because if you mis forecast, in other words,

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if your forecast is a hundred, but you produce 80

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or 120, it may mean

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that the grid operator will actually penalize you

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for mis forecasting.

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There's a thing called cause

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or pays in the, energy market in Australia,

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for instance, if you under forecast, they have

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to bring more power on the grid from somewhere else.

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You basically have to pay for that fact.

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And if you, forecast too low, right?

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They may have to cut somebody else off, okay?

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That sort of thing. And so if your bands are wide

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around your expected output, you may decide,

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I'd rather put that, electricity into my battery now

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and then send it to the grid when I know exactly

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what I can get out from the battery rather than put it into

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the grid and risk a penalty.

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Okay? But if they're narrow, the bands around the forecast,

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you're confident it's okay on the operator side of the,

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of the situation.

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Basically what happens is

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that if you have probabilistic forecasts

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and you can sort of look at the, the near future, you say,

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okay, well, it looks like the, the air bounds

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around the forecast are small.

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I don't have to think about too much in reserve to, to,

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you know, fix up the problem if there is one.

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Whereas if they're wide, you might have

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to be flexible in the operation of

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The grid. So

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it helps the decision making on both sides.

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As far as I can see in the literature, there's a lot of,

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explanation of similar scenarios,

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but also there's a lot of different approaches

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to setting both deterministic

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and probabilistic, forecasts.

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And, the thing is that,

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my view is sometimes, and I don't wanna be too critical,

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'cause a lot of these papers are do very well in actually,

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you know, constructing these forecasts,

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but sometimes people I think,

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believe that they might have to use very powerful

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tools in order to do it properly,

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and they don't necessarily check

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to see if simple tools will work as well.

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Um, we've just run a recent experiment with, you know,

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a simple forecast from, solar

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and wind farms using very simple statistical models

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for a short time span.

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If you go a long time span, they won't work,

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but for a short time span,

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like the electricity market in Australia,

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quite often they will work quite well

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as some sophisticated machine learning tools,

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and yet they're f fully explainable in physical sense,

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whereas sometimes the machine learning ones, it's bit hard

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to unpack what they actually mean,

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and maybe they might be not transferable

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as easily and stuff like that.

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The literature, says a lot of good things about,

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different approaches,

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but the nice thing is that,

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especially a few recent papers talk about, you know,

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how useful it is in the sense that we've just explained

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for the generator

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and the, operator of the system as well.

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And I think that this leads into another little point

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that's quite interesting, I think,

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and that is that it's not just good enough

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to think about the skill of the forecasting method,

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but, how useful it is, is it actually of use.

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And in fact, you, researchers from the University

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of Alva Union in, Indian Ocean

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and myself have a new PhD student who has been working

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for the last year

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and over a year in Reunion Island, working on the value

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of forecasting rather than just the skill using,

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some examples from microgrids on that island.

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And, next year she'll be here with me working on,

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looking at the national electricity market

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and looking at the value of forecasting at it in terms of

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not just monetary means, but also so social means as well.

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So that was, a project funded

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by the Australian Renewable Energy Agency.

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Okay? Uh,

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after a request from the Australian energy market operator

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who run the grid and the market in Australia,

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or principally on the East coast over to South Australia

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and including Tasmania, it's too far to have,

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the grid extended to Western Australia.

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They operate their own.

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Um, now, and this was a project called for

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by Arena Renewable Energy Agency.

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Uh, I think there was about 12 consortia, working

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with different solar farms and wind farms to try

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and improve, the five minute forecasting

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of the output from those, farms.

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Now, the reason for that was that originally, the

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amo the operator itself did all that forecasting

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for all the different solar farms and wind farms,

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but then it became too many.

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So they said, okay,

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you've gotta do some self forecasting, okay?

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So we understand what might be available from these,

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variable renewable energy, the systems in the, in,

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you know, five minute, future and stuff like this.

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Um, trouble was that most of the solar

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and wind farms didn't actually have, people

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adept in forecasting methods.

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And so they, amo asked the, energy,

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the arena to fund some projects,

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and we were one of those, we had five different solar

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farms involved.

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And basically what happened was,

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I used my simple statistical tools.

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Um, some people from University of New South Wales used,

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satellite images, to try

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and predict what was going to, how the clouds are going

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to move, shall we say, and then,

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and evolve in the next five minutes.

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Um, some people from the C-S-I-R-O in Australia also had

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sky cameras installed at the solar farms,

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and they were looking at sky images

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and once again, trying

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to bring cloud movement, cloud evolution.

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And then we looked at using machine,

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simple machine learning tools to blend these forecasts

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to improve the overall efficiency of the forecast.

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Now, once again, the goal was not to

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improve the skill of the forecast, but to try

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and avert penalties for the solar farms from

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that cause or pay penalty, okay?

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Because that was the thing.

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They were incurring huge penalties,

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for mis forecasting,

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and they needed to improve that

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to improve their profitability.

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So that was the real goal.

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And we looked at specifically what

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combinations of

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Models seem to work better in particular situations.

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And so, that helped a EO,

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you know, do better in their Australian solar energy

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forecasting systems and Australian wind energy forecasting

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systems as well with the solar

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and wind farms, they don't at the present time

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bid into the system except just their expected forecast.

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Okay? That's it. Um, the more traditional,

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generators,

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they submit bids from minus a thousand dollars

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to plus I think $17,000 per megawatt,

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per megawatt, hour.

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And, that's,

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at diff 10 different price bands.

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And so they'll nominate at each price band

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what they can supply

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and, in the next five minutes, that sort of thing.

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Um, at the moment, solar

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and wind farms only give a particular,

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forecast, okay?

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Or they may not submit a forecast if they don't want

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to participate in that particular five minute interval.

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Um, this will possibly change,

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but I'm not sure exactly how in the future,

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because for instance, in South Australia where I am

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situated, the goal is,

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and it's just about to be put into legislation,

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as I understand that by 2027, a hundred percent

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of electricity will come from renewables.

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And that's, that's not every minute of the, of the year.

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Okay? Over the year, the total,

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generated will be a hundred percent of the supply.

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So sometimes the amount generated would be more than the,

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sorry, the demand,

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more than demand that'll be sent off to other states

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and vice versa at certain times.

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But over the year, the average amount should be equal,

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average amount should be equal.

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And so when that happens, of course, the,

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the idea at the moment is that the solar

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and wind farms are basically price takers.

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Okay? So the price is set by the traditional generators,

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but if there's no traditional generators, what do you do?

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You've gotta set some other type of mechanism.

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So probably at least there'll be some sort of,

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idea of how much you can supply at different,

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price levels, maybe, from the solar and wind farms.

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But in order to do that, that once again, that,

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probabilistic forecast will allow them to

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sort of decide the risk they can take at different levels.

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I mean, if you, if you think, okay, I'm,

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I'm gonna make sure that I

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Get into the market, so you might put a low value on your,

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the amount that you can, a low dollar value,

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for high amounts of electricity.

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But, you know, if you mis forecast,

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you're gonna get penalized a lot.

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Okay? Um, but if the price is high,

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that might be a risk worth taking.

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Okay? So, you know, it, it is probably like that.

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I mean, I'm speculating

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because even the, the operators don't know exactly

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how the market is going to change yet.

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I don't think. It doesn't look like in the, you know,

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the integrated system plan that's just been produced and,

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and other sorts of, documents from a emo

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and I think it's under discussion, to be honest,

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how this will operate.

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But I'm sure that whatever the market structure is,

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especially for solar

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and wind farms, that,

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putting air bounds on the forecast will help them make their

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decisions on the financial side of things better be one

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of the things we found out from it, well,

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it seemed like from the, projects we did was that,

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you know, there's different components to the companies

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that run these, farms.

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And one of them is how we actually manage all the financial,

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actions rather than the sort of technical actions of,

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you know, how you run the farm itself.

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How are you gonna make money out of it on the market?

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And so, you know, these people will be, you know,

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vitally looking at things like probabilistic forecasts if

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they don't do right now, the little experiments we've done

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so far at the moment for short-term forecasting seem

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to show that the,

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machine learning tools, at least the ones we've tried,

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simple neural networks

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and, long short-term memory systems

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and stuff like this seem to work, fairly

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similarly to simple, statistical models.

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When I say statistical models, I'm talking about,

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auto aggressive moving average plus some seasonality,

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especially with solar farms, obviously,

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because there's nothing generated at night

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and, all that sort of stuff.

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Uh, but, one, one place

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where we did, as I said, find that they were very useful was

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that idea of when you look at different approaches,

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physical models like, you know, sky cameras,

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NWP, neter, weather prediction models as well,

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and then, satellite images,

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and then maybe using statistical

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and everything like that as well.

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And then try the machine learning tools

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actually were worked really well at blending those

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and trying to figure out what, particular

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epochs shall we say,

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or particular times of the year, different types

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of models might work better.

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So you get different weights on those models.

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So, you know, possibly that's an area

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where they can be more useful than in a simple,

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straightforward forecast.

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One of the overriding things that you see in a lot

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of the literature is a concept for setting the,

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the, the distribution of the, of the bounds, shall we say,

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is a thing called quantile regression.

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Now, regression itself, most people have heard

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of things like line of best fit.

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So ordinary regression does that type of thing,

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and that's a, that basically forecasts the mean

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of the distribution, okay?

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Quantile regression, basically what you do is try

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to forecast the different percentiles of the distribution

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or quantiles, okay?

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And so you get this sort of all these,

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the whole distribution of the, of the errors

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as you go forward, all right?

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Now that seems to work pretty well,

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for a lot of, researchers.

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Uh, and it seems, quite a,

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a common method sometimes with some varieties to it,

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some improvements, et cetera, et cetera.

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But it seems to be a pretty prevalent method.

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So what I did in the most recent work was compare that

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to something that as far

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as I know nobody else has ever done,

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and that's to take the forecast

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for say, a five minute forecast, find the errors,

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and instead of doing quanta regression on those errors,

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actually take those errors

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and do some, transformation

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of the errors because they're not, they're not symmetric,

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they're certainly not normally distributed,

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but you can do a normalizing transformation

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of the heirs when you do that.

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You can use another method from financial mathematics

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called, arch

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and garch methods for forecasting the variance

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arch means auto aggressive conditional heteros castic,

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it's a mouthful, but see,

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something's heteros castic if the variance changes over

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time, which it does in these systems, okay?

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It's not constant like you'd like to have it, okay?

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You know, if you do a line of best fit, you're hoping

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that the variance around that line

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with the airs is constant, okay?

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With forecasting from solar or wind farms,

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or just the, the resources themselves,

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the variance changes over time, okay?

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Now, if you transform the errors to

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normal distribution, you can use these arch

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and garch methods to forecast the variance.

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Why you transform the normals that those methods rely on,

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the airs being normally distributed in order to do it.

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So you forecast the variance,

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then you can set these error bounds, you know,

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whatever level around the forecast you want back, transform

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to the, the distribution of the variance, of, sorry,

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of the distribution, of the errors that you originally had.

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And then you get the quantiles that way

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it's bit more sophisticated,

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but it seems to work better than the standard,

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quantile regression approaches.

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And, so it's just a little extra wrinkle that I, I,

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I've tried and it seems to improve the, the forecast,

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the probabilistic forecast when I say improve, okay?

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The, there's two things that you want to make sure

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that happen with the probabilistic forecast,

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that when you forecast forward,

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and if you check the actual afterwards, okay,

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if you're trying to make a 95% prediction interval from your

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probabilistic forecast, you hope that 95%

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of those observe values as they come along

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actually fall within that interval

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and, you know, other intervals as well.

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But also you want to have sharpness,

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so you want the interval as narrow as possible, okay?

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So in both cases, I got the right sort of coverage,

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shall we say, but with, with that transformation method,

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the intervals were a little bit sharper.

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Maybe there's a nice way where we can actually take, say,

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some, which I didn't, we didn't try

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with this last little experiment, take the forecast from,

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a simple statistical method

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and a forecast from the LSTM method

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and maybe figure out how to

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blend them in a nice optimal way.

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Because perhaps,

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even though the overall statistics are pretty similar

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with the two methods, maybe at certain types of situations,

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one works better than the other.

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And if you can, you know, somehow

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use some machine learning methods to, optimally

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blend those two, that could be really beneficial.

"Enhancing Energy Market Decisions: The Importance of Probabilistic Forecasting in Renewable Energy Generation"

00:00:00 - It's my belief that instead of just forecasting the expected, generation, shall we say, from a solar, wind farm in the next five minutes, which is the timeframe that the energy market works on in Australia, or even, you know, a longer period, it's better if you know the bounds around that forecast. I think are they, are they narrow? Is your forecast really sharp or are they wide? Is your forecast, you know, a bit loose, shall we say? because that can help the decision making both on, the generator's side and on the operator of the system
00:00:44 - I think with the, on the generator side, let's, have a little scenario. For instance, say you've got a, a solar farm and you have a battery, attached to it as well, which is becoming more and more common, or a wind farm as well. Okay? So you predict in the next five minutes, you're going to output a hundred megawatts from the, from the farm. Okay? Now, if the bands are, the bands around, or the, the possible error around that is small, okay, then you can be sort of more confident
00:01:25 - Because if you mis forecast, in other words, if your forecast is a hundred, but you produce 80 or 120, it may mean that the grid operator will actually penalize you for mis forecasting. There's a thing called cause or pays in the, energy market in Australia, for instance, if you under forecast, they have to bring more power on the grid from somewhere else. You basically have to pay for that fact. And if you, forecast too low, right? They may have to cut somebody else off, okay? That sort of thing. And so if your bands are wide
00:02:08 - I'd rather put that, electricity into my battery now and then send it to the grid when I know exactly what I can get out from the battery rather than put it into the grid and risk a penalty. Okay? But if they're narrow, the bands around the forecast, you're confident it's okay on the operator side of the, of the situation. Basically what happens is that if you have probabilistic forecasts and you can sort of look at the, the near future, you say, okay, well, it looks like the, the air bounds
00:02:47 - I don't have to think about too much in reserve to, to, you know, fix up the problem if there is one. Whereas if they're wide, you might have to be flexible in the operation of The grid. So it helps the decision making on both sides. As far as I can see in the literature, there's a lot of, explanation of similar scenarios, but also there's a lot of different approaches to setting both deterministic and probabilistic, forecasts. And, the thing is that,
00:03:29 - 'cause a lot of these papers are do very well in actually, you know, constructing these forecasts, but sometimes people I think, believe that they might have to use very powerful tools in order to do it properly, and they don't necessarily check to see if simple tools will work as well. we've just run a recent experiment with, you know, a simple forecast from, solar and wind farms using very simple statistical models

"Enhancing Short-Term Energy Forecasting: The Integration of Statistical Methods and Machine Learning in Australia's Renewable Energy Market"

00:04:07 - If you go a long time span, they won't work, but for a short time span, like the electricity market in Australia, quite often they will work quite well as some sophisticated machine learning tools, and yet they're f fully explainable in physical sense, whereas sometimes the machine learning ones, it's bit hard to unpack what they actually mean, and maybe they might be not transferable as easily and stuff like that. The literature, says a lot of good things about, different approaches, but the nice thing is that, especially a few recent papers talk about, you know,
00:04:50 - for the generator and the, operator of the system as well. And I think that this leads into another little point that's quite interesting, I think, and that is that it's not just good enough to think about the skill of the forecasting method, but, how useful it is, is it actually of use. And in fact, you, researchers from the University of Alva Union in, Indian Ocean and myself have a new PhD student who has been working for the last year and over a year in Reunion Island, working on the value
00:05:33 - some examples from microgrids on that island. And, next year she'll be here with me working on, looking at the national electricity market and looking at the value of forecasting at it in terms of not just monetary means, but also so social means as well. So that was, a project funded by the Australian Renewable Energy Agency. Okay? after a request from the Australian energy market operator who run the grid and the market in Australia, or principally on the East coast over to South Australia and including Tasmania, it's too far to have,
00:06:13 - They operate their own. now, and this was a project called for by Arena Renewable Energy Agency. I think there was about 12 consortia, working with different solar farms and wind farms to try and improve, the five minute forecasting of the output from those, farms. Now, the reason for that was that originally, the amo the operator itself did all that forecasting for all the different solar farms and wind farms, but then it became too many.
00:06:53 - you've gotta do some self forecasting, okay? So we understand what might be available from these, variable renewable energy, the systems in the, in, you know, five minute, future and stuff like this. trouble was that most of the solar and wind farms didn't actually have, people adept in forecasting methods. And so they, amo asked the, energy, the arena to fund some projects, and we were one of those, we had five different solar farms involved.
00:07:36 - I used my simple statistical tools. some people from University of New South Wales used, satellite images, to try and predict what was going to, how the clouds are going to move, shall we say, and then, and evolve in the next five minutes. some people from the C-S-I-R-O in Australia also had sky cameras installed at the solar farms, and they were looking at sky images and once again, trying to bring cloud movement, cloud evolution. And then we looked at using machine, simple machine learning tools to blend these forecasts

"Balancing Profitability and Forecasting Accuracy in Renewable Energy Markets"

00:08:20 - Now, once again, the goal was not to improve the skill of the forecast, but to try and avert penalties for the solar farms from that cause or pay penalty, okay? Because that was the thing. They were incurring huge penalties, for mis forecasting, and they needed to improve that to improve their profitability. So that was the real goal. And we looked at specifically what combinations of Models seem to work better in particular situations.
00:09:04 - you know, do better in their Australian solar energy forecasting systems and Australian wind energy forecasting systems as well with the solar and wind farms, they don't at the present time bid into the system except just their expected forecast. Okay? That's it. the more traditional, generators, they submit bids from minus a thousand dollars to plus I think $17,000 per megawatt, per megawatt, hour.
00:09:43 - at diff 10 different price bands. And so they'll nominate at each price band what they can supply and, in the next five minutes, that sort of thing. at the moment, solar and wind farms only give a particular, forecast, okay? Or they may not submit a forecast if they don't want to participate in that particular five minute interval. this will possibly change, but I'm not sure exactly how in the future, because for instance, in South Australia where I am
00:10:24 - and it's just about to be put into legislation, as I understand that by 2027, a hundred percent of electricity will come from renewables. And that's, that's not every minute of the, of the year. Okay? Over the year, the total, generated will be a hundred percent of the supply. So sometimes the amount generated would be more than the, sorry, the demand, more than demand that'll be sent off to other states and vice versa at certain times. But over the year, the average amount should be equal,
00:11:04 - And so when that happens, of course, the, the idea at the moment is that the solar and wind farms are basically price takers. Okay? So the price is set by the traditional generators, but if there's no traditional generators, what do you do? You've gotta set some other type of mechanism. So probably at least there'll be some sort of, idea of how much you can supply at different, price levels, maybe, from the solar and wind farms. But in order to do that, that once again, that, probabilistic forecast will allow them to
00:11:48 - I mean, if you, if you think, okay, I'm, I'm gonna make sure that I Get into the market, so you might put a low value on your, the amount that you can, a low dollar value, for high amounts of electricity. But, you know, if you mis forecast, you're gonna get penalized a lot. Okay? but if the price is high, that might be a risk worth taking. Okay? So, you know, it, it is probably like that. I mean, I'm speculating because even the, the operators don't know exactly how the market is going to change yet. I don't think. It doesn't look like in the, you know,

"Enhancing Financial Decision-Making for Renewable Energy through Probabilistic Forecasting Techniques"

00:12:29 - and other sorts of, documents from a emo and I think it's under discussion, to be honest, how this will operate. But I'm sure that whatever the market structure is, especially for solar and wind farms, that, putting air bounds on the forecast will help them make their decisions on the financial side of things better be one of the things we found out from it, well, it seemed like from the, projects we did was that, you know, there's different components to the companies that run these, farms. And one of them is how we actually manage all the financial,
00:13:13 - you know, how you run the farm itself. How are you gonna make money out of it on the market? And so, you know, these people will be, you know, vitally looking at things like probabilistic forecasts if they don't do right now, the little experiments we've done so far at the moment for short-term forecasting seem to show that the, machine learning tools, at least the ones we've tried, simple neural networks and, long short-term memory systems and stuff like this seem to work, fairly
00:13:55 - When I say statistical models, I'm talking about, auto aggressive moving average plus some seasonality, especially with solar farms, obviously, because there's nothing generated at night and, all that sort of stuff. but, one, one place where we did, as I said, find that they were very useful was that idea of when you look at different approaches, physical models like, you know, sky cameras, NWP, neter, weather prediction models as well, and then, satellite images, and then maybe using statistical
00:14:34 - And then try the machine learning tools actually were worked really well at blending those and trying to figure out what, particular epochs shall we say, or particular times of the year, different types of models might work better. So you get different weights on those models. So, you know, possibly that's an area where they can be more useful than in a simple, straightforward forecast. One of the overriding things that you see in a lot of the literature is a concept for setting the, the, the distribution of the, of the bounds, shall we say,
00:15:18 - Now, regression itself, most people have heard of things like line of best fit. So ordinary regression does that type of thing, and that's a, that basically forecasts the mean of the distribution, okay? Quantile regression, basically what you do is try to forecast the different percentiles of the distribution or quantiles, okay? And so you get this sort of all these, the whole distribution of the, of the errors as you go forward, all right? Now that seems to work pretty well,
00:15:57 - and it seems, quite a, a common method sometimes with some varieties to it, some improvements, et cetera, et cetera. But it seems to be a pretty prevalent method. So what I did in the most recent work was compare that to something that as far as I know nobody else has ever done, and that's to take the forecast for say, a five minute forecast, find the errors, and instead of doing quanta regression on those errors,

"Enhancing Forecast Accuracy through Variance Transformation and Model Blending Techniques"

00:16:38 - and do some, transformation of the errors because they're not, they're not symmetric, they're certainly not normally distributed, but you can do a normalizing transformation of the heirs when you do that. You can use another method from financial mathematics called, arch and garch methods for forecasting the variance arch means auto aggressive conditional heteros castic, it's a mouthful, but see, something's heteros castic if the variance changes over time, which it does in these systems, okay?
00:17:22 - You know, if you do a line of best fit, you're hoping that the variance around that line with the airs is constant, okay? With forecasting from solar or wind farms, or just the, the resources themselves, the variance changes over time, okay? Now, if you transform the errors to normal distribution, you can use these arch and garch methods to forecast the variance. Why you transform the normals that those methods rely on,
00:18:04 - So you forecast the variance, then you can set these error bounds, you know, whatever level around the forecast you want back, transform to the, the distribution of the variance, of, sorry, of the distribution, of the errors that you originally had. And then you get the quantiles that way it's bit more sophisticated, but it seems to work better than the standard, quantile regression approaches. And, so it's just a little extra wrinkle that I, I,
00:18:44 - the probabilistic forecast when I say improve, okay? The, there's two things that you want to make sure that happen with the probabilistic forecast, that when you forecast forward, and if you check the actual afterwards, okay, if you're trying to make a 95% prediction interval from your probabilistic forecast, you hope that 95% of those observe values as they come along actually fall within that interval and, you know, other intervals as well. But also you want to have sharpness, so you want the interval as narrow as possible, okay?
00:19:27 - shall we say, but with, with that transformation method, the intervals were a little bit sharper. Maybe there's a nice way where we can actually take, say, some, which I didn't, we didn't try with this last little experiment, take the forecast from, a simple statistical method and a forecast from the LSTM method and maybe figure out how to blend them in a nice optimal way. Because perhaps, even though the overall statistics are pretty similar
00:20:18 - one works better than the other. And if you can, you know, somehow use some machine learning methods to, optimally blend those two, that could be really beneficial.

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