How were the forecasts for optimizing demand side generated?

Power Laws
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Hi @bull,

Would you be able to tell us how the load and PV forecasts were generated for each site?

Thanks!

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That’s an interesting question. My guess is that they simply add random noise to true values.

It tried to improve the forecasts using machine learning but I only could improve the accuracy about a 2%.

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I’m doing the master thesis on this topic in Lappeenranta University of Technology. Our team used 6 heuristics and reinforcement learning. We picked the best model for the each site based on the score on previous runs and time-series statistics. It gave us about -0.15xx.

@hoa.nguyen, @twsthomas@gmail.com, @Barbe, @ironbar could you share your approach in this task please? Your scores are much higher than our. What is your secret?)

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Hi Denis,
If I end on the price positions I will probably make a detailed post of my solution.

The summary of my solution:

  • Dynamic programming
  • Simplification of the problem. After thinking I discovered that I could group the timesteps into bigger blocks. The reason for making divisions were: changes on prices and changes on the balance of the system(positive balance or negative balance). So instead of optimizing for 96 timesteps the problem was simplified to a few blocks.
  • Simplification of the actions available at each timestep. I carefully think of which actions had sense depending on the state of the battery and the balance of the system. This was important to do a fast optimization.
  • Small improvement of the forecast using linear regression. But the effect was very small.
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