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Electricity Supply Forecasting: Difference between revisions
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''This page is about the applications of machine learning (ML) in the context of electricity supply forecasting. For an overview of [https://en.wikipedia.org/wiki/Energy_forecasting energy forecasting], [https://en.wikipedia.org/wiki/Wind_power_forecasting wind], or [https://en.wikipedia.org/wiki/Solar_power_forecasting solar forecasting] more generally, please see the Wikipedia page on this topic.''
The supply and demand of power must both be forecast ahead of time to inform electricity planning and scheduling. ML can help make these forecasts more accurate, improve temporal and spatial resolution, and quantify uncertainty.
== Background Readings ==
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* '''"A review on the forecasting of wind speed and generated power"''' '''(2009)'''<ref>{{Cite journal|last=Lei|first=Ma|last2=Shiyan|first2=Luan|last3=Chuanwen|first3=Jiang|last4=Hongling|first4=Liu|last5=Yan|first5=Zhang|date=2009-05|title=A review on the forecasting of wind speed and generated power|url=http://dx.doi.org/10.1016/j.rser.2008.02.002|journal=Renewable and Sustainable Energy Reviews|volume=13|issue=4|pages=915–920|doi=10.1016/j.rser.2008.02.002|issn=1364-0321}}</ref>: A review of work in wind speed and wind power forecasting.
== Conferences, Journals, and Professional Organizations ==
=== Journals ===
* '''International Journal of Forecasting''': The official journal of the [https://forecasters.org/ International Institute of Forecasters]. Journal website [https://www.journals.elsevier.com/international-journal-of-forecasting here].
=== Organizations ===▼
* '''OpenClimateFix''': A "non-profit research and development lab" working on projects including solar PV forecasting and PV panel mapping. Website [https://openclimatefix.org/ here].▼
== Libraries and Tools ==
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* '''SubseasonalRodeo''': "A benchmark dataset for training and evaluating ''subseasonal forecasting systems''—systems predicting temperature or precipitation 2-6 weeks in advance—in the western contiguous United States." Available [https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/IHBANG here].
* '''American Meteorological Society 2013-2014 Solar Energy Prediction Contest''': Contest data for producing daily forecasts of solar energy, available [https://www.kaggle.com/c/ams-2014-solar-energy-prediction-contest here].
=== Wind power forecasting ===
* '''A collection and categorization of open-source wind and wind power datasets''': The paper gives an overview of open-source wind and wind power datasets, available [https://onlinelibrary.wiley.com/doi/epdf/10.1002/we.2766 here].
== Future Directions ==
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* '''Decision-integration:''' As supply and demand forecasts ultimately need to inform power system optimization decisions, a fruitful direction may be to integrate knowledge of how these decisions are made into ML models. For instance, deep neural networks have been used to forecast electricity demand in a way that optimizes for electricity scheduling costs rather than forecast accuracy,<ref>Donti, Priya, Brandon Amos, and J. Zico Kolter. "Task-based end-to-end model learning in stochastic optimization." In ''Advances in Neural Information Processing Systems'', pp. 5484-5494. 2017.</ref> and this notion could be extended to optimizing for greenhouse gas emissions.
* '''Interpretable/explainable ML and uncertainty quantification:''' Techniques that explain or quantify the uncertainty of forecasts could help power system operators better integrate these forecasts into their operations, and facilitate applications such as robust optimization.
▲* '''OpenClimateFix''': A "non-profit research and development lab" working on projects including solar PV forecasting and PV panel mapping. Website [https://openclimatefix.org/ here].
== References ==
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