Energy Demand Forecasting: Difference between revisions

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[TODO -- redo intro] Since variable generation and electricity demand both fluctuate, they must be forecast ahead of time to inform real-time electricity scheduling and longer-term system planning. Better short-term forecasts can allow system operators to reduce their reliance on polluting standby plants and to proactively manage increasing amounts of variable sources. Better long-term forecasts can help system operators (and investors) determine where and when power plants should be built. While many system operators today use basic forecasting techniques, forecasts will need to become increasingly accurate, span multiple horizons in time and space, and better quantify uncertainty to support these use cases. Machine learning has commonly been used on all these fronts.
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==
*'''"Probabilistic electric load forecasting: A tutorial review" (2016)'''<ref>{{Cite journal|last=Hong|first=Tao|last2=Fan|first2=Shu|date=2016-07|title=Probabilistic electric load forecasting: A tutorial review|url=|journal=International Journal of Forecasting|volume=32|issue=3|pages=914–938|doi=10.1016/j.ijforecast.2015.11.011|issn=0169-2070}}</ref>: A tutorial and review of methods of probabilistic electricity load forecasting.
==Conferences, Journals, and Professional Organizations==
*'''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.
== Relevant Organizations ==
== References ==