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This page is about the applications of machine learning (ML) in the context of energy demand forecasting. For an overview of energy 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.
- "Electrical load forecasting models: A critical systematic review" (2017): A review and taxonomy of electricity load forecasting models.
- "Probabilistic electric load forecasting: A tutorial review" (2016): A tutorial and review of methods of probabilistic electricity load forecasting.
Conferences, Journals, and Professional Organizations
- International Journal of Forecasting: The official journal of the International Institute of Forecasters. Journal website here.
Libraries and Tools
- See the Electricity Systems page for general electricity systems datasets.
- List of Low-voltage level load data sets: Curated list of smart meter data from the household and building level as well as substation data in the distribution system, available here.
- 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, 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 Groups and Organizations
- Kuster, Corentin; Rezgui, Yacine; Mourshed, Monjur (2017-11). "Electrical load forecasting models: A critical systematic review". Sustainable Cities and Society. 35: 257–270. doi:10.1016/j.scs.2017.08.009. ISSN 2210-6707. Check date values in:
- Hong, Tao; Fan, Shu (2016-07). "Probabilistic electric load forecasting: A tutorial review". International Journal of Forecasting. 32 (3): 914–938. doi:10.1016/j.ijforecast.2015.11.011. ISSN 0169-2070. Check date values in:
- 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.