Electricity Supply Forecasting

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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 energy forecasting, wind, or 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[edit | edit source]

Solar power forecasting[edit | edit source]

  • "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization" (2020)[1]: A review of work in solar power forecasting.
  • "Forecasting of photovoltaic power generation and model optimization: A review" (2018)[2]: A review of work in solar power forecasting.

Wind power forecasting[edit | edit source]

  • "Current methods and advances in forecasting of wind power generation" (2012)[3]: A review of work in wind power forecasting.
  • "A review on the forecasting of wind speed and generated power" (2009)[4]: A review of work in wind speed and wind power forecasting.

Conferences, Journals, and Professional Organizations[edit | edit source]

Journals[edit | edit source]

Libraries and Tools[edit | edit source]

  • See this list by Jack Kelly of Open Climate Fix for useful tools for processing and visualizing data during solar PV nowcasting workflows.

Data[edit | edit source]

General[edit | edit source]

Solar power forecasting[edit | edit source]

  • See this list by Jack Kelly of Open Climate Fix for useful data sources for solar PV nowcasting.
  • 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 here.
  • American Meteorological Society 2013-2014 Solar Energy Prediction Contest: Contest data for producing daily forecasts of solar energy, available here.

Future Directions[edit | edit source]

  • Hybrid physical modeling: As weather fundamentally drives solar and wind power production, and important direction could be for solar and wind power forecasts to draw on innovations in climate modeling, weather forecasting, and hybrid physical modeling techniques.[2] These techniques might be able to improve short- to medium-term forecasts (e.g., by modeling fine-grained wind turbulence) and enable ML-based forecasts to deal with issues of weather distribution shift for longer-term (e.g. year-scale) forecasts.
  • 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,[5] 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[edit | edit source]

  • OpenClimateFix: A "non-profit research and development lab" working on projects including solar PV forecasting and PV panel mapping. Website here.

References[edit | edit source]

  1. Ahmed, R.; Sreeram, V.; Mishra, Y.; Arif, M.D. (2020-05). "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization". Renewable and Sustainable Energy Reviews. 124: 109792. doi:10.1016/j.rser.2020.109792. ISSN 1364-0321. Check date values in: |date= (help)
  2. 2.0 2.1 Das, Utpal Kumar; Tey, Kok Soon; Seyedmahmoudian, Mehdi; Mekhilef, Saad; Idris, Moh Yamani Idna; Van Deventer, Willem; Horan, Bend; Stojcevski, Alex (2018-01). "Forecasting of photovoltaic power generation and model optimization: A review". Renewable and Sustainable Energy Reviews. 81: 912–928. doi:10.1016/j.rser.2017.08.017. ISSN 1364-0321. Check date values in: |date= (help)
  3. Foley, Aoife M.; Leahy, Paul G.; Marvuglia, Antonino; McKeogh, Eamon J. (2012-01). "Current methods and advances in forecasting of wind power generation". Renewable Energy. 37 (1): 1–8. doi:10.1016/j.renene.2011.05.033. ISSN 0960-1481. Check date values in: |date= (help)
  4. Lei, Ma; Shiyan, Luan; Chuanwen, Jiang; Hongling, Liu; Yan, Zhang (2009-05). "A review on the forecasting of wind speed and generated power". Renewable and Sustainable Energy Reviews. 13 (4): 915–920. doi:10.1016/j.rser.2008.02.002. ISSN 1364-0321. Check date values in: |date= (help)
  5. 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.