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.'' |
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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. |
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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. |
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== Background Readings == |
== 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. |
* '''"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. |
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== Conferences, Journals, and Professional Organizations == |
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* '''"Electrical load forecasting models: A critical systematic review" (2017)'''<ref>{{Cite journal|last=Kuster|first=Corentin|last2=Rezgui|first2=Yacine|last3=Mourshed|first3=Monjur|date=2017-11|title=Electrical load forecasting models: A critical systematic review|url=http://dx.doi.org/10.1016/j.scs.2017.08.009|journal=Sustainable Cities and Society|volume=35|pages=257–270|doi=10.1016/j.scs.2017.08.009|issn=2210-6707}}</ref>: A review and taxonomy of electricity load forecasting models. |
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* '''"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=http://dx.doi.org/10.1016/j.ijforecast.2015.11.011|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. |
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== Community == |
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=== Journals === |
=== Journals === |
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* '''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]. |
* '''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]. |
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== Libraries and Tools == |
== 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]. |
* '''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]. |
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* '''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]. |
* '''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]. |
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* '''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]. |
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== Future Directions == |
== 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. |
* '''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. |
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* '''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. |
* '''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. |
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== References == |
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Latest revision as of 07:27, 5 July 2022
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]
- International Journal of Forecasting: The official journal of the International Institute of Forecasters. Journal website here.
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]
- See the Electricity Systems page for general electricity systems datasets.
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.
Wind power forecasting[edit | edit source]
- 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 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]
- ↑ 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) - ↑ 2.0 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) - ↑ 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) - ↑ 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) - ↑ 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.