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.
 
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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 [https://en.wikipedia.org/wiki/Energy_forecasting Wikipedia page] on this topic.''
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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.
   
 
==Background Readings==
 
==Background Readings==
===Electricity demand forecasting===
 
   
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*'''"Energy forecasting: A review and outlook" (2020)'''. <ref>{{Cite journal|last=Tao|first=Hong|last2=Pinson|first2=Pierre|last3=Wang|first3=Yi|last4=Weron|first4=Rafal|last5=Yang|first5=Dazhi|last6=Zareipour|first6=Hamidreza|date=2020|title=Energy forecasting: A review and outlook|url=https://ieeexplore.ieee.org/document/9218967|journal=IEEE Open Access Journal of Power and Energy}}</ref>: A high level review and overview of the energy forecasting field and challenges.
 
*'''"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.
 
*'''"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.
 
*'''"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.
 
*'''"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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*'''"Review of Low-Voltage Load Forecasting: Methods, Applications, and Recommendations" (2021)'''<ref>{{Cite journal|last=Haben|first=Stephen|last2=Arora|first2=Siddharth|last3=Giasemidis|first3=Georgios|last4=Voss|first4=Marcus|last5=Greetham|first5=Danica Vukadinovic|year=2021|title=Review of Low-Voltage Load Forecasting: Methods, Applications, and Recommendations|url=https://arxiv.org/abs/2106.00006|journal=Applied Energy|doi=10.1016/j.apenergy.2021.117798|issn=0306-2619|volume=304|pages=117798}}</ref>: A review of load forecasting with focus of distribution grids containing a chapter on machine learning approaches applied in the field. It further addresses problems in the field like a lack of benchmarks and dataset bias.
   
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==Conferences, Journals, and Professional Organizations==
==Community==
 
 
===Journals===
 
===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].
 
*'''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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*'''Foresight''': Another journal published by the [https://forecasters.org/ International Institute of Forecasters] with a more applied focus. Journal website [https://forecasters.org/foresight/ here].
   
 
==Libraries and Tools==
 
==Libraries and Tools==
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*See the [[Electricity Systems]] page for general electricity systems datasets.
 
*See the [[Electricity Systems]] page for general electricity systems datasets.
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===Distribution System===
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* '''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 [https://low-voltage-loadforecasting.github.io/ here].
   
 
==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.
 
*'''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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== Relevant Groups and Organizations ==
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* '''International Institute of Forecasters''': An organization for researchers, practitioners, and students, publishing the [https://www.journals.elsevier.com/international-journal-of-forecasting International Journal of Forecasting] and organizing the [https://isf.forecasters.org/ International Symposium on Forecasting (ISF)]. Homepage [https://forecasters.org/ here].
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* '''IEEE Working Group on Energy Forecasting and Analytics:''' A working group for energy forecasting that has organized for instance [http://www.drhongtao.com/gefcom GEFCOM challenges], homepage [http://www.eeyiwang.com/WGEFA.html here].
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== References ==

Latest revision as of 12:47, 13 January 2022

🌎 This article is a stub, and is currently under construction. You can help by adding to it!

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.

Background Readings[edit | edit source]

  • "Energy forecasting: A review and outlook" (2020). [1]: A high level review and overview of the energy forecasting field and challenges.
  • "Electrical load forecasting models: A critical systematic review" (2017)[2]: A review and taxonomy of electricity load forecasting models.
  • "Probabilistic electric load forecasting: A tutorial review" (2016)[3]: A tutorial and review of methods of probabilistic electricity load forecasting.
  • "Review of Low-Voltage Load Forecasting: Methods, Applications, and Recommendations" (2021)[4]: A review of load forecasting with focus of distribution grids containing a chapter on machine learning approaches applied in the field. It further addresses problems in the field like a lack of benchmarks and dataset bias.

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

Journals[edit | edit source]

Libraries and Tools[edit | edit source]

Data[edit | edit source]

General[edit | edit source]

Distribution System[edit | edit source]

  • 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.

Future Directions[edit | edit source]

  • 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]

References[edit | edit source]

  1. Tao, Hong; Pinson, Pierre; Wang, Yi; Weron, Rafal; Yang, Dazhi; Zareipour, Hamidreza (2020). "Energy forecasting: A review and outlook". IEEE Open Access Journal of Power and Energy.
  2. 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: |date= (help)
  3. 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: |date= (help)
  4. Haben, Stephen; Arora, Siddharth; Giasemidis, Georgios; Voss, Marcus; Greetham, Danica Vukadinovic (2021). "Review of Low-Voltage Load Forecasting: Methods, Applications, and Recommendations". Applied Energy. 304: 117798. doi:10.1016/j.apenergy.2021.117798. ISSN 0306-2619.
  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.