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''This page is about the intersection of electricity systems and machine learning (ML) in the context of climate change mitigation. For an overview of electricity systems as a whole, please see the [https://en.wikipedia.org/wiki/Electric_power_system Wikipedia page] on this topic.''[[File:ElectricitySystems.png|thumb|A schematic of selected opportunities to reduce greenhouse emissions from electricity systems using machine learning. From "Tackling Climate Change with Machine Learning."<ref name=":0">{{Cite journal|last=Rolnick|first=David|last2=Donti|first2=Priya L.|last3=Kaack|first3=Lynn H.|last4=Kochanski|first4=Kelly|last5=Lacoste|first5=Alexandre|last6=Sankaran|first6=Kris|last7=Ross|first7=Andrew Slavin|last8=Milojevic-Dupont|first8=Nikola|last9=Jaques|first9=Natasha|last10=Waldman-Brown|first10=Anna|last11=Luccioni|first11=Alexandra|date=2019-11-05|title=Tackling Climate Change with Machine Learning|url=http://arxiv.org/abs/1906.05433|journal=arXiv:1906.05433 [cs, stat]}}</ref>]]AsThe describedenergy insupply thesector papercontributes "Tacklingabout Climate35% Changeof withhuman-caused Machinegreenhouse gas Learning"emissions,<ref name=":0" />:<blockquote>AIBruckner hasT., beenI.A. calledBashmakov, theY. newMulugetta, electricityH. Chum, givenA. itsde potentialla toVega transformNavarro, entireJ. industriesEdmonds, A.<ref>{{Cite web|title=AndrewFaaij, Ng:B. ArtificialFungtammasan, IntelligenceA. isGarg, theE. NewHertwich, ElectricityD. -Honnery, YouTube|url=https://wwwD.youtube Infield, M.com/watch?v=21EiKfQYZXc|website=www Kainuma, S.youtube Khennas, S.com}}</ref> InterestinglyKim, electricityH.B. itselfNimir, isK. oneRiahi, ofN. theStrachan, industriesR. thatWiser, AIand isX. poisedZhang, to2014: transformEnergy Systems. ManyIn: electricity''Climate systemsChange are2014: awashMitigation inof data,Climate andChange. theContribution industryof hasWorking begunGroup III to envisionthe next-generationFifth systemsAssessment (smartReport grids)of driventhe byIntergovernmental AIPanel andon ML.<ref>{{CiteClimate journal|last=Ramchurn|first=SarvapaliChange'' D[Edenhofer, O.|last2=Vytelingum|first2=Perukrishnen|last3=Rogers|first3=Alex|last4=Jennings|first4=Nicholas, R.|date=2012|title=Putting thePichs-Madruga, 'smarts'Y. intoSokona, theE. smartFarahani, grid|url=http://dxS.doi Kadner, K.org/10 Seyboth, A.1145/2133806 Adler, I.2133825|journal=Communications ofBaum, theS. ACM|volume=55|issue=4|pages=86–97|doi=10Brunner, P.1145/2133806 Eickemeier, B.2133825|issn=0001-0782|via=}}</ref><ref>{{Citation|title=Machine LearningKriemann, TechniquesJ. forSavolainen, SupportingS. RenewableSchlömer, EnergyC. Generationvon andStechow, Integration:T. AZwickel and Survey|url=http://dxJ.doiC.org/10 Minx (eds.)].1007/978-3-319-13290-7_7|publisher=Springer InternationalCambridge Publishing}}University Press, Cambridge, United Kingdom and New York, NY, USA.</ref><ref>{{Cite web|title=Howwithin artificialwhich intelligencedecarbonizing willelectricity affectsupply theplays futurean ofimportant energyrole. andIn addition, many climate change strategies in sectors such as [[Buildings and Cities|url=https://www.brookings.edu/research/how-artificial-intelligencebuildings]], [[transportation]], and [[industry]] rely on low-carbon electricity. To reduce greenhouse gas emissions from electricity systems, it will be necessary to both transition quickly to low-affect-the-future-of-energy-carbon electricity sources (e.g., solar, wind, and-climate/|website=Brookings|date=2019-01-10|language=en-US}}</ref> nuclear) and to reduce emissions from existing electricity system operations in the meantime.
AI and machine learning are often discussed in the electricity sector in the context of smart grids,<ref>{{Cite journal|last=Ramchurn|first=Sarvapali D.|last2=Vytelingum|first2=Perukrishnen|last3=Rogers|first3=Alex|last4=Jennings|first4=Nicholas R.|date=2012|title=Putting the 'smarts' into the smart grid|url=http://dx.doi.org/10.1145/2133806.2133825|journal=Communications of the ACM|volume=55|issue=4|pages=86–97|doi=10.1145/2133806.2133825|issn=0001-0782|via=}}</ref><ref>{{Citation|title=Machine Learning Techniques for Supporting Renewable Energy Generation and Integration: A Survey|url=http://dx.doi.org/10.1007/978-3-319-13290-7_7|publisher=Springer International Publishing}}</ref><ref>{{Cite web|title=How artificial intelligence will affect the future of energy and climate|url=https://www.brookings.edu/research/how-artificial-intelligence-will-affect-the-future-of-energy-and-climate/|website=Brookings|date=2019-01-10|language=en-US}}</ref> which broadly refer to the concept of "intelligent" electric grids managed automatically in a data-driven manner. In particular, ML has been used to forecast electricity supply and demand, to improve power system optimization, and to improve system efficiency through applications such as predictive maintenance. In addition, ML has also been used to accelerate scientific discovery of clean energy technologies, and to gather electricity infrastructure data that may be useful for system planners and policymakers.<ref name=":0" />
Electricity systems are responsible for about a quarter of human-caused greenhouse gas emissions each year.<ref>IPCC. Climate Change 2014: ''Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [O. Edenhofer, R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlomer, C. von Stechow, T. Zwickel, J.C. Minx, (eds.)]. 2014.</ref> Moreover, as buildings, transportation, and other sectors seek to replace GHG-emitting fuels, demand for low-carbon electricity will grow. To reduce emissions from electricity systems, society must
* Rapidly transition to low-carbon electricity sources (such as solar, wind, hydro, and nuclear) and phase out carbon-emitting sources (such as coal, natural gas, and other fossil fuels).
* Reduce emissions from existing CO<sub>2</sub>-emitting power plants, since the transition to low-carbon power will not happen overnight.
* Implement these changes across all countries and contexts, as electricity systems are everywhere.
ML can contribute on all fronts by informing the research, deployment, and operation of electricity system technologies. Such contributions include accelerating the development of clean energy technologies, improving forecasts of demand and clean energy, improving electricity system optimization and management, and enhancing system monitoring. These contributions require a variety of ML paradigms and techniques, as well as close collaborations with the electricity industry and other experts to integrate insights from operations research, electrical engineering, physics, chemistry, the social sciences, and other fields.</blockquote>
== Machine Learning Application Areas ==
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