Electricity Systems: Difference between revisions

''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>]]As described in the paper "Tackling Climate Change with Machine Learning"<ref name=":0" />:<blockquote>AI has been called the new electricity, given its potential to transform entire industries.<ref>{{Cite web|title=Andrew Ng: Artificial Intelligence is the New Electricity - YouTube|url=https://www.youtube.com/watch?v=21EiKfQYZXc|website=www.youtube.com}}</ref> Interestingly, electricity itself is one of the industries that AI is poised to transform. Many electricity systems are awash in data, and the industry has begun to envision next-generation systems (smart grids) driven by AI and ML.<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>

ElectricityThe systemsenergy aresupply responsiblesector forcontributes about a quarter35% of human-caused greenhouse gas emissions each year.,<ref>IPCCBruckner T., ClimateI.A. ChangeBashmakov, Y. Mulugetta, H. Chum, A. de la Vega Navarro, J. Edmonds, A. Faaij, B. Fungtammasan, A. Garg, E. Hertwich, D. Honnery, D. Infield, M. Kainuma, S. Khennas, S. Kim, H.B. Nimir, K. Riahi, N. Strachan, R. Wiser, and X. Zhang, 2014: Energy Systems. In: ''Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change'' [Edenhofer, 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. SchlomerSchlömer, C. von Stechow, T. Zwickel, and J.C. Minx, (eds.)]. 2014Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.</ref> Moreoverwithin which decarbonizing electricity supply plays an important role. In addition, many climate change strategies in sectors such as [[Buildings and Cities|buildings]], [[transportation]], and other[[industry]] sectorsrely seekon tolow-carbon replaceelectricity. GHG-emittingTo fuelsreduce greenhouse gas emissions from electricity systems, demandit forwill be necessary to both transition quickly to low-carbon electricity willsources grow(e.g., Tosolar, wind, and nuclear) and to reduce emissions from existing electricity systems,system societyoperations mustin the meantime.

*'''[[Electricity Supply Forecasting|Supply]] and, [[Energy Demand Forecasting|demand]] and [[Energy Price Forecasting|price]] forecasting''': The supply and demand of power must both be forecast ahead of time to inform electricity planning and scheduling. In more volatile energy systems, also forecasting of prices becomes relevant to utilize flexibility effectively. ML can help make these forecasts more accurate, improve temporal and spatial resolution, and quantify uncertainty.