Electricity Systems

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This page is about the intersection of electricity systems and machine learning in the context of climate change mitigation. For an overview of electricity systems as a whole, please see the Wikipedia page on this topic.
A schematic of selected opportunities to reduce greenhouse emissions from electricity systems using machine learning. From "Tackling Climate Change with Machine Learning."[1]
As described in the paper "Tackling Climate Change with Machine Learning"[1]:
AI has been called the new electricity, given its potential to transform entire industries.[2] 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.[3][4][5]

Electricity systems are responsible for about a quarter of human-caused greenhouse gas emissions each year.[6] 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 CO2-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.

Machine Learning Application Areas

Enabling low-carbon electricity

  • Supply and demand forecasting: 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.
  • Improving power system optimization: Scheduling algorithms on the power grid have trouble handling large quantities of solar, wind, and other time-varying electricity sources. ML can help improve electricity scheduling algorithms, control storage and flexible demand, and design real-time electricity prices that reduce CO2 emissions.
  • Improving system planning: Algorithms for planning new low-carbon energy infrastructure are often large and slow. ML can help speed up or provide proxies for these algorithms.
  • Informing maximum power point tracking: Maximum power point tracking refers to a variety of techniques that aim to maximize the power output of weather-dependent renewable energy generators, such as solar panels and wind turbines. ML can help model attributes of renewable energy systems or actively control these systems (e.g., by modulating wind turbine rotation speed) in order to improve power output.
  • Accelerated science for clean energy technologies: Designing new materials is important for many applications, including energy storage via batteries or solar/chemical fuels. ML can help suggest promising materials to try, thereby speeding up the materials discovery process.
  • Informing nuclear fusion research: Nuclear fusion has the potential to produce safe, carbon-free electricity, but such reactors continue to consume more energy than they produce. While basic science and engineering are still needed, ML can help inform nuclear fusion research in a variety of ways, e.g., by suggesting parameters for physical experiments or modeling the behavior of plasma inside reactors.

Reducing current-system impacts

  • Methane leak detection: In addition to the unavoidable climate impacts of burning fossil fuels, natural gas extraction sites, pipelines, and compressor stations leak methane, a powerful greenhouse gas. ML can help detect and prevent these leaks.
  • Power grid emissions modeling: Reducing the emissions associated with electricity use requires understanding what the emissions on the electric grid actually are at any given moment. ML can help estimate and forecast emissions, and potentially model the uncertainty in these estimates.

General-purpose applications

  • Energy infrastructure mapping: There are many cases in which decision-relevant information about energy infrastructure -- such as the locations and sizes of solar panels, or the location of power transmission and distribution infrastructure -- is not readily available. ML can help map some of this energy infrastructure using satellite imagery.
  • Predictive maintenance and fault detection: Quickly detecting power system faults can help reduce power system waste or improve the utilization of low-carbon energy resources. ML can help detect faults in real time from sensor data, or even forecast them ahead of time to enable preemptive maintenance.
  • State estimation: Many power distribution systems have few sensors, but are increasingly necessary to monitor due to the increase in rooftop solar power. ML can provide algorithms for understanding the state of distribution systems in "low-observability" scenarios where traditional state estimation algorithms may not suffice.
  • Greenhouse gas emissions mapping: While some electricity system operators release publicly-available data on the emissions associated with fossil fuel generators, this data is not available in many cases. ML can help map greenhouse gas emissions using a combination of remote sensing and on-the-ground data.

Background Readings


  • Chapter 7: "Energy Systems" in the IPCC Fifth Assessment Report (2014)[7]: An overview of "issues related to the mitigation of greenhouse gas emissions (GHG) from the energy supply sector." Available here.
  • "Energy Primer: A Handbook for Energy Market Basics" by the U.S. Federal Energy Regulatory Commission (2020)[8]: A primer on wholesale electricity, natural gas, and oil/petroleum markets, as well as energy-related financial markets, in the United States. Available here.


  • "Electric Power Systems: A Conceptual Introduction" (2006)[9]: A textbook "intended to bridge the gap between formal engineering texts and more popularly accessible descriptions of electric power technology."
  • "Power Generation, Operation, and Control" (2013)[10]: A canonical reference on the engineering and economics of electric power systems.
  • "Fundamentals of Power System Economics" (2018)[11]: An introduction to electricity markets.


  • Greening the Grid toolkit: A collection of readings, trainings, and other resources on power grids, renewable energy, energy storage, and electric vehicles. Available here.

Online Courses and Course Materials

  • "Computational Methods for the Smart Grid": "[A]n introduction to recent advances in computational methods applied to sustainable energy and the smart grid... provid[ing] students with a broad background in state-of-the-art computational methods that repeatedly arise in these domains, such as machine learning, optimization, and control." Lecture slides, videos, and assignments available here.
  • "Electric Power Systems" on Coursera: A course covering the "standards and policies of the electric utility industry," including "basic vocabulary used in the business" and an introduction of "the electric power system, from generation of the electricity all the way to the wall plug." Enroll here.

Conferences, Journals, and Professional Societies

Major conferences

Major journals

  • IEEE Transactions on Power Systems: Covers the "requirements, planning, analysis, reliability, operation, and economics of electric generating, transmission, and distribution systems for general industrial, commercial, public, and domestic consumption." Journal website here.
  • IEEE Transactions on Smart Grid: "[A] cross disciplinary and internationally archival journal aimed at disseminating results of research on smart grid that relates to, arises from, or deliberately influences energy generation, transmission, distribution and delivery." Journal website here.

Major professional societies

  • IEEE Power & Energy Society: "[T]he world's largest forum for sharing the latest in technological developments in the electric power industry, for developing standards that guide the development and construction of equipment and systems, and for educating members of the industry and the general public." Website here.

Libraries and Tools

  • PowerTAC: A power system simulation environment, available here.
  • Energy Policy Simulator: A tool to simulate the greenhouse gas emissions effects of various climate and energy policies, available here.
  • Optimal Power Flow (OPF) Sampler Package: A Julia package to generate power grid data samples via optimal power flow methods, available here.
  • Pyiso: A Python client library for data from power grid balancing authorities in the United States, Canada, and Europe. Documentation here.


See the subpages listed above for application-specific datasets.

United States

  • Public Utility Data Liberation (PUDL) Project: A project integrating a variety of United States federal electricity data sources, available here via GitHub.
  • United States ISO/RTO data: Electricity market data (e.g., prices, supply, and demand) are available online for a number of US independent system operator/regional transmission organization regions, namely CAISO, ERCOT, ISO-NE, MISO, NYISO, PJM, and SPP. (These data can also be accessed via the pyiso Python library.)
  • Pecan Street: Disaggregated energy and water data, available here (requires login)
  • United States Environmental Protection Agency's Air Markets Program data: Datasets from the US EPA's emissions trading programs. For instance, the Continuous Emissions Monitoring System dataset (also available via the EPA's FTP site) provides hourly emissions and generation for many fossil fuel generators in the United States. Available here.


  • European electricity market data from electricityMap: Links to (and parsers for) European power system data, available here via GitHub.
  • European electricity market data via Pyiso: The pyiso Python library provides data from European power grid control areas.


  • Rural Electricity Demand in India (REDI) Dataset: "The dataset contains detailed information on electricity demand in rural India. The dataset covers 10,000 households and 2,000 rural enterprises across 200 villages in Bihar, Uttar Pradesh, Odisha, and Rajasthan." Available here.


  • Project Sunroof by Google: Detailed estimates of rooftop solar potential based on sunlight and roof space, available here.


  1. 1.0 1.1 Rolnick, David; Donti, Priya L.; Kaack, Lynn H.; Kochanski, Kelly; Lacoste, Alexandre; Sankaran, Kris; Ross, Andrew Slavin; Milojevic-Dupont, Nikola; Jaques, Natasha; Waldman-Brown, Anna; Luccioni, Alexandra (2019-11-05). "Tackling Climate Change with Machine Learning". arXiv:1906.05433 [cs, stat].
  2. "Andrew Ng: Artificial Intelligence is the New Electricity - YouTube". www.youtube.com.
  3. Ramchurn, Sarvapali D.; Vytelingum, Perukrishnen; Rogers, Alex; Jennings, Nicholas R. (2012). "Putting the 'smarts' into the smart grid". Communications of the ACM. 55 (4): 86–97. doi:10.1145/2133806.2133825. ISSN 0001-0782.
  4. Machine Learning Techniques for Supporting Renewable Energy Generation and Integration: A Survey, Springer International Publishing
  5. "How artificial intelligence will affect the future of energy and climate". Brookings. 2019-01-10.
  6. 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.
  7. Bruckner T., I.A. Bashmakov, 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., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
  8. Federal Energy Regulatory Commission. "Energy Primer: A Handbook of Energy Market Basics." Federal Energy Regulatory Commission: Washington, DC, USA (2020).
  9. Von Meier, Alexandra. "Electric power systems." A Conceptual Introduction (2006).
  10. Wood, A. J., Wollenberg, B. F., & Sheblé, G. B. (2013). Power generation, operation, and control. John Wiley & Sons.
  11. Kirschen, D. S., & Strbac, G. (2018). Fundamentals of power system economics. John Wiley & Sons.