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Electricity Systems: Difference between revisions

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AI has been called the new electricity, given its potential to transform entire industries. 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.
TODO: add context regarding contribution to emissions, connection to ML, and selected readings
 
Electricity systems are responsible for about a quarter of human-caused greenhouse gas emissions each year. 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.
 
== Selected Readings ==
 
== Data ==
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