Electricity Systems: Difference between revisions

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.<ref name=":0" /> 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.

 

 

=== Enabling low-carbon electricity ===

* Predictive maintenance

 

 

=== Primers ===

* '''"Nuclear fusion" in Nature Physics''': A collection of articles on the state of nuclear fusion research. Available [https://www.nature.com/collections/bccqhmkbyw/ here].

 

 

*'''"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 [http://www.cs.cmu.edu/~zkolter/course/15-884/ here].

*'''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 [https://www.ieee-pes.org/ here].

 

 

*'''PowerTAC''': A power system simulation environment, available [https://powertac.org/ here].