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Electricity Systems: Difference between revisions

proposal to add problem areas
(change entry formatting)
(proposal to add problem areas)
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.
== Problem areas ==
=== Enabling low-carbon electricity ===
* Supply and demand forecasting
* Improving scheduling and flexible demand
* Accelerated materials science for clean energy technologies
* Optimizing variable generators
* System planning for clean energy technologies
* Predictive maintenance and fault detection
* Accelerating nuclear fusion science
=== Reducing current-system impacts ===
* Methane leak detection
* Modeling emissions
=== Other ===
* Data collection via remote sensing
* Predictive maintenance
== Background readings ==
* '''[[Remote Sensing Datasets|Satellite imagery datasets]]:''' Datasets that may be useful for applications such as power plant emissions detection, power grid mapping, solar panel mapping, etc.
*'''Project Sunroof by Google''': Detailed estimates of rooftop solar potential based on sunlight and roof space, available [https://www.google.com/get/sunroof/data-explorer/ here].
== Selected problems ==
''Under construction''
== References ==
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