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Electricity Systems: Difference between revisions

(Added Energy Disaggregation to general-purpose applications.)
*[[Power System State Estimation|'''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 Detection|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.
*'''[[EnergyNon-Intrusive Load Monitoring|Non-Intrusive Load Monitoring Disaggregation(NILM)]]''': ML can be used to disaggregate the net-load measurements into their individual components. It finds application in disaggregating native load and PV generation measurements from the net-load measurements obtained at the feeder-lever or at the residential-level. Another aspect of energy disaggregation is non intrusive load monitoring, wherein the load measurement is disaggregated into different components representing the consumption of different household electric appliances.
== Background Readings ==
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