Buildings and Cities: Difference between revisions
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*'''[[Energy Demand Forecasting|Forecasting energy loads]]:''' The supply and demand of electric and thermal loads must be forecast ahead of time to inform electricity planning and scheduling. ML can help make these forecasts more accurate, improve temporal resolution, and quantify uncertainty. |
*'''[[Energy Demand Forecasting|Forecasting energy loads]]:''' The supply and demand of electric and thermal loads must be forecast ahead of time to inform electricity planning and scheduling. ML can help make these forecasts more accurate, improve temporal resolution, and quantify uncertainty. |
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*'''[[Device usage patterns analysis]]:''' A better understanding of the own energy consumption can lead to better energy efficiency by changing one's behavior or exchanging inefficient devices. ML can help to disaggregate a household's smart meter data and attribute energy consumption to individual devices for increased transparency. |
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*'''[[Device usage patterns analysis]]:''' |
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*'''[[Controlling HVAC and lighting systems]]:''' |
*'''[[Controlling HVAC and lighting systems]]:''' |
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*'''Fault detection and [[Predictive Maintenance|predictive maintenance]] in building systems:''' |
*'''Fault detection and [[Predictive Maintenance|predictive maintenance]] in building systems:''' |
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