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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*'''[[Non-Intrusive Load Monitoring]]:''' 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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*'''[[Controlling HVAC and lighting systems]]:''' Current building energy management systems are manually designed by human operators, which leads to energy inefficient operations. ML can help develop predictive models of building energy systems, leading to efficient building operation via advanced model-based optimal control methods. |
*'''[[Controlling HVAC and lighting systems]]:''' Current building energy management systems are manually designed by human operators, which leads to energy inefficient operations. ML can help develop predictive models of building energy systems, leading to efficient building operation via advanced model-based optimal control methods. |
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*'''Fault detection and [[Predictive Maintenance|predictive maintenance]] in building systems:''' Buildings are embedded with complex engineering systems in dynamic interplay. Component's malfunctions are a constant threat to the building operations economics and comfort and safety of human occupants. ML can help to transfer from reactive maintenance to predictive maintenance and cut the maintenance cost by prolonging the remaining useful life of building engineering systems. |
*'''Fault detection and [[Predictive Maintenance|predictive maintenance]] in building systems:''' Buildings are embedded with complex engineering systems in dynamic interplay. Component's malfunctions are a constant threat to the building operations economics and comfort and safety of human occupants. ML can help to transfer from reactive maintenance to predictive maintenance and cut the maintenance cost by prolonging the remaining useful life of building engineering systems. |
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