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.
*'''[[DeviceNon-Intrusive usageLoad patternsMonitoring|Non-Intrusive analysisLoad Monitoring (NILM)]]:''' 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.
*'''[[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.
*'''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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* '''Macro-scale [[Macro-scale energy demand assessment in cities|energy demand]] and [[Greenhouse Gas Emissions Detection|GHG emissions]] assessment in cities:''' While some electricity system operators release publicly-available data on energy use and the emissions associated with fossil fuel generators, this data is not available in many cases. ML can help map greenhouse gas emissions using remote sensing and/or on-the-ground data.
* '''[[Identifying building retrofit needs]]:''' For reducing energy use for thermal comfort in buildings, many buildings need to be retrofitted to increase their thermal performance. ML can help pinpoint which buildings and which specific parts of buildings would yield the best performance gains.
* '''[[Designing Low-carbonCarbon urbanUrban formForm|Designing from neighborhoods to large agglomerations|Low-carbon urban form, from neighborhoods toCarbon largeUrban agglomerationsForm]]:''' Urban form, the physical form of cities, has important implications of energy use and GHG emissions, for example sprawled cities can induce mobility demand. ML can help analyze energy use implications of different urban forms, simulate urban development pathways and (re-)designing neighborhoods by finding patterns in urban form data.
 
=== The future of cities ===
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* '''[https://trynthink.github.io/buildingsdatasets/ Datasets from the Department of Energy]''': A list of datasets including Building Operations Data, Building Stock & Energy Data and Developer Resources.
* '''[http://episcope.eu/building-typology/overview The TABULA project]:''' This project provides typologies of buildings in the EU that are relevant to their energy uses.
* '''[https://data.openei.org/submissions/2977 AlphaBuilding - Synthetic Dataset]:''' A synthetic building operation dataset that includes HVAC, lighting, electric loads, occupant counts, environmental parameters, end-use and whole-building energy consumptions at 10-minute intervals.
 
=== City metabolism ===