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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.
*'''[[
*'''[[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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*'''[[AI-guided building design and planning]]:''' Current building designs are drawn by the joined hands of the architect, mechanical, electrical, and control engineers using various computer-aided design tools. ML can help to navigate and optimize complex design landscapes, often balancing conflicting requirements such as energy efficiency, comfort, and cost.
*'''[[Sector-coupled districts and district heating systems]]''': To achieve decarbonization across the heating, electricity, and mobility sectors, they are increasingly coupled within districts in a joint spatial and organizational context. ML can help by providing surrogate models of thermal processes and quantify uncertainties of loads, supply, and mobility behavior.
*'''[[Surrogate
=== Urban planning ===
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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-
=== The future of cities ===
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=== Urban transportation ===
''Main article: [[Transportation]]''
Urban transportation is of high relevance to mitigating climate change in cities, as mobility within cities represents a large share of the total final energy use in the transportation sector (40% in 2010<ref>{{Cite book|title=Policy Pathways: A Tale of Renewed Cities. International Energy Agency|last=IEA|first=|publisher=|year=2013|isbn=|location=Paris|pages=98}}</ref>). Transportation topics are treated as a separate [[Transportation|section]] of the wiki, where areas of particular relevance include:▼
▲Urban transportation is of high relevance to mitigating climate change in cities, as mobility within cities represents a large share of the total final energy use in the transportation sector (40% in 2010<ref>{{Cite book|title=Policy Pathways: A Tale of Renewed Cities. International Energy Agency|last=IEA|first=|publisher=|year=2013|isbn=|location=Paris|pages=98}}</ref>). Transportation topics are treated as a separate [[Transportation|section]] of the wiki, where areas of particular relevance include:
* '''[[
* '''[[
* '''[[Electric vehicle charging infrastructure]]'''
* '''[[Fostering urban cycling]]'''
* '''[[Supporting public transportation network expansion]]'''
== Background Readings ==
=== Relevant IPCC chapters ===
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==Libraries and Tools==
{{SectionStub}}
==Data==
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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 ===
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