Buildings and Cities: Difference between revisions
Machine Learning Application Areas -> Added list and sub-pages 'urban planning'; added preliminary list 'optimizing building''; added list 'the future of cities' and 'urban transportation'
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(Machine Learning Application Areas -> Added list and sub-pages 'urban planning'; added preliminary list 'optimizing building''; added list 'the future of cities' and 'urban transportation') |
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=== Optimizing buildings ===
* Energy demand forecasting
* Predictive maintenance and fault-detection in HVAC systems
* Demand response
* ''(under construction)''
=== Urban planning ===
* '''Mapping [[Built-up infrastructure mapping|built-up]] and [[Energy Infrastructure Mapping|energy]] infrastructure:''' Decarbonizing the building and urban transportation sectors requires accurate mapping of the existing infrastructure but there are large data gaps. ML can help generate such data from remote sensing and existing maps.
* '''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.
* '''[[Low-carbon urban form from neighborhoods to large agglomerations|Low-carbon urban form, from neighborhoods to large agglomerations]]:''' 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 ===
*'''Efficient sensing:'''
*'''Causal inference of policy interventions:'''
*'''Assessing urban climate:'''
*'''Enabling nature-based solutions in cities:'''
*'''Predictive maintenance of public infrastructure:'''
=== Urban 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:
* '''Understanding mobility patterns'''
* '''Enabling low-carbon shared mobility'''
* '''Electric vehicle charging infrastructure'''
* '''Fostering urban cycling'''
* '''Supporting public transportation network expansion'''
<br />
== Background Readings ==
=== Relevant IPCC chapters ===
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