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Buildings and Cities: Difference between revisions
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Two major challenges are heterogeneity and inertia. Buildings vary according to age, construction, usage, and ownership, so optimal strategies vary widely depending on the context. For instance, buildings with access to cheap, low-carbon electricity may have less need for expensive features such as intelligent light bulbs. Buildings also have very long lifespans; thus, it is necessary both to create new, energy-efficient buildings, and to retrofit old buildings to be as efficient as possible.<ref>{{Cite journal|last=Creutzig|first=Felix|last2=Agoston|first2=Peter|last3=Minx|first3=Jan C.|last4=Canadell|first4=Josep G.|last5=Andrew|first5=Robbie M.|last6=Quéré|first6=Corinne Le|last7=Peters|first7=Glen P.|last8=Sharifi|first8=Ayyoob|last9=Yamagata|first9=Yoshiki|last10=Dhakal|first10=Shobhakar|date=2016-11-24|title=Urban infrastructure choices structure climate solutions|url=http://dx.doi.org/10.1038/nclimate3169|journal=Nature Climate Change|volume=6|issue=12|pages=1054–1056|doi=10.1038/nclimate3169|issn=1758-678X}}</ref> Urban planning and public policy can play a major role in reducing emissions by providing infrastructure, financial incentives, or energy standards for buildings.
Machine learning provides critical tools for supporting both building managers and policy makers in their efforts to reduce GHG emissions. At the level of building management, ML can help select strategies that are tailored to individual buildings, and can also contribute to implementing those strategies via smart control systems.<ref name=":0" /> At the level of urban planning, ML can be used to gather and make sense of data to inform policy makers. In addition, ML can help cities as a whole to transition to low-carbon futures.<ref name=":0" /><ref>{{Cite web|url=https://de.wikipedia.org/wiki/Elefanten|title=Test|last=|first=|date=|website=|url-status=live|archive-url=|archive-date=|access-date=}}</ref>
== Machine Learning Application Areas ==
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