Buildings and Cities: Difference between revisions

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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.
*'''[[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.
*'''[[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 modelling]]''': Building energy simulation (BES) programs are software tools that simulate the complex physics of buildings and are key enabling tools for R&D in the building's domain. However, detailed BES models are notoriously difficult to design, tune and typically have high computational demands. ML, in conjunction with physics, can help to build accurate yet computationally efficient surrogate models for faster simulations.
*'''[[Surrogate modeling]]''': Building energy simulation (BES) programs are software tools that simulate the complex physics of buildings and are key enabling tools for R&D in the building's domain. However, detailed BES models are notoriously difficult to design, tune and typically have high computational demands. ML, in conjunction with physics, can help to build accurate yet computationally efficient surrogate models for faster simulations.


=== Urban planning ===
=== Urban planning ===
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* '''[[Understanding mobility patterns]]'''
* '''[[Understanding mobility patterns]]'''
* '''[[Enabling low-carbon shared mobility]]'''
* '''[[Enabling low-carbon shared mobility]]'''
* '''[[Electric vehicle charging infrastructure]]'''
* '''Electric vehicle charging infrastructure'''
* '''[[Fostering urban cycling]]'''
* '''Fostering urban cycling'''
* '''[[Supporting public transportation network expansion]]'''
* '''Supporting public transportation network expansion'''
== Background Readings ==
== Background Readings ==
=== Relevant IPCC chapters ===
=== Relevant IPCC chapters ===
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