A real-time predictive software prototype for simulating urban-scale energy consumption based on surrogate models. Issue 4 (28th November 2021)

• Record Type: Journal Article
• Authors: Rahimian, Mina; Duarte, Jose Pinto; Iulo, Lisa Domenica
• Is Part Of: AI EDAM. Volume 35:Issue 4(2021)
• Page Start: 353
• Page End: 368
• Publication Date: 2021-11-28
• Subjects: Artificial neural networks -- simulation -- surrogate modeling -- urban-scale energy modeling; Engineering design -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; 620.00420285
• DOI: 10.1017/S0890060421000184
• Languages: English
• ISSNs: 0890-0604

Abstract

Abstract: This paper discusses the development of an experimental software prototype that uses surrogate models for predicting the monthly energy consumption of urban-scale community design scenarios in real time. The surrogate models were prepared by training artificial neural networks on datasets of urban form and monthly energy consumption values of all zip codes in San Diego county. The surrogate models were then used as the simulation engine of a generative urban design tool, which generates hypothetical communities in San Diego following the county's existing urban typologies and then estimates the monthly energy consumption value of each generated design option. This paper and developed software prototype is part of a larger research project that evaluates the energy performance of community microgrids via their urban spatial configurations. This prototype takes the first step in introducing a new set of tools for architects and urban designers with the goal of engaging them in the development process of community microgrids.