Machine Learning for Building Energy Modeling

Debaditya Chakraborty () and Hakan Başağaoğlu ()
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Debaditya Chakraborty: University of Texas at San Antonio
Hakan Başağaoğlu: Edwards Aquifer Authority

A chapter in Handbook of Smart Energy Systems, 2023, pp 667-688 from Springer

Abstract: Abstract Machine learning (ML), a subset of artificial intelligence (AI), has emerged as a versatile tool for the design, analysis, and control of efficient and climate change-resilient smart energy systems in buildings. This chapter presents an overview of building energy modeling (BEM) using ML models and its implementation for the projection of building energy demand under future climate change scenarios generated by global circulation models (GCMs). It also provides a step-by-step practical guide for the development and use of ML-based BEM to project the deviation in future energy requirements in a prototype residential building due to the impact of climate change. This chapter concludes with the discussion on future directions in applied ML for BEM and long-term projections of building energy consumption with the particular emphasis on the use of explainable ML models. ML-based BEMs can potentially be used for scenario-specific life cycle cost-benefit analysis during the design or retrofitting stages and facilitate both short- and long-term decision-making when integrated with the data from smart energy systems.

Keywords: Building energy consumption; Artificial intelligence; Machine learning; Future climate change scenario; Future energy consumption (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-97940-9_28

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DOI: 10.1007/978-3-030-97940-9_28

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