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Machine Learning for Building Energy Modeling

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Handbook of Smart Energy Systems

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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.

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Authors and Affiliations

  1. University of Texas at San Antonio, San Antonio, TX, USA

    Debaditya Chakraborty

  2. Edwards Aquifer Authority, San Antonio, TX, USA

    Hakan Başağaoğlu

Authors
  1. Debaditya Chakraborty
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  2. Hakan Başağaoğlu
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Correspondence to Debaditya Chakraborty .

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Editors and Affiliations

  1. Information Technology and Decision Sciences, University of North Texas, Denton, TX, USA

    Michel Fathi

  2. Centre for research on Risks and Crises (CRC), Mines Paris-PSL University, Sophia Antipolis, France

    Enrico Zio

  3. Industrial & Systems Engineering, University of Florida, GAINESVILLE, FL, USA

    Panos M. Pardalos

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Chakraborty, D., Başağaoğlu, H. (2023). Machine Learning for Building Energy Modeling. In: Fathi, M., Zio, E., Pardalos, P.M. (eds) Handbook of Smart Energy Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-97940-9_28

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