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Issues of Application of Machine Learning Models for Virtual and Real-Life Buildings

Author

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  • Young Min Kim

    (Department of Convergence Engineering for Future City, Sungkyunkwan University, Suwon, Gyeonggi 16419, Korea)

  • Ki Uhn Ahn

    (School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan University, Suwon, Gyeonggi 16419, Korea)

  • Cheol Soo Park

    (Department of Convergence Engineering for Future City, Sungkyunkwan University, Suwon, Gyeonggi 16419, Korea
    School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan University, Suwon, Gyeonggi 16419, Korea)

Abstract

The current Building Energy Performance Simulation (BEPS) tools are based on first principles. For the correct use of BEPS tools, simulationists should have an in-depth understanding of building physics, numerical methods, control logics of building systems, etc. However, it takes significant time and effort to develop a first principles-based simulation model for existing buildings—mainly due to the laborious process of data gathering, uncertain inputs, model calibration, etc. Rather than resorting to an expert’s effort, a data-driven approach (so-called “inverse” approach) has received growing attention for the simulation of existing buildings. This paper reports a cross-comparison of three popular machine learning models (Artificial Neural Network (ANN), Support Vector Machine (SVM), and Gaussian Process (GP)) for predicting a chiller’s energy consumption in a virtual and a real-life building. The predictions based on the three models are sufficiently accurate compared to the virtual and real measurements. This paper addresses the following issues for the successful development of machine learning models: reproducibility, selection of inputs, training period, outlying data obtained from the building energy management system (BEMS), and validation of the models. From the result of this comparative study, it was found that SVM has a disadvantage in computation time compared to ANN and GP. GP is the most sensitive to a training period among the three models.

Suggested Citation

  • Young Min Kim & Ki Uhn Ahn & Cheol Soo Park, 2016. "Issues of Application of Machine Learning Models for Virtual and Real-Life Buildings," Sustainability, MDPI, vol. 8(6), pages 1-15, June.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:6:p:543-:d:71762
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    References listed on IDEAS

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    1. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
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    Cited by:

    1. Carpenter, Joseph & Woodbury, Keith A. & O'Neill, Zheng, 2018. "Using change-point and Gaussian process models to create baseline energy models in industrial facilities: A comparison," Applied Energy, Elsevier, vol. 213(C), pages 415-425.
    2. Mirian Jiménez-Torres & Catalina Rus-Casas & Lenin Guillermo Lemus-Zúiga & Leocadio Hontoria, 2017. "The Importance of Accurate Solar Data for Designing Solar Photovoltaic Systems—Case Studies in Spain," Sustainability, MDPI, vol. 9(2), pages 1-14, February.
    3. Hyo-Jun Kim & Young-Hum Cho, 2021. "Optimal Control Method of Variable Air Volume Terminal Unit System," Energies, MDPI, vol. 14(22), pages 1-15, November.

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