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Buildings Energy Efficiency Analysis and Classification Using Various Machine Learning Technique Classifiers

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  • César Benavente-Peces

    (ETS Ingeniería y Sistemas de Telecomunicación, Universidad Politécnica de Madrid, Calle de Nikola Tesla sn, 28031 Madrid, Spain)

  • Nisrine Ibadah

    (LRIT Laboratory, Associated Unit to CNRST (URAC 29), IT Rabat Center, Faculty of Sciences, Mohammed V University, Rabat 1014 RP, Morocco)

Abstract

Energy efficiency is a major concern to achieve sustainability in modern society. Smart cities sustainability depends on the availability of energy-efficient infrastructures and services. Buildings compose most of the city, and they are responsible for most of the energy consumption and emissions to the atmosphere (40%). Smart cities need smart buildings to achieve sustainability goals. Building’s thermal modeling is essential to face the energy efficiency race. In this paper, we show how ICT and data science technologies and techniques can be applied to evaluate the energy efficiency of buildings. In concrete, we apply machine learning techniques to classify buildings based on their energy efficiency. Particularly, our focus is on single-family buildings in residential areas. Along this paper, we demonstrate the capabilities of machine learning techniques to classify buildings depending on their energy efficiency. Moreover, we analyze and compare the performance of different classifiers. Furthermore, we introduce new parameters which have some impact on the buildings thermal modeling, especially those concerning the environment where the building is located. We also make an insight on ICT and remark the growing relevance in data acquisition and monitoring of relevant parameters by using wireless sensor networks. It is worthy to remark the need for an appropriate and reliable dataset to achieve the best results. Moreover, we demonstrate that reliable classification is feasible with a few featured parameters.

Suggested Citation

  • César Benavente-Peces & Nisrine Ibadah, 2020. "Buildings Energy Efficiency Analysis and Classification Using Various Machine Learning Technique Classifiers," Energies, MDPI, vol. 13(13), pages 1-24, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3497-:d:381187
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    References listed on IDEAS

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    Cited by:

    1. Seung Yeoun Choi & Sean Hay Kim, 2021. "Knowledge Acquisition and Representation for High-Performance Building Design: A Review for Defining Requirements for Developing a Design Expert System," Sustainability, MDPI, vol. 13(9), pages 1-36, April.
    2. Anastasios I. Dounis, 2022. "Machine Intelligence in Smart Buildings," Energies, MDPI, vol. 16(1), pages 1-5, December.
    3. Hernández, José L. & de Miguel, Ignacio & Vélez, Fredy & Vasallo, Ali, 2024. "Challenges and opportunities in European smart buildings energy management: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    4. Xunzhi Yin & Jiaqi Yu & Qi Dong & Yongheng Jia & Cheng Sun, 2020. "Energy Sustainability of Rural Residential Buildings with Bio-Based Building Fabric in Northeast China," Energies, MDPI, vol. 13(21), pages 1-14, November.
    5. Chunwang Xiaogeng LiRen & Xiaojun Ma & Fuxiang Chen & Zhicheng Yang & Sandeep Panchal, 2022. "Simulation and inspection of fault arc in building energy-saving distribution system," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 331-339, March.
    6. George M. Stavrakakis & Dimitris Bakirtzis & Korina-Konstantina Drakaki & Sofia Yfanti & Dimitris Al. Katsaprakakis & Konstantinos Braimakis & Panagiotis Langouranis & Konstantinos Terzis & Panagiotis, 2024. "Application of the Typology Approach for Energy Renovation Planning of Public Buildings’ Stocks at the Local Level: A Case Study in Greece," Energies, MDPI, vol. 17(3), pages 1-30, January.
    7. Yong-Joon Jun & Seung-ho Ahn & Kyung-Soon Park, 2021. "Improvement Effect of Green Remodeling and Building Value Assessment Criteria for Aging Public Buildings," Energies, MDPI, vol. 14(4), pages 1-28, February.

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