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Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric Analysis

Author

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  • Ahmed Abdelaziz

    (Nova Information Management School, Universidade Nova de Lisboa, 1070-312 Lisboa, Portugal
    Information System Department, Higher Technological Institute, HTI, Cairo 44629, Egypt)

  • Vitor Santos

    (Nova Information Management School, Universidade Nova de Lisboa, 1070-312 Lisboa, Portugal)

  • Miguel Sales Dias

    (Department of Information Science and Technology, Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, 1649-026 Lisboa, Portugal)

Abstract

The high level of energy consumption of buildings is significantly influencing occupant behavior changes towards improved energy efficiency. This paper introduces a systematic literature review with two objectives: to understand the more relevant factors affecting energy consumption of buildings and to find the best intelligent computing (IC) methods capable of classifying and predicting energy consumption of different types of buildings. Adopting the PRISMA method, the paper analyzed 822 manuscripts from 2013 to 2020 and focused on 106, based on title and abstract screening and on manuscripts with experiments. A text mining process and a bibliometric map tool (VOS viewer) were adopted to find the most used terms and their relationships, in the energy and IC domains. Our approach shows that the terms “consumption,” “residential,” and “electricity” are the more relevant terms in the energy domain, in terms of the ratio of important terms (TITs), whereas “cluster” is the more commonly used term in the IC domain. The paper also shows that there are strong relations between “Residential Energy Consumption” and “Electricity Consumption,” “Heating” and “Climate. Finally, we checked and analyzed 41 manuscripts in detail, summarized their major contributions, and identified several research gaps that provide hints for further research.

Suggested Citation

  • Ahmed Abdelaziz & Vitor Santos & Miguel Sales Dias, 2021. "Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric Analysis," Energies, MDPI, vol. 14(22), pages 1-31, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7810-:d:684870
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    References listed on IDEAS

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    1. Anna Pamula & Zbigniew Gontar & Beata Gontar & Tetiana Fesenko, 2023. "Latent Dirichlet Allocation in Public Procurement Documents Analysis for Determining Energy Efficiency Issues in Construction Works at Polish Universities," Energies, MDPI, vol. 16(12), pages 1-23, June.
    2. Feng Wu & Wanqiang Xu & Chaoran Lin & Yanwei Zhang, 2022. "Knowledge Trajectories on Public Crisis Management Research from Massive Literature Text Using Topic-Clustered Evolution Extraction," Mathematics, MDPI, vol. 10(12), pages 1-18, June.
    3. Bianca Goia & Tudor Cioara & Ionut Anghel, 2022. "Virtual Power Plant Optimization in Smart Grids: A Narrative Review," Future Internet, MDPI, vol. 14(5), pages 1-22, April.
    4. Alberto Barbaresi & Mattia Ceccarelli & Giulia Menichetti & Daniele Torreggiani & Patrizia Tassinari & Marco Bovo, 2022. "Application of Machine Learning Models for Fast and Accurate Predictions of Building Energy Need," Energies, MDPI, vol. 15(4), pages 1-16, February.

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