IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i13p3497-d381187.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/13/3497/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/13/3497/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cui, Borui & Fan, Cheng & Munk, Jeffrey & Mao, Ning & Xiao, Fu & Dong, Jin & Kuruganti, Teja, 2019. "A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses," Applied Energy, Elsevier, vol. 236(C), pages 101-116.
    2. Rubén Pérez-Chacón & José M. Luna-Romera & Alicia Troncoso & Francisco Martínez-Álvarez & José C. Riquelme, 2018. "Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities," Energies, MDPI, vol. 11(3), pages 1-19, March.
    3. Alanne, Kari & Cao, Sunliang, 2017. "Zero-energy hydrogen economy (ZEH2E) for buildings and communities including personal mobility," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 697-711.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anastasios I. Dounis, 2022. "Machine Intelligence in Smart Buildings," Energies, MDPI, vol. 16(1), pages 1-5, December.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Evelina Di Corso & Tania Cerquitelli & Daniele Apiletti, 2018. "METATECH: METeorological Data Analysis for Thermal Energy CHaracterization by Means of Self-Learning Transparent Models," Energies, MDPI, vol. 11(6), pages 1-24, May.
    2. Jefferson Brooks & Ana Rivera & Miguel Chen Austin & Nathalia Tejedor-Flores, 2022. "A Machine Learning-Based Approach to Estimate Energy Flows of the Mangrove Forest: The Case of Panama Bay," Sustainability, MDPI, vol. 15(1), pages 1-20, December.
    3. Rongheng Lin & Budan Wu & Yun Su, 2018. "An Adaptive Weighted Pearson Similarity Measurement Method for Load Curve Clustering," Energies, MDPI, vol. 11(9), pages 1-17, September.
    4. Younghoon Seo & Donghyun Lim & Woongbee Son & Yeongmin Kwon & Junghwa Kim & Hyungjoo Kim, 2020. "Deriving Mobility Service Policy Issues Based on Text Mining: A Case Study of Gyeonggi Province in South Korea," Sustainability, MDPI, vol. 12(24), pages 1-20, December.
    5. Ewa Chodakowska & Joanicjusz Nazarko & Łukasz Nazarko, 2021. "ARIMA Models in Electrical Load Forecasting and Their Robustness to Noise," Energies, MDPI, vol. 14(23), pages 1-22, November.
    6. Fan Yang & Qian Mao, 2023. "Auto-Evaluation Model for the Prediction of Building Energy Consumption That Combines Modified Kalman Filtering and Long Short-Term Memory," Sustainability, MDPI, vol. 15(22), pages 1-16, November.
    7. Dana-Mihaela Petroșanu & George Căruțașu & Nicoleta Luminița Căruțașu & Alexandru Pîrjan, 2019. "A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal B," Energies, MDPI, vol. 12(24), pages 1-64, December.
    8. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    9. Cezary Stępniak & Dorota Jelonek & Magdalena Wyrwicka & Iwona Chomiak-Orsa, 2021. "Integration of the Infrastructure of Systems Used in Smart Cities for the Planning of Transport and Communication Systems in Cities," Energies, MDPI, vol. 14(11), pages 1-19, May.
    10. Paweł Dymora & Mirosław Mazurek & Bartosz Sudek, 2021. "Comparative Analysis of Selected Open-Source Solutions for Traffic Balancing in Server Infrastructures Providing WWW Service," Energies, MDPI, vol. 14(22), pages 1-23, November.
    11. Li, Yanfei & O'Neill, Zheng & Zhang, Liang & Chen, Jianli & Im, Piljae & DeGraw, Jason, 2021. "Grey-box modeling and application for building energy simulations - A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    12. Wei, Ziqing & Ren, Fukang & Zhu, Yikang & Yue, Bao & Ding, Yunxiao & Zheng, Chunyuan & Li, Bin & Zhai, Xiaoqiang, 2022. "Data-driven two-step identification of building thermal characteristics: A case study of office building," Applied Energy, Elsevier, vol. 326(C).
    13. Zhou, Yuekuan & Cao, Sunliang & Hensen, Jan L.M. & Lund, Peter D., 2019. "Energy integration and interaction between buildings and vehicles: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    14. Mohammed Olama & Jin Dong & Isha Sharma & Yaosuo Xue & Teja Kuruganti, 2020. "Frequency Analysis of Solar PV Power to Enable Optimal Building Load Control," Energies, MDPI, vol. 13(18), pages 1-18, September.
    15. Simona Vasilica Oprea & Adela Bâra, 2019. "Big Data Solutions for Efficient Operation of Microgrids," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 266-271, August.
    16. Dewangan, Chaman Lal & Vijayan, Vineeth & Shukla, Devesh & Chakrabarti, S. & Singh, S.N. & Sharma, Ankush & Hossain, Md. Alamgir, 2023. "An improved decentralized scheme for incentive-based demand response from residential customers," Energy, Elsevier, vol. 284(C).
    17. Ana De Las Heras & Amalia Luque-Sendra & Francisco Zamora-Polo, 2020. "Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era," Sustainability, MDPI, vol. 12(22), pages 1-25, November.
    18. Huang, Sen & Lin, Yashen & Chinde, Venkatesh & Ma, Xu & Lian, Jianming, 2021. "Simulation-based performance evaluation of model predictive control for building energy systems," Applied Energy, Elsevier, vol. 281(C).
    19. Velander, Taylor B. & Joyce, Michael J. & Kujawa, Angela M. & Sanders, Robert L. & Keenlance, Paul W. & Moen, Ron A., 2023. "A dynamic thermal model for predicting internal temperature of tree cavities and nest boxes," Ecological Modelling, Elsevier, vol. 478(C).
    20. Sana Mujeeb & Nadeem Javaid & Manzoor Ilahi & Zahid Wadud & Farruh Ishmanov & Muhammad Khalil Afzal, 2019. "Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities," Sustainability, MDPI, vol. 11(4), pages 1-29, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3497-:d:381187. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.