IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i19p6042-d641088.html
   My bibliography  Save this article

Smart Building Energy Inefficiencies Detection through Time Series Analysis and Unsupervised Machine Learning

Author

Listed:
  • Hanaa Talei

    (Smart Systems Lab, ENSIAS, Mohammed V University in Rabat, Rabat 10000, Morocco
    School of Science and Engineering, Al Akhawayn University, Ifrane 53000, Morocco)

  • Driss Benhaddou

    (Department of Computer Engineering Technology, University of Houston, Houston, TX 77204, USA)

  • Carlos Gamarra

    (Houston Advanced Research Center, Houston, TX 77381, USA)

  • Houda Benbrahim

    (Department of Computer Science and Decision Support, Ecole Nationale Supérieure d’Informatique et d’Analyse des Systèmes, Rabat 10112, Morocco)

  • Mohamed Essaaidi

    (College of Engineering, Ecole Nationale Supérieure d’Informatique et d’Analyse des Systèmes, Rabat 10112, Morocco)

Abstract

The climate of Houston, classified as a humid subtropical climate with tropical influences, makes the heating, ventilation, and air conditioning (HVAC) systems the largest electricity consumers in buildings. HVAC systems in commercial buildings are usually operated by a centralized control system and/or an energy management system based on a fixed schedule and scheduled control of a zone setpoint, which is not appropriate for many buildings with changing occupancy rates. Lately, as part of energy efficiency analysis, attention has focused on collecting and analyzing smart meters and building-related data, as well as applying supervised learning techniques, to propose new strategies to operate HVAC systems and reduce energy consumption. On the other hand, unsupervised learning techniques have been used to study the consumption information and profile characterization of different buildings after cluster analysis is performed. This paper adopts a different approach by revealing the power of unsupervised learning to cluster data and unveiling hidden patterns. In this study, we also identify energy inefficiencies after exploring the cluster results of a single building’s HVAC consumption data and building usage data as part of the energy efficiency analysis. Time series analysis and the K-means clustering algorithm are successfully applied to identify new energy-saving opportunities in a highly efficient office building located in the Houston area (TX, USA). The paper uses 1-year data from a highly efficient Leadership in Energy and Environment Design (LEED)-, Energy Star-, and Net Zero-certified building, showing a potential energy savings of 6% using the K-means algorithm. The results show that clustering is instrumental in helping building managers identify potential additional energy savings.

Suggested Citation

  • Hanaa Talei & Driss Benhaddou & Carlos Gamarra & Houda Benbrahim & Mohamed Essaaidi, 2021. "Smart Building Energy Inefficiencies Detection through Time Series Analysis and Unsupervised Machine Learning," Energies, MDPI, vol. 14(19), pages 1-21, September.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6042-:d:641088
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/19/6042/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/19/6042/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    2. Li, Guannan & Hu, Yunpeng & Chen, Huanxin & Li, Haorong & Hu, Min & Guo, Yabin & Liu, Jiangyan & Sun, Shaobo & Sun, Miao, 2017. "Data partitioning and association mining for identifying VRF energy consumption patterns under various part loads and refrigerant charge conditions," Applied Energy, Elsevier, vol. 185(P1), pages 846-861.
    3. Huyen Do & Kristen S. Cetin, 2019. "Data-Driven Evaluation of Residential HVAC System Efficiency Using Energy and Environmental Data," Energies, MDPI, vol. 12(1), pages 1-15, January.
    4. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
    5. Anderson, Austin & Rezaie, Behnaz, 2019. "Geothermal technology: Trends and potential role in a sustainable future," Applied Energy, Elsevier, vol. 248(C), pages 18-34.
    6. Li, Wenzhuo & Koo, Choongwan & Hong, Taehoon & Oh, Jeongyoon & Cha, Seung Hyun & Wang, Shengwei, 2020. "A novel operation approach for the energy efficiency improvement of the HVAC system in office spaces through real-time big data analytics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    7. Bishnu Nepal & Motoi Yamaha & Hiroya Sahashi & Aya Yokoe, 2019. "Analysis of Building Electricity Use Pattern Using K-Means Clustering Algorithm by Determination of Better Initial Centroids and Number of Clusters," Energies, MDPI, vol. 12(12), pages 1-17, June.
    8. Jung, Wooyoung & Jazizadeh, Farrokh, 2019. "Human-in-the-loop HVAC operations: A quantitative review on occupancy, comfort, and energy-efficiency dimensions," Applied Energy, Elsevier, vol. 239(C), pages 1471-1508.
    9. Gaitani, N. & Lehmann, C. & Santamouris, M. & Mihalakakou, G. & Patargias, P., 2010. "Using principal component and cluster analysis in the heating evaluation of the school building sector," Applied Energy, Elsevier, vol. 87(6), pages 2079-2086, June.
    10. Alexander Tureczek & Per Sieverts Nielsen & Henrik Madsen, 2018. "Electricity Consumption Clustering Using Smart Meter Data," Energies, MDPI, vol. 11(4), pages 1-18, April.
    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. Bartlomiej Kawa & Piotr Borkowski, 2023. "Integration of Machine Learning Solutions in the Building Automation System," Energies, MDPI, vol. 16(11), pages 1-18, June.
    2. Anastasios I. Dounis, 2022. "Machine Intelligence in Smart Buildings," Energies, MDPI, vol. 16(1), pages 1-5, December.
    3. António Gomes Martins & Luís Pires Neves & José Luís Sousa, 2023. "Electricity Demand Side Management," Energies, MDPI, vol. 16(16), pages 1-3, August.
    4. Yuriy Leonidovich Zhukovskiy & Margarita Sergeevna Kovalchuk & Daria Evgenievna Batueva & Nikita Dmitrievich Senchilo, 2021. "Development of an Algorithm for Regulating the Load Schedule of Educational Institutions Based on the Forecast of Electric Consumption within the Framework of Application of the Demand Response," Sustainability, MDPI, vol. 13(24), pages 1-26, December.
    5. 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.

    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. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.
    2. 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.
    3. Alperen Yayla & Kübra Sultan Świerczewska & Mahmut Kaya & Bahadır Karaca & Yusuf Arayici & Yunus Emre Ayözen & Onur Behzat Tokdemir, 2022. "Artificial Intelligence (AI)-Based Occupant-Centric Heating Ventilation and Air Conditioning (HVAC) Control System for Multi-Zone Commercial Buildings," Sustainability, MDPI, vol. 14(23), pages 1-29, December.
    4. Gianluca Serale & Luca Gnoli & Emanuele Giraudo & Enrico Fabrizio, 2021. "A Supervisory Control Strategy for Improving Energy Efficiency of Artificial Lighting Systems in Greenhouses," Energies, MDPI, vol. 14(1), pages 1-19, January.
    5. 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.
    6. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    7. Mir M. Ali & Kheir Al-Kodmany & Paul J. Armstrong, 2023. "Energy Efficiency of Tall Buildings: A Global Snapshot of Innovative Design," Energies, MDPI, vol. 16(4), pages 1-23, February.
    8. Wenxiao Chu & Maria Vicidomini & Francesco Calise & Neven Duić & Poul Alborg Østergaard & Qiuwang Wang & Maria da Graça Carvalho, 2022. "Recent Advances in Low-Carbon and Sustainable, Efficient Technology: Strategies and Applications," Energies, MDPI, vol. 15(8), pages 1-30, April.
    9. Kurnia, Jundika C. & Putra, Zulfan A. & Muraza, Oki & Ghoreishi-Madiseh, Seyed Ali & Sasmito, Agus P., 2021. "Numerical evaluation, process design and techno-economic analysis of geothermal energy extraction from abandoned oil wells in Malaysia," Renewable Energy, Elsevier, vol. 175(C), pages 868-879.
    10. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
    11. Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    12. 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.
    13. Rongjiang Ma & Xianlin Wang & Ming Shan & Nanyang Yu & Shen Yang, 2020. "Recognition of Variable-Speed Equipment in an Air-Conditioning System Using Numerical Analysis of Energy-Consumption Data," Energies, MDPI, vol. 13(18), pages 1-14, September.
    14. Yang, Shiyu & Wan, Man Pun & Ng, Bing Feng & Dubey, Swapnil & Henze, Gregor P. & Chen, Wanyu & Baskaran, Krishnamoorthy, 2020. "Experimental study of model predictive control for an air-conditioning system with dedicated outdoor air system," Applied Energy, Elsevier, vol. 257(C).
    15. Liangwen Yan & Fengfeng Qian & Wei Li, 2018. "Research on Key Parameters Operation Range of Central Air Conditioning Based on Binary K-Means and Apriori Algorithm," Energies, MDPI, vol. 12(1), pages 1-13, December.
    16. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2022. "Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention," Applied Energy, Elsevier, vol. 321(C).
    17. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
    18. Cui, Can & Zhang, Xin & Cai, Wenjian, 2020. "An energy-saving oriented air balancing method for demand controlled ventilation systems with branch and black-box model," Applied Energy, Elsevier, vol. 264(C).
    19. Wang, Gaosheng & Song, Xianzhi & Shi, Yu & Yang, Ruiyue & Yulong, Feixue & Zheng, Rui & Li, Jiacheng, 2021. "Heat extraction analysis of a novel multilateral-well coaxial closed-loop geothermal system," Renewable Energy, Elsevier, vol. 163(C), pages 974-986.
    20. Rongjiang Ma & Shen Yang & Xianlin Wang & Xi-Cheng Wang & Ming Shan & Nanyang Yu & Xudong Yang, 2020. "Systematic Method for the Energy-Saving Potential Calculation of Air-Conditioning Systems via Data Mining. Part I: Methodology," Energies, MDPI, vol. 14(1), pages 1-15, December.

    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:14:y:2021:i:19:p:6042-:d:641088. 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.