IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i22p8543-d973310.html
   My bibliography  Save this article

Prediction of Building Electricity Consumption Based on Joinpoint−Multiple Linear Regression

Author

Listed:
  • Hao Yang

    (School of Architecture, Huaqiao University, Xiamen 361021, China
    Xiamen Key Laboratory of Ecological Building Construction, Xiamen 361021, China)

  • Maoyu Ran

    (School of Architecture, Huaqiao University, Xiamen 361021, China
    Xiamen Key Laboratory of Ecological Building Construction, Xiamen 361021, China)

  • Chaoqun Zhuang

    (Data-Centric Engineering, The Alan Turing Institute, The British Library, 96 Euston Road, London NW1 2DB, UK)

Abstract

Reliable energy consumption forecasting is essential for building energy efficiency improvement. Regression models are simple and effective for data analysis, but their practical applications are limited by the low prediction accuracy under ever-changing building operation conditions. To address this challenge, a Joinpoint–Multiple Linear Regression (JP–MLR) model is proposed in this study, based on the investigation of the daily electricity usage data of 8 apartment complexes located within a university in Xiamen, China. The univariate model is first built using the Joinpoint Regression (JPR) method, and then the remaining residuals are evaluated using the Multiple Linear Regression (MLR) method. The model contains six explanatory variables, three of which are continuous (mean outdoor air temperature, mean relative humidity, and temperature amplitude) and three of which are categorical (gender, holiday index, and sunny day index). The performance of the JP–MLR model is compared to that of the other four data-driven algorithm models: JPR, MLR, Back Propagation (BP) neural network, and Random Forest (RF). The JP–MLR model, which has an R 2 value of 95.77%, has superior prediction performance when compared to the traditional regression-based JPR model and MLR model. It also performs better than the machine learning-based BP model and is identical to that of the RF model. This demonstrates that the JP–MLR model has satisfactory prediction performance and offers building operators an effective prediction tool. The proposed research method also provides also serves as a reference for electricity consumption analysis in other types of buildings.

Suggested Citation

  • Hao Yang & Maoyu Ran & Chaoqun Zhuang, 2022. "Prediction of Building Electricity Consumption Based on Joinpoint−Multiple Linear Regression," Energies, MDPI, vol. 15(22), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8543-:d:973310
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/22/8543/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/22/8543/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jiayu Li & Bohong Zheng & Komi Bernard Bedra & Zhe Li & Xiao Chen, 2021. "Evaluating the Effect of Window-to-Wall Ratios on Cooling-Energy Demand on a Typical Summer Day," IJERPH, MDPI, vol. 18(16), pages 1-13, August.
    2. Soo-Jeong Kim & Doo-Yong Park, 2022. "Study on the Variation in Heating Energy Based on Energy Consumption from the District Heating System, Simulations and Pattern Analysis," Energies, MDPI, vol. 15(11), pages 1-21, May.
    3. Sanchari Deb & Xiao-Zhi Gao, 2022. "Prediction of Charging Demand of Electric City Buses of Helsinki, Finland by Random Forest," Energies, MDPI, vol. 15(10), pages 1-18, May.
    4. Marwen Elkamel & Lily Schleider & Eduardo L. Pasiliao & Ali Diabat & Qipeng P. Zheng, 2020. "Long-Term Electricity Demand Prediction via Socioeconomic Factors—A Machine Learning Approach with Florida as a Case Study," Energies, MDPI, vol. 13(15), pages 1-21, August.
    5. Jing Shen & Jian Wan & Se-Jung Lim & Lifeng Yu, 2018. "Random-forest-based failure prediction for hard disk drives," International Journal of Distributed Sensor Networks, , vol. 14(11), pages 15501477188, November.
    6. Venkataramana Veeramsetty & Arjun Mohnot & Gaurav Singal & Surender Reddy Salkuti, 2021. "Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models," Energies, MDPI, vol. 14(11), pages 1-21, May.
    7. Khuram Pervez Amber & Muhammad Waqar Aslam & Anzar Mahmood & Anila Kousar & Muhammad Yamin Younis & Bilal Akbar & Ghulam Qadar Chaudhary & Syed Kashif Hussain, 2017. "Energy Consumption Forecasting for University Sector Buildings," Energies, MDPI, vol. 10(10), pages 1-18, October.
    8. Braun, M.R. & Altan, H. & Beck, S.B.M., 2014. "Using regression analysis to predict the future energy consumption of a supermarket in the UK," Applied Energy, Elsevier, vol. 130(C), pages 305-313.
    9. Yang, Tao & Pan, Yiqun & Mao, Jiachen & Wang, Yonglong & Huang, Zhizhong, 2016. "An automated optimization method for calibrating building energy simulation models with measured data: Orientation and a case study," Applied Energy, Elsevier, vol. 179(C), pages 1220-1231.
    Full references (including those not matched with items on IDEAS)

    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. R. Rueda & M. P. Cuéllar & M. Molina-Solana & Y. Guo & M. C. Pegalajar, 2019. "Generalised Regression Hypothesis Induction for Energy Consumption Forecasting," Energies, MDPI, vol. 12(6), pages 1-22, March.
    2. Seyed Azad Nabavi & Alireza Aslani & Martha A. Zaidan & Majid Zandi & Sahar Mohammadi & Naser Hossein Motlagh, 2020. "Machine Learning Modeling for Energy Consumption of Residential and Commercial Sectors," Energies, MDPI, vol. 13(19), pages 1-22, October.
    3. Faisal Mohammad & Mohamed A. Ahmed & Young-Chon Kim, 2021. "Efficient Energy Management Based on Convolutional Long Short-Term Memory Network for Smart Power Distribution System," Energies, MDPI, vol. 14(19), pages 1-23, September.
    4. Tamer, Tolga & Gürsel Dino, Ipek & Meral Akgül, Cagla, 2022. "Data-driven, long-term prediction of building performance under climate change: Building energy demand and BIPV energy generation analysis across Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    5. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "An energy-saving oriented air balancing strategy for multi-zone demand-controlled ventilation system," Energy, Elsevier, vol. 172(C), pages 1053-1065.
    6. Anna Kipping & Erik Trømborg, 2017. "Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock," Energies, MDPI, vol. 11(1), pages 1-20, December.
    7. 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.
    8. Yuan, Jun & Nian, Victor & Su, Bin & Meng, Qun, 2017. "A simultaneous calibration and parameter ranking method for building energy models," Applied Energy, Elsevier, vol. 206(C), pages 657-666.
    9. Omar Jouma El-Hafez & Tarek Y. ElMekkawy & Mohamed Kharbeche & Ahmed Massoud, 2022. "Impact of COVID-19 Pandemic on Qatar Electricity Demand and Load Forecasting: Preparedness of Distribution Networks for Emerging Situations," Sustainability, MDPI, vol. 14(15), pages 1-13, July.
    10. Atif Maqbool Khan & Artur Wyrwa, 2024. "A Survey of Quantitative Techniques in Electricity Consumption—A Global Perspective," Energies, MDPI, vol. 17(19), pages 1-38, September.
    11. Glasgo, Brock & Hendrickson, Chris & Azevedo, Inês Lima, 2017. "Assessing the value of information in residential building simulation: Comparing simulated and actual building loads at the circuit level," Applied Energy, Elsevier, vol. 203(C), pages 348-363.
    12. Ahmad, Tanveer & Chen, Huanxin & Huang, Ronggeng & Yabin, Guo & Wang, Jiangyu & Shair, Jan & Azeem Akram, Hafiz Muhammad & Hassnain Mohsan, Syed Agha & Kazim, Muhammad, 2018. "Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment," Energy, Elsevier, vol. 158(C), pages 17-32.
    13. Khamma, Thulasi Ram & Zhang, Yuming & Guerrier, Stéphane & Boubekri, Mohamed, 2020. "Generalized additive models: An efficient method for short-term energy prediction in office buildings," Energy, Elsevier, vol. 213(C).
    14. Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019. "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, vol. 80(C), pages 937-949.
    15. Pedro Paulo Fernandes da Silva & Alberto Hernandez Neto & Ildo Luis Sauer, 2021. "Evaluation of Model Calibration Method for Simulation Performance of a Public Hospital in Brazil," Energies, MDPI, vol. 14(13), pages 1-20, June.
    16. Yannan Zhou & Jixia Huang & Mingxiang Huang & Yicheng Lin, 2019. "The Driving Forces of Carbon Dioxide Equivalent Emissions Have Spatial Spillover Effects in Inner Mongolia," IJERPH, MDPI, vol. 16(10), pages 1-14, May.
    17. Mahesh, Aeidapu & Sandhu, Kanwarjit Singh, 2015. "Hybrid wind/photovoltaic energy system developments: Critical review and findings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1135-1147.
    18. Hamed Yassaghi & Simi Hoque, 2021. "Impact Assessment in the Process of Propagating Climate Change Uncertainties into Building Energy Use," Energies, MDPI, vol. 14(2), pages 1-27, January.
    19. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    20. Taesub Lim & Jaewang Seok & Daeung Danny Kim, 2017. "A Comparative Study of Energy Performance of Fumed Silica Vacuum Insulation Panels in an Apartment Building," Energies, MDPI, vol. 10(12), pages 1-12, 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:15:y:2022:i:22:p:8543-:d:973310. 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.