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

Author

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  • 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
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    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.
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