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

Hao Yang, Maoyu Ran () and Chaoqun Zhuang ()
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Hao Yang: School of Architecture, Huaqiao University, Xiamen 361021, China
Maoyu Ran: School of Architecture, Huaqiao University, Xiamen 361021, China
Chaoqun Zhuang: Data-Centric Engineering, The Alan Turing Institute, The British Library, 96 Euston Road, London NW1 2DB, UK

Energies, 2022, vol. 15, issue 22, 1-19

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.

Keywords: joinpoint regression; Multiple Linear Regression; back propagation neural network; Random Forest; electricity consumption; prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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