Predictive Analysis of a Building’s Power Consumption Based on Digital Twin Platforms

Fengyi Han, Fei Du (), Shuo Jiao and Kaifang Zou
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Fengyi Han: BIM Institute of Technology and Industry, Changchun Institute of Technology, Changchun 130103, China
Fei Du: BIM Institute of Technology and Industry, Changchun Institute of Technology, Changchun 130103, China
Shuo Jiao: BIM Institute of Technology and Industry, Changchun Institute of Technology, Changchun 130103, China
Kaifang Zou: BIM Institute of Technology and Industry, Changchun Institute of Technology, Changchun 130103, China

Energies, 2024, vol. 17, issue 15, 1-19

Abstract: Colleges and universities are large consumers of energy, with a huge potential for building energy efficiency, and need to reduce energy consumption to build a low-carbon, energy-saving campus. Predicting the energy consumption of campus buildings can help to accurately manage the electricity consumption of buildings and reduce the energy consumption of buildings. However, the electricity consumption of a building’s operation is affected by many factors, and it is difficult to establish a model for analysis and prediction. Therefore, in this study, the training building of the BIM education center on campus was selected as the research object, and a digital twin O&M platform was established by integrating IoT, digital twin technology (DDT), smart meter monitoring devices, and indoor environment monitoring devices. The O&M management platform can monitor real-time changes in indoor power consumption data and environmental parameters, and organize data on multiple influencing factors and power consumption. Following training, validation, and testing, the machine learning models (back propagation neural network, support vector model, and multiple linear regression model) were assessed and compared for accuracy. Following the multiple linear regression and support vector models, the backpropagation neural network model exhibited the highest accuracy. Consistent with the actual power consumption detection results in the BIM education center, the backpropagation neural network model produced results. Consequently, the BP model created in this study demonstrated its dependability and ability to forecast campus building power usage, assisting the university in organizing its energy supply and creating a campus that prioritizes conservation.

Keywords: building energy consumption; digital twin; influencing factors; machine learning method; multiple linear regression model (MLR); support vector model (SVM); backpropagation neural network (BP) (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: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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