Construction of EMD-SVR-QGA Model for Electricity Consumption: Case of University Dormitory

Yuanyuan Zhou, Min Zhou, Qing Xia and Wei-Chiang Hong
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Yuanyuan Zhou: School of Management, China University of Mining & Technology, Xuzhou 221116, China
Min Zhou: School of Management, China University of Mining & Technology, Xuzhou 221116, China
Qing Xia: School of Management, China University of Mining & Technology, Xuzhou 221116, China
Wei-Chiang Hong: Department of Information Management, Oriental Institute of Technology, Panchiao, New Taipei 221, Taiwan

Mathematics, 2019, vol. 7, issue 12, 1-23

Abstract: In the context of the nationwide call for “energy savings” in China, it is desirable to establish a more accurate forecasting model to manage the electricity consumption from the university dormitory, to provide a suitable management approach, and eventually, to achieve the “green campus” policy. This paper applies the empirical mode decomposition (EMD) method and the quantum genetic algorithm (QGA) hybridizing with the support vector regression (SVR) model to forecast the daily electricity consumption. Among the decomposed intrinsic mode functions (IMFs), define three meaningful items: item A contains the terms but the residual term; item B contains the terms but without the top two IMFs (with high randomness); and item C contains the terms without the first two IMFs and the residual term, where the first two terms imply the first two high-frequency part of the electricity consumption data, and the residual term is the low-frequency part. These three items are separately modeled by the employed SVR-QGA model, and the final forecasting values would be computed as A + B − C. Therefore, this paper proposes an effective electricity consumption forecasting model, namely EMD-SVR-QGA model, with these three items to forecast the electricity consumption of a university dormitory, China. The forecasting results indicate that the proposed model outperforms other compared models.

Keywords: empirical mode decomposition (EMD); quantum genetic algorithm (QGA); intrinsic mode function (IMF); support vector regression (SVR); university dormitory; electricity consumption (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
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