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.
Highlights
The university dormitory is the place where college students spend most of their daily life.The electricity consumption is often huge without any saving actions
This paper proposes a novel empirical mode decomposition (EMD)-support vector regression (SVR)-quantum genetic algorithm (QGA) electricity consumption forecasting model to provide the campus managers with more accurate electricity consumption forecasting from the university dormitory
It is superior in capturing the fluctuation variation of the electricity consumption and reveals its potentials to indicate the daily patterns of the electricity consumption of the dormitory which is useful to take some valuable activities regarding improving electricity consumption habits
Summary
The university dormitory is the place where college students spend most of their daily life. Many researchers proposed lots of electricity consumption forecasting models to receive more accurate forecasting results. This paper would apply the EMD method to extract the electricity consumption time series data into several IMFs, each IMF is forecasted by an SVR model with. To compare the forecasting performance among the proposed EMD-SVR-QGA model and other compared models, the original SVR model, the SVR-QGA model (hybridizing the QGA algorithm with an SVR model), and the H-EMD-SVR-PSO model [40], the electricity consumption data are collected from a university dormitory, Quanshan Campus, Jiangsu Normal University, China, with daily type, from 1 September 2018 to 31 March 2019.
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