Abstract
Along with the improvement of Chinese people’s living standard, the proportion of residential energy consumption in total energy consumption is rapidly increasing in China year by year. Accurately forecasting the residential energy consumption is conducive to making energy programming and supply plan for the administrative departments or energy companies. By improving the grey action quantity of traditional grey model with an exponential time term, a novel power‐driven grey model is proposed to forecast energy consumption as reference data for decision makers. The nonlinear parameter of power‐driven grey action quantity is a crucial factor to influence the prediction precision. To promote the prediction accuracy of the power‐driven grey model, whale optimization algorithm is adopted to seek for the optimal value of the nonlinear parameter. Two validations on real‐world datasets are conducted, and the results indicate that the power‐driven grey model has significant advantages on the aspect of prediction performance compared with the other seven classical grey prediction methods. Finally, the power‐driven grey model is applied in forecasting the total residential energy and the thermal energy consumption of China.
Highlights
E rest of the paper is organized as follows
An optimum value of nonlinear parameter α can make the power-driven grey model obtain the best prediction performance because the parameter directly affects the grey action quantity and can control the development coefficient. erefore, an optimization problem with constraint is built to obtain the optimum value of α, in which the objective function is to minimize the fit error of the power-driven grey prediction model. e equality constraints of the optimization problem are formulated in the previous modeling process
A novel power-driven grey prediction method called GM(1,1,eαt) is proposed to forecast the total residential energy consumption and the residential thermal energy consumption of China in this paper. e grey input of GM(1,1,eαt) is an exponential term which is different from the grey action quantity of the traditional grey model
Summary
The optimization of the grey action quantity is an effective means to increase the performance and applicability of the grey model from the previous section. is section proposes a novel power-driven grey model in which a natural exponential function of time is considered as the grey action quantity. 4. Determining the Nonlinear Parameter of the Power-Driven Grey Model with Whale Optimization Algorithm. An optimum value of nonlinear parameter α can make the power-driven grey model obtain the best prediction performance because the parameter directly affects the grey action quantity and can control the development coefficient. Erefore, an optimization problem with constraint is built to obtain the optimum value of α, in which the objective function is to minimize the fit error of the power-driven grey prediction model. The power-driven grey model with optimal parameters is used to forecast the future value in case study
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