Research on PSO-ARMA-SVR Short-Term Electricity Consumption Forecast Based on the Particle Swarm Algorithm

Author:

Zhu Wenbo1ORCID,Ma Hao1ORCID,Cai Gaoyan2ORCID,Chen Jianwen3ORCID,Wang Xiucai4ORCID,Li Aiyuan5ORCID

Affiliation:

1. School of Mechatronic Engineering and Automation, Foshan University, Foshan 528000, China

2. Guangdong Haodi Innovation Technology Co., Ltd., Foshan 528000, China

3. School of Electronic Information Engineering, Foshan University, Foshan 528000, China

4. School of Materials Science and Hydrogen Energy, Foshan University, Foshan 528000, China

5. Journal Editorial Department, Foshan University, Foshan 528000, China

Abstract

Aimed at the problem of order determination of short-term power consumption in a time series model, a new method was proposed to determine the order p and the moving average q of the ARMA model by particle swarm optimization (PSO).According to the difference between the predicted value and the real value of the ARMA model, the fitness function of the particle swarm optimization algorithm is constructed, while the optimal solution which satisfies the ARMA model is confirmed by adjusting the inertia weight, population size, particle velocity, and iteration number. Finally, SVR regression is performed by using a support vector machine to correct the residual sequence obtained after the prediction of ARMA. The final prediction result is obtained by adding the predicted values and corrected residual. Based on the data of historical electricity load of a residential district in 2016~2017, the proposed method is compared with the traditional models. The result of the use of MATLAB simulation shows that the method is simple and feasible, greatly improves the model prediction accuracy, and implements the new method for short-term load forecasting of a small sample.

Publisher

Hindawi Limited

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems

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