Research on PSO-ARMA-SVR Short-Term Electricity Consumption Forecast Based on the Particle Swarm Algorithm. (25th February 2021)

• Record Type: Journal Article
• Authors: Zhu, Wenbo; Ma, Hao; Cai, Gaoyan; Chen, Jianwen; Wang, Xiucai; Li, Aiyuan
• Other Names: Liu Xin Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2021(2021)
• Publication Date: 2021-02-25
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2021/6691537
• Languages: English
• ISSNs: 1530-8669

Abstract

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.