Affiliations: [a] School of Business, Anhui University, Hefei, Anhui, China
| [b] Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, USA
| [c] School of Mathematical Sciences, Anhui University, Hefei, Anhui, China
| [d] School of Computer Science and Technology, Anhui University, Hefei, Anhui, China
Abstract: In this paper, we develop a new triangular fuzzy series combination forecasting method based on triangular fuzzy discrete difference equation forecasting model and PSO-SVR, and use the developed forecasting method to power load forecasting. First, we propose a triangular fuzzy discrete difference equation (TFDDE) forecasting model to predict the triangular fuzzy series, which can accurately predict the fluctuating trend and is suitable for small sample data. Then, the support vector regression optimized by particle swarm optimization (PSO-SVR) is adopted to further improve the forecast result of TFDDE forecasting model, in which the parameters of support vector regression are optimally obtained by particle swarm optimization algorithm so as to avoid the blindness of artificial selection. Finally, the practical example of load forecasting of US PJM power market is employed to illustrate the proposed forecasting method. The experimental results show that the proposed forecasting method produces much better forecasting performance than some existing triangular fuzzy series models. The proposed combination forecasting method, which fully capitalizes on the time series forecasting model and intelligent algorithm, makes the triangular fuzzy series prediction more accurate than before and has good applicability. This is the first attempt of employing discrete difference equation theory for the triangular fuzzy series forecasting.
Keywords: Power load forecasting, triangular fuzzy discrete difference equation, support vector regression, particle swarm optimization
