A New Short-term Power Load Forecasting Model Based on Chaotic Time Series and SVM

作者：Niu Dongxiao^*; Wang Yongli; Duan Chunming; Xing Mian

来源：Journal of Universal Computer Science, 2009, 15(13): 2726-2745.

• Support vector machine
• Chaotic time series
• Lyapunov exponents
• Parameter selection
• Load forecasting

This paper presents a model for power load forecasting using support vector machine and chaotic time series. The new model can make more accurate prediction. In the past few years, along with power system privatization and deregulation, accurate forecast of electricity load has received increasing attention. According to the chaotic and non-linear characters of power load data, the model of support vector machines (SVM) based on chaotic time series has been established. The time series matrix has also been established according to the theory of phase-space reconstruction. The Lyapunov exponents, one important component of chaotic time series, are used to determine time delay and embedding dimension, the decisive parameters for SVM. Then support vector machines algorithm is used to predict power load. In order to prove the rationality of chosen dimension, another two random dimensions are selected to compare with the calculated dimension. And to prove the effectiveness of the model, BP algorithm is used to compare with the results of SVM. Findings show that the model is effective and highly accurate in the forecasting of short-term power load. It means that the model combined with SVM and chaotic time series learning system have more advantage than other models.

• 出版日期2009
• 单位华北电力大学