A New Model to Short-Term Power Load Forecasting Combining Chaotic Time Series and SVM

Abstract

Accurate forecasting of electricity load has been one of the most important issues in the electricity industry. Recently, 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 Lyapunov exponents was established. The time series matrix was established according to the theory of phase-space reconstruction, and then Lyapunov exponents was computed to determine time delay and embedding dimension. Then support vector machines algorithm was used to predict power load. In order to prove the rationality of chosen dimension, another two random dimensions were selected to compare with the calculated dimension. And to prove the effectiveness of the model, BP algorithm was used to compare with the result of SVM. The results show that the model is effective and highly accurate in the forecasting of short-term power load. It is denoted that the model combining SVM and chaotic time series learning system has advantage than other models.