Optimization of support vector machine power load forecasting model based on data mining and Lyapunov exponents

Abstract

According to the chaotic and non-linear characters of power load data, the time series matrix is established with the theory of phase-space reconstruction, and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension. Due to different features of the data, data mining algorithm is conducted to classify the data into different groups. Redundant information is eliminated by the advantage of data mining technology, and the historical loads that have highly similar features with the forecasting day are searched by the system. As a result, the training data can be decreased and the computing speed can also be improved when constructing support vector machine (SVM) model. Then, SVM algorithm is used to predict power load with parameters that get in pretreatment. In order to prove the effectiveness of the new model, the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network. It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%, 1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension, 14-dimension and BP network, respectively. This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.