A hybrid model for electricity price forecasting based on least square support vector machines with combined kernel

Abstract

With the continuous development of the global electricity market, the electricity market needs the electricity price forecasting result to be accurate, and electricity price forecasting also has more profound practical significance. Accurate price forecasting can not only better reflect the operation of the electricity market but also help to make better decisions about the electricity market. However, the electricity price is affected by a variety of uncertain subjective and objective factors, as well as the constraints of the power system, making it more difficult to obtain accurate electricity price forecasting than electricity load forecasting. The kernel function occupies a very important position in a support vector machine (SVM), which is the key to the mature development of SVM theory. When using SVM for classification and regression, the appropriate kernel function is the basis and precondition for obtaining better classification and approximation effects. In this paper, a new combinatorial kernel function that combines RBF and UKF kernel functions is proposed and is applied to a least squares support vector machine (LSSVM), and based on this, a new hybrid model empirical mode (EMD)-Mixed-LSSVM is proposed to forecast electricity price. The hybrid model puts the electricity price data after the noise reduction by the EMD into the LSSVM with the combined kernel function for calculation. In this paper, we forecast the electricity price in Australia and compare the results of EMD-Mixed-LSSVM with other methods. The results show that EMD-Mixed-LSSVM can effectively improve the accuracy of electricity price forecasting.