A Hybrid Model of Least Squares Support Vector Regression Optimized by Particle Swarm Optimization for Electricity Demand Prediction

To further increase prediction accuracy, improve power management and reduce waste, this paper proposes a hybrid electric load forecasting model based on wavelet analysis (WA) and least squares support vector regression (LSSVR) with particle swarm optimization (PSO) algorithm. Where wavelet analysis is used to transform the original electric data sequence into multi-resolution subsets during the preprocessing stage and then the decomposed subsets are inserted into LSSVR to realize prediction, finally the ultimate prediction results are obtained via the wavelet reconstruction with all the independent prediction results. However, the key to influence forecasting accuracy is the parameters used in the LSSVR, in this paper PSO is used to optimize the kernel parameter Δ and the regularization parameter γ of LSSVR and choose the appropriate parameters for the hybrid forecasting model. The effectiveness of the proposed hybrid model has been proved in electric load prediction; the prediction results show that the proposed hybrid model outperforms the Elman networks model, the radial basis function (RBF) neural network model and LSSVR optimized only with PSO. The hybrid model achieves satisfying results, the mean absolute percentage error (MAPE) with 0.907% and the coefficient of determination (R 2) with 0.9936, it offers a higher forecasting precision.