A hybrid application algorithm based on the support vector machine and artificial intelligence: An example of electric load forecasting

Abstract

Accurate electric load forecasting could prove to be a very useful tool for all market participants in electricity markets. Because it can not only help power producers and consumers make their plans but also can maximize their profits. In this paper, a new combined forecasting method (ESPLSSVM) based on empirical mode decomposition, seasonal adjustment, particle swarm optimization (PSO) and least squares support vector machine (LSSVM) model is proposed. In the electric market, noise signals usually affect the forecasting accuracy, which were caused by different erratic factors. First of all, ESPLSSVM uses an empirical mode decomposition-based signal filtering method to reduce the influence of noise signals. Secondly, ESPLSSVM eliminates the seasonal components from the de-noised resulting series and then it models the resultant series using the LSSVM which is optimized by PSO (PLSSVM). Finally, by multiplying the seasonal indexes by the PLSSVM forecasts, ESPLSSVM acquires the final forecasting result. The effectiveness of the presented method is examined by comparing with different methods including basic LSSVM (LSSVM), empirical mode decomposition-based signal filtering method processed by LSSVM (ELSSVM) and seasonal adjustment processed by LSSVM (SLSSVM). Case studies show ESPLSSVM performed better than the other three load forecasting approaches.