Learn More
Monthly forecasting of electric energy consumption is important for planning the generation and distribution of power utilities. However, the features of this time series are so complex that directly modeling is difficult. Three kinds of relatively simple series can be derived when a discrete wavelet transform is used to extract the raw features, namely,(More)
Many models have been developed to forecast wind farm power output. It is generally difficult to determine whether the performance of one model is consistently better than that of another model under all circumstances. Motivated by this finding, we aimed to integrate groups of models into an aggregated model using fuzzy theory to obtain further performance(More)
—In this paper, we propose Hybrid Particle Swarm Optimization (HPSO) with genetic algorithm(GA) mutation to optimize the SVM forecasting model. In the process of doing so, we first use HPSO with genetic algorithm to make pretreatment of the data. PSO with GA model is a method for finding a solution of stochastic global optimizer based on swarm intelligence.(More)