A hybrid short-term load forecasting model developed by factor and feature selection algorithms using improved grasshopper optimization algorithm and principal component analysis

Hybrid load forecasting models analyze linear and nonlinear components separately. If hybrid models were integrated with factor and feature selection algorithms, they would improve significantly. In the hybrid model proposed by this paper, the initial data were decomposed by an empirical mode decomposition (EMD) model. The linear component was analyzed through the autoregressive integrated moving average (ARIMA) method and the nonlinear component by a neural network (NN) and weighted by the improved flower pollination algorithm (IFPA). With the nonlinear component, the input load demand variable was decomposed by a wavelet transform (WT). In this paper, the improved grasshopper optimization algorithm (IGOA) and the principal component analysis (PCA) were employed to determine the input feature and input factor, respectively. Therefore, the proposed model was composed of EMD, IGOA, PCA, ARIMA, IFPA, NN, and WT algorithms. Finally, Iran’s Electricity Market (IEM) data were used to show improvements in the precision of the proposed forecasting model.

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