Combining PSO-SVR and Random Forest Based Feature Selection for Day-ahead Peak Load Forecasting

In recent years, with computer and Internet technology developing at a breakneck speed, ensuring the fair dispatching and stable operation of the smart grid has increasingly become the focus of power companies. The emergence of the smart grid makes it difficult for a single model to accurately predict the complex power data, and the combined prediction model has become the main target of research by experts and scholars. To this end, a new combination prediction model is proposed in this research. The missing values in the data set are filled by the forecasts of the Random Forest (RF) approach, then the significance of input attributes is computed. After that, the feature selection is performed, and the peak load forecasting is modeled by using support vector regression based on particle swarm optimization (PSO-SVR). The experimental results using real data from a county in Jiangxi Province indicate that the model's prediction ability with feature selection is better than that of the model without feature selection.