Hybrid model for renewable energy and loads prediction based on data mining and variational mode decomposition

Accurate renewable resource and load forecasting plays a key role in the progress of power grid planning schemes. In this study, a hybrid short-term forecasting method based K-means clustering and variational mode decomposition (VMD) technique is proposed to deal with the problem of forecasting accuracy. K-means clustering is a means of data mining approach and used for classifying data into several clusters. A cluster selection method is adopted to extract similar features from historical days. To better analyse the time series of historical data, VMD decomposes time series data into an ensemble of components with different frequencies. Self-adaptive evolutionary extreme learning machine as a novel and fast regression tool is trained and used for predicting each component. Eventually, the forecasting result generated by reconstructing all the predicted components values. The performance of the proposed hybrid forecasting model is evaluated by using real data from National Renewable Energy Laboratory. The simulation results show that it can obtain better forecasting accuracy than some previously reported methods.