Forecasting of short-term power demands in Polish Power System using ensemble of LSTM networks

The article presents and discusses the results of the research of forecasting power demands in Polish Power System with time horizon of one hour ahead in conditions of limited availability of forecasting model input data, covering only three months. The prediction was carried out using deep neural networks - LSTM (Long Short-Term Memory) connected to an ensemble. The performance of the ensemble is much more efficient than individual networks working separately. The numerical experiments were conducted using MATLAB computing environment. The accuracy of the predictions was estimated using such statistical measures as MAPE, MAE, RMSE, Pearson correlation coefficient R.