COMPARISON OF THE RESULTS OF SHORT-TERM FORECASTING OF ELECTRICITY IMBALANCES OF THE IPS OF UKRAINE USING AUTOREGRESSIVE MODELS AND ARTIFICIAL NEURAL NETWORKS

Abstract

The article presents the results of the study of models for short-term forecasting of overall electricity imbalances in the IPS of Ukraine. The analysis of forecasting results obtained using different types of autoregressive models and two forecasting models based on artificial neural networks was performed. Conducted research based on actual data of the balancing market of electric energy of Ukraine showed the effectiveness of using artificial neural networks for the specified task. It is shown that the application of the LSTM (Long short-term memory) artificial neural network model achieves the highest forecasting accuracy for both positive and negative electricity imbalances, respectively, compared to forecasting using autoregressive models. Bibl. 11, fig. 3, table.