Short-term forecasting of electricity imbalances using artificial neural networks

Abstract

Currently, the problem of improving results of short-term forecasting of electricity imbalances in the modern electricity market of Ukraine is a current problem. In order to solve this problem, two types of neural networks with recurrent layers LSTM and LSTNet were analyzed in this work. A comparison of the results of short-term forecasting of daily schedules of electricity imbalances using LSTM and LSTNet neural networks with vector autoregression model (VARMA) was carried out. Actual data of the balancing market were used for the research. Analysis of the results shows that the smallest forecast error was achieved using the LSTM artificial neural network architecture.