Short-Term Load Forecasting in Power System Using Recurrent Neural Network

As energy demand increases rapidly, short-term load forecasting is becoming progressively vital in power system dispatch and demand response. This study proposes a short-term load forecasting approach for the power system in Vietnam using a gated recurrent unit-based deep learning model. The model uses historical load sequences to forecast single-step and multi-step ahead values of load consumption. The dataset, provided by Ho Chi Minh City Power Corporation (EVNHCMC), includes hourly load consumption data. The simulation results demonstrate the effectiveness of the developed prediction algorithm, particularly for multi-step forecasting.

In 2023 International Conference on System Science and Engineering (ICSSE)