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. In this regard, a gated recurrent unit-based deep learning model is applied to use the historical load sequences to forecast the single-step and multi-step ahead values of the load consumption. The hourly load consumption dataset is provided by Ho Chi Minh City Power Corporation (EVNHCMC). Simulation results prove the effectiveness of the developed prediction algorithm for short-term load forecasting, especially for multi-step forecasting.