Hybrid HHO-Wavenet Model Applies in Short-term Load Forecasting for Microgrid System

Power load forecasting is an important issue in a microgrid (MG) energy management. Accurate load forecasting is urgently required for effective power management for MG. This paper proposes a new method for short-term load forecasting (STLF). This method uses both long and short data series provided for a Wavenet-based model inspired by a Long Short-Term Memory (LSTM), to forecast hourly load demand. To increase the accuracy of the prediction model, this study used the Harris Hawks Optimization (HHO) algorithm to include in the calculation in the Wavenet network. In order to demonstrate the effectiveness of the model, we work with the load data set of an MG model belonging to the Ho Chi Minh City power grid. The forecasting model is compared with the previous forecasting models. The results show that our proposed model outperforms other deep learning-based models in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE).