Mô hình kết hợp HHO-GCN-LSTM ứng dụng trong dự báo đồ thị phụ tải cho lưới điện nhỏ

Abstract

Power load graph forecasting is of great interest in microgrid (MG) energy management. The need for accurate short-term load charting is particularly important for efficient power management for MG. This paper proposes a new method for short-term load forecasting (STLF). This method uses the time series of temperature and load data provided to the model based on the Graph Convolutional Network (GCN) model to combine the characterization of the input and output data given into the calculation for the corresponding LSTM network to forecast the hourly load graph in the future. In order to evaluate the accuracy of the prediction model, this study used the HHO optimization algorithm to calculate the GCN-LSTM network. To compare the results of the model with other forecasting models, we work with the load data set of an MG model belonging to the Ho Chi Minh City power grid. The forecast model is compared with previous forecasting models. The results show that our proposed model has superior results compared to other deep learning-based models in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE).