APPLICATION OF COMBINING DATA PREPROCESSING WITH WAVELET FILTERING FOR GCN-LSTM NETWORK WITH HHO OPTIMIZATION ALGORITHM IN LOAD FORECASTING MODEL

Abstract

Accurate daily load forecasting is critical for effective energy management planning. In this study, the article proposes a new method for daily load forecasting that takes advantage of load data and weather data over time in Tien Giang. The forecast model is improved by incorporating a data preprocessing Wavelet filter to the graph convolutional network to combine input data across time points, days of the year, and other input features. The output of the graph convolution network is then fed into the Long Short Term Memory network with an optimization algorithm in the load forecasting model. The forecasting model is evaluated based on load data from the mini-grid model in Tien Giang province's power grid, comparing it with other deep learning-based forecasting models. The results show that the proposed model outperforms other models in terms of root mean square error and average absolute percentage error, proving the effectiveness of the method in terms of reliability and accuracy.