A deep learning approach for load demand forecasting of power system

This study presents a deep learning methodology tailored for the precise forecasting of power system load demands, a critical component in the management and planning of electrical grids. The focus of our thesis lies in comparing the performance of several regression models, including both traditional machine learning algorithms and a deep learning model, specifically a Long Short-Term Memory (LSTM) network. The performances of models were evaluated based on various metrics, namely Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and for all except the LSTM model, the Root Mean Absolute Error (RMAE) and losses. Our findings reveal that the LSTM model significantly outperforms traditional regression techniques, achieving an MAE of 2.1351 and an MSE of 3.8831, metrics that are substantially lower than those recorded for the other models. This highlights the LSTM's superior ability in handling the complexity and variability inherent in power load demand forecasting. This study underscores the potential of deep learning, particularly LSTM networks, in enhancing the accuracy and reliability of power load demand forecasting. Such advancements are vital for optimizing the operation and efficiency of power systems, facilitating better energy management, and contributing to the overall stability of electrical grids.