Speaker
Yana Podlesna
Description
Presented work investigates deep learning methods, focusing on Temporal Fusion Transformer (TFT), for multi-horizon forecasting of energy demand in power systems. The TFT model's performance is benchmarked against traditional machine learning models such as XGBoost and Random Forest and evaluated over 6-hour and 24-hour ahead predictions. The TFT's capacity for handling temporal dependencies proves advantageous, enhancing the accuracy of energy demand prediction. The results illuminate the transformative potential of advanced deep learning methods in improving power system management amid growing renewable energy integration.
