| Title |
Hybrid Deep Learning-Based Forecast of Weekly Power Demand Combining Attention-Mechanism |
| Authors |
윤상철(Sang-Cheol Yun) ; 김병호(Byoungho Kim) ; 김홍래(Hongrae Kim) |
| DOI |
https://doi.org/10.5370/KIEE.2024.73.9.1507 |
| Keywords |
Load forecasting; Deep Learning; LSTM; GRU; Attention Mechanism |
| Abstract |
In the current situation of increasing power demand, research is needed for better electricity demand forecasting. This study presents a new approach to enhance the accuracy of electricity demand forecasting. The objective of this study is to facilitate more precise Load Forecasting by incorporating the Attention Mechanism into the existing deep learning models, LSTM and GRU. For this purpose, LSTM and GRU models combined with Attention Mechanism were applied to actual power demand data. The experimental results confirmed that the proposed model significantly improved the accuracy of power demand prediction compared to the existing models. These results show that the addition of Attention Mechanism can contribute to improving the performance of deep learning-based power demand prediction models. |