Hybrid Deep Learning-Based Forecast of Weekly Power Demand Combining Attention-Mechanism

被引:0
|
作者
Yun, Sang-Cheol [2 ]
Kim, Byoungho [1 ]
Kim, Hongrae [1 ]
机构
[1] Dept. of Electronics and Information Engineering, Soonchunhyang University, Korea, Republic of
[2] Dept. of Electrical and Robot Engineering, Soonchunhyang University, Korea, Republic of
来源
Transactions of the Korean Institute of Electrical Engineers | 2024年 / 73卷 / 09期
关键词
Contrastive Learning - Electric load forecasting - Prediction models;
D O I
10.5370/KIEE.2024.73.9.1507
中图分类号
学科分类号
摘要
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. Copyright © The Korean Institute of Electrical Engineers.
引用
收藏
页码:1507 / 1512
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