Short-term prediction of regional energy consumption by metaheuristic optimized deep learning models
Modern civilization is heavily dependent on energy, which burdens the energy sector. Therefore, a highly accurate energy consumption forecast is essential to provide valuable information for efficient energy distribution and storage. This study proposed a hybrid deep learning model, called I-CNN-JS,...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
The University of Danang
2024-11-01
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| Series: | Tạp chí Khoa học và Công nghệ |
| Subjects: | |
| Online Access: | https://jst-ud.vn/jst-ud/article/view/9585 |
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| Summary: | Modern civilization is heavily dependent on energy, which burdens the energy sector. Therefore, a highly accurate energy consumption forecast is essential to provide valuable information for efficient energy distribution and storage. This study proposed a hybrid deep learning model, called I-CNN-JS, by incorporating a jellyfish search (JS) algorithm into an ImageNet-winning convolutional neural network (I-CNN) to predict week-ahead energy consumption. First, numerical data were encoded into grayscale images for input of the proposed model, showcasing the novelty of using image data for analysis. Second, a newly developed metaheuristic optimization algorithm, JS, was used to improving model accuracy. Results showed that the proposed method outperformed conventional numerical input methods. The optimized model yielded a mean absolute percentage error improvement of 0.5% compared to the default models, indicating that JS is a promising method for achieving the optimal hyperparameters. Sensitivity analysis further evaluated the impact of image pixel orientation on performance model. |
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| ISSN: | 1859-1531 |