Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization
The power load prediction is significant in a sustainable power system, which is the key to the energy system’s economic operation. An accurate prediction of the power load can provide a reliable decision for power system planning. However, it is challenging to predict the power load with a single m...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/4346803 |
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| author | Xue-Bo Jin Hong-Xing Wang Xiao-Yi Wang Yu-Ting Bai Ting-Li Su Jian-Lei Kong |
| author_facet | Xue-Bo Jin Hong-Xing Wang Xiao-Yi Wang Yu-Ting Bai Ting-Li Su Jian-Lei Kong |
| author_sort | Xue-Bo Jin |
| collection | DOAJ |
| description | The power load prediction is significant in a sustainable power system, which is the key to the energy system’s economic operation. An accurate prediction of the power load can provide a reliable decision for power system planning. However, it is challenging to predict the power load with a single model, especially for multistep prediction, because the time series load data have multiple periods. This paper presents a deep hybrid model with a serial two‐level decomposition structure. First, the power load data are decomposed into components; then, the gated recurrent unit (GRU) network, with the Bayesian optimization parameters, is used as the subpredictor for each component. Last, the predictions of different components are fused to achieve the final predictions. The power load data of American Electric Power (AEP) were used to verify the proposed predictor. The results showed that the proposed prediction method could effectively improve the accuracy of power load prediction. |
| format | Article |
| id | doaj-art-2917bbb0336e4556b3b49e76ac636c80 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-2917bbb0336e4556b3b49e76ac636c802025-02-03T01:04:27ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/43468034346803Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian OptimizationXue-Bo Jin0Hong-Xing Wang1Xiao-Yi Wang2Yu-Ting Bai3Ting-Li Su4Jian-Lei Kong5School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, ChinaThe power load prediction is significant in a sustainable power system, which is the key to the energy system’s economic operation. An accurate prediction of the power load can provide a reliable decision for power system planning. However, it is challenging to predict the power load with a single model, especially for multistep prediction, because the time series load data have multiple periods. This paper presents a deep hybrid model with a serial two‐level decomposition structure. First, the power load data are decomposed into components; then, the gated recurrent unit (GRU) network, with the Bayesian optimization parameters, is used as the subpredictor for each component. Last, the predictions of different components are fused to achieve the final predictions. The power load data of American Electric Power (AEP) were used to verify the proposed predictor. The results showed that the proposed prediction method could effectively improve the accuracy of power load prediction.http://dx.doi.org/10.1155/2020/4346803 |
| spellingShingle | Xue-Bo Jin Hong-Xing Wang Xiao-Yi Wang Yu-Ting Bai Ting-Li Su Jian-Lei Kong Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization Complexity |
| title | Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization |
| title_full | Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization |
| title_fullStr | Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization |
| title_full_unstemmed | Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization |
| title_short | Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization |
| title_sort | deep learning prediction model with serial two level decomposition based on bayesian optimization |
| url | http://dx.doi.org/10.1155/2020/4346803 |
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