Parallel LSTM-Based Regional Integrated Energy System Multienergy Source-Load Information Interactive Energy Prediction
The multienergy interaction characteristic of regional integrated energy systems can greatly improve the efficiency of energy utilization. This paper proposes an energy prediction strategy for multienergy information interaction in regional integrated energy systems from the perspective of horizonta...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/7414318 |
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| author | Bo Wang Liming Zhang Hengrui Ma Hongxia Wang Shaohua Wan |
| author_facet | Bo Wang Liming Zhang Hengrui Ma Hongxia Wang Shaohua Wan |
| author_sort | Bo Wang |
| collection | DOAJ |
| description | The multienergy interaction characteristic of regional integrated energy systems can greatly improve the efficiency of energy utilization. This paper proposes an energy prediction strategy for multienergy information interaction in regional integrated energy systems from the perspective of horizontal interaction and vertical interaction. Firstly, the multienergy information coupling correlation of the regional integrated energy system is analyzed, and the horizontal interaction and vertical interaction mode are proposed. Then, based on the long short-term memory depth neural network time series prediction, parallel long short-term memory multitask learning model is established to achieve horizontal interaction among multienergy systems and based on user-driven behavioral data to achieve vertical interaction between source and load. Finally, uncertain resources composed of wind power, photovoltaic, and various loads on both sides of source and load integrated energy prediction are achieved. The simulation results of the measured data show that the interactive parallel prediction method proposed in this article can effectively improve the prediction effect of each subtask. |
| format | Article |
| id | doaj-art-5324d9c50d6a48a783ee0c070994e8ae |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-5324d9c50d6a48a783ee0c070994e8ae2025-02-03T01:21:33ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/74143187414318Parallel LSTM-Based Regional Integrated Energy System Multienergy Source-Load Information Interactive Energy PredictionBo Wang0Liming Zhang1Hengrui Ma2Hongxia Wang3Shaohua Wan4School of Electrical Engineering, Wuhan University, Wuhan 430070, ChinaSchool of Electrical Engineering, Wuhan University, Wuhan 430070, ChinaTus-Institute for Renewable Energy, Qinghai University, Xining, ChinaSchool of Electrical Engineering, Wuhan University, Wuhan 430070, ChinaSchool of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, ChinaThe multienergy interaction characteristic of regional integrated energy systems can greatly improve the efficiency of energy utilization. This paper proposes an energy prediction strategy for multienergy information interaction in regional integrated energy systems from the perspective of horizontal interaction and vertical interaction. Firstly, the multienergy information coupling correlation of the regional integrated energy system is analyzed, and the horizontal interaction and vertical interaction mode are proposed. Then, based on the long short-term memory depth neural network time series prediction, parallel long short-term memory multitask learning model is established to achieve horizontal interaction among multienergy systems and based on user-driven behavioral data to achieve vertical interaction between source and load. Finally, uncertain resources composed of wind power, photovoltaic, and various loads on both sides of source and load integrated energy prediction are achieved. The simulation results of the measured data show that the interactive parallel prediction method proposed in this article can effectively improve the prediction effect of each subtask.http://dx.doi.org/10.1155/2019/7414318 |
| spellingShingle | Bo Wang Liming Zhang Hengrui Ma Hongxia Wang Shaohua Wan Parallel LSTM-Based Regional Integrated Energy System Multienergy Source-Load Information Interactive Energy Prediction Complexity |
| title | Parallel LSTM-Based Regional Integrated Energy System Multienergy Source-Load Information Interactive Energy Prediction |
| title_full | Parallel LSTM-Based Regional Integrated Energy System Multienergy Source-Load Information Interactive Energy Prediction |
| title_fullStr | Parallel LSTM-Based Regional Integrated Energy System Multienergy Source-Load Information Interactive Energy Prediction |
| title_full_unstemmed | Parallel LSTM-Based Regional Integrated Energy System Multienergy Source-Load Information Interactive Energy Prediction |
| title_short | Parallel LSTM-Based Regional Integrated Energy System Multienergy Source-Load Information Interactive Energy Prediction |
| title_sort | parallel lstm based regional integrated energy system multienergy source load information interactive energy prediction |
| url | http://dx.doi.org/10.1155/2019/7414318 |
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