The Time-Series Production Simulation in Cost Management of New Energy Grid Connection Under the Internet of Things
To promote the intelligent and efficient development of new energy grid connection management, this work first analyzes the current situation and problems in cost management for new energy grid connections. It is found that existing models are not effectively adaptable to complex and dynamic energy...
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| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10445258/ |
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| author | Shurui Wang |
| author_facet | Shurui Wang |
| author_sort | Shurui Wang |
| collection | DOAJ |
| description | To promote the intelligent and efficient development of new energy grid connection management, this work first analyzes the current situation and problems in cost management for new energy grid connections. It is found that existing models are not effectively adaptable to complex and dynamic energy systems. Therefore, this work constructs a comprehensive monitoring system based on Internet of Things (IoT) technology. This system monitors and collects the energy production and consumption data in real-time to simulate the processes of new energy generation, storage, transmission, and consumption. The model considers different types of new energy resources, including solar, wind, and a time-series production simulation method is employed to simulate the energy production process. Finally, an improved Informer model for intelligent cost management for new energy grid connection is built. The research results indicate that with the penetration of new energy, the system’s idle capacity gradually increases, and the solar power generation also increases, but the utilization hours of solar energy slightly decrease. Moreover, the improved Informer model performs well in the management of new energy grid connections. The introduced Wasserstein distance improvement method positively enhances the model’s prediction accuracy, with a decrease of 208.4 in Mean Squared Error, a reduction of 145.6 in Root Mean Squared Error, and a decrease of 7.14 in Mean Absolute Error. This work provides an innovative solution for IoT-based cost management of new energy grid connections, having theoretical significance and practical value. |
| format | Article |
| id | doaj-art-a0f736e987b94ae5a2d13fcd3990cc1f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a0f736e987b94ae5a2d13fcd3990cc1f2025-01-28T00:00:52ZengIEEEIEEE Access2169-35362024-01-0112323693238010.1109/ACCESS.2024.337016210445258The Time-Series Production Simulation in Cost Management of New Energy Grid Connection Under the Internet of ThingsShurui Wang0https://orcid.org/0009-0001-2431-5475Polytechnic Institute, Zhejiang University, Hangzhou, ChinaTo promote the intelligent and efficient development of new energy grid connection management, this work first analyzes the current situation and problems in cost management for new energy grid connections. It is found that existing models are not effectively adaptable to complex and dynamic energy systems. Therefore, this work constructs a comprehensive monitoring system based on Internet of Things (IoT) technology. This system monitors and collects the energy production and consumption data in real-time to simulate the processes of new energy generation, storage, transmission, and consumption. The model considers different types of new energy resources, including solar, wind, and a time-series production simulation method is employed to simulate the energy production process. Finally, an improved Informer model for intelligent cost management for new energy grid connection is built. The research results indicate that with the penetration of new energy, the system’s idle capacity gradually increases, and the solar power generation also increases, but the utilization hours of solar energy slightly decrease. Moreover, the improved Informer model performs well in the management of new energy grid connections. The introduced Wasserstein distance improvement method positively enhances the model’s prediction accuracy, with a decrease of 208.4 in Mean Squared Error, a reduction of 145.6 in Root Mean Squared Error, and a decrease of 7.14 in Mean Absolute Error. This work provides an innovative solution for IoT-based cost management of new energy grid connections, having theoretical significance and practical value.https://ieeexplore.ieee.org/document/10445258/Time-series production simulationdeep learningInternet of Thingsnew energy grid connectioncost management |
| spellingShingle | Shurui Wang The Time-Series Production Simulation in Cost Management of New Energy Grid Connection Under the Internet of Things IEEE Access Time-series production simulation deep learning Internet of Things new energy grid connection cost management |
| title | The Time-Series Production Simulation in Cost Management of New Energy Grid Connection Under the Internet of Things |
| title_full | The Time-Series Production Simulation in Cost Management of New Energy Grid Connection Under the Internet of Things |
| title_fullStr | The Time-Series Production Simulation in Cost Management of New Energy Grid Connection Under the Internet of Things |
| title_full_unstemmed | The Time-Series Production Simulation in Cost Management of New Energy Grid Connection Under the Internet of Things |
| title_short | The Time-Series Production Simulation in Cost Management of New Energy Grid Connection Under the Internet of Things |
| title_sort | time series production simulation in cost management of new energy grid connection under the internet of things |
| topic | Time-series production simulation deep learning Internet of Things new energy grid connection cost management |
| url | https://ieeexplore.ieee.org/document/10445258/ |
| work_keys_str_mv | AT shuruiwang thetimeseriesproductionsimulationincostmanagementofnewenergygridconnectionundertheinternetofthings AT shuruiwang timeseriesproductionsimulationincostmanagementofnewenergygridconnectionundertheinternetofthings |