An adaptive load forecasting model in microgrids: A cloud-edge orchestrated approach tailored for accuracy, real-time response, and privacy needs
The load forecasting tasks in different types of microgrids offer diversified requirements on application, such as forecasting accuracy, model complexity restrictions, and hardware environment. In our paper, typical load forecasting tasks in microgrids are classified into accuracy-oriented, real-tim...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525000419 |
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| author | Yan Zhao Jiaqi Shi Donglai Wang He Jiang Xiang Zhang |
| author_facet | Yan Zhao Jiaqi Shi Donglai Wang He Jiang Xiang Zhang |
| author_sort | Yan Zhao |
| collection | DOAJ |
| description | The load forecasting tasks in different types of microgrids offer diversified requirements on application, such as forecasting accuracy, model complexity restrictions, and hardware environment. In our paper, typical load forecasting tasks in microgrids are classified into accuracy-oriented, real-time response and privacy-preserving type. An adaptive load forecasting model is proposed considering the trade-off between accuracy and efficiency by utilizing the customized AI algorithm and real cloud-edge orchestrated architecture. The decoupled module of forecasting model is considerably analyzed from accuracy impact and computing resource occupation, which arranges in different hardware environments to meet needs of different microgrid. Finally, the adaptive forecasting model is verified by the actual dataset from the MiRIS microgrid in Belgium. The proposed model can achieve satisfactory trade-off between accuracy and computation resource consumption, which meets the requirement for different types of microgrid load forecasting tasks. |
| format | Article |
| id | doaj-art-e98542d1667c4f45838ad15251ef51b7 |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-e98542d1667c4f45838ad15251ef51b72025-02-05T04:31:00ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-04-01165110490An adaptive load forecasting model in microgrids: A cloud-edge orchestrated approach tailored for accuracy, real-time response, and privacy needsYan Zhao0Jiaqi Shi1Donglai Wang2He Jiang3Xiang Zhang4Key Laboratory of Regional Multi-Energy System Integration and Control in Liaoning Province, Shenyang Institute of Engineering, Shenyang, 110136, ChinaKey Laboratory of Regional Multi-Energy System Integration and Control in Liaoning Province, Shenyang Institute of Engineering, Shenyang, 110136, China; Corresponding author.Key Laboratory of Regional Multi-Energy System Integration and Control in Liaoning Province, Shenyang Institute of Engineering, Shenyang, 110136, ChinaKey Laboratory of Regional Multi-Energy System Integration and Control in Liaoning Province, Shenyang Institute of Engineering, Shenyang, 110136, ChinaState Grid Liaoning Electric Power Research Institute, Shenyang, 110006, ChinaThe load forecasting tasks in different types of microgrids offer diversified requirements on application, such as forecasting accuracy, model complexity restrictions, and hardware environment. In our paper, typical load forecasting tasks in microgrids are classified into accuracy-oriented, real-time response and privacy-preserving type. An adaptive load forecasting model is proposed considering the trade-off between accuracy and efficiency by utilizing the customized AI algorithm and real cloud-edge orchestrated architecture. The decoupled module of forecasting model is considerably analyzed from accuracy impact and computing resource occupation, which arranges in different hardware environments to meet needs of different microgrid. Finally, the adaptive forecasting model is verified by the actual dataset from the MiRIS microgrid in Belgium. The proposed model can achieve satisfactory trade-off between accuracy and computation resource consumption, which meets the requirement for different types of microgrid load forecasting tasks.http://www.sciencedirect.com/science/article/pii/S0142061525000419Load forecastingDifferent types of microgridsCustomized AI algorithmCloud-edge orchestrated architecture |
| spellingShingle | Yan Zhao Jiaqi Shi Donglai Wang He Jiang Xiang Zhang An adaptive load forecasting model in microgrids: A cloud-edge orchestrated approach tailored for accuracy, real-time response, and privacy needs International Journal of Electrical Power & Energy Systems Load forecasting Different types of microgrids Customized AI algorithm Cloud-edge orchestrated architecture |
| title | An adaptive load forecasting model in microgrids: A cloud-edge orchestrated approach tailored for accuracy, real-time response, and privacy needs |
| title_full | An adaptive load forecasting model in microgrids: A cloud-edge orchestrated approach tailored for accuracy, real-time response, and privacy needs |
| title_fullStr | An adaptive load forecasting model in microgrids: A cloud-edge orchestrated approach tailored for accuracy, real-time response, and privacy needs |
| title_full_unstemmed | An adaptive load forecasting model in microgrids: A cloud-edge orchestrated approach tailored for accuracy, real-time response, and privacy needs |
| title_short | An adaptive load forecasting model in microgrids: A cloud-edge orchestrated approach tailored for accuracy, real-time response, and privacy needs |
| title_sort | adaptive load forecasting model in microgrids a cloud edge orchestrated approach tailored for accuracy real time response and privacy needs |
| topic | Load forecasting Different types of microgrids Customized AI algorithm Cloud-edge orchestrated architecture |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525000419 |
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