Real-Time AI-Based Power Demand Forecasting for Peak Shaving and Consumption Reduction Using Vehicle-to-Grid and Reused Energy Storage Systems: A Case Study at a Business Center on Jeju Island
The increasing demand for electricity and the environmental challenges associated with traditional fossil fuel-based power generation have accelerated the global transition to renewable energy sources. While renewable energy offers significant advantages, including low carbon emissions and sustainab...
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MDPI AG
2025-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/6/3050 |
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| author | Kibaek Kim Dongwoo Ko Juwon Jung Jeng-Ok Ryu Kyung-Ja Hur Young-Joo Kim |
| author_facet | Kibaek Kim Dongwoo Ko Juwon Jung Jeng-Ok Ryu Kyung-Ja Hur Young-Joo Kim |
| author_sort | Kibaek Kim |
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| description | The increasing demand for electricity and the environmental challenges associated with traditional fossil fuel-based power generation have accelerated the global transition to renewable energy sources. While renewable energy offers significant advantages, including low carbon emissions and sustainability, its inherent variability and intermittency create challenges for grid stability and energy management. This study contributes to addressing these challenges by developing an AI-driven power consumption forecasting system. The core of the proposed system is a multi-cluster long short-term memory model (MC-LSTM), which combines k-means clustering with LSTM neural networks to enhance forecasting accuracy. The MC-LSTM model achieved an overall prediction accuracy of 97.93%, enabling dynamic, real-time demand-side energy management. Furthermore, to validate its effectiveness, the system integrates vehicle-to-grid technology and reused energy storage systems as external energy sources. A real-world demonstration was conducted in a commercial building on Jeju Island, where the AI-driven system successfully reduced total energy consumption by 21.3% through optimized peak shaving and load balancing. The proposed system provides a practical framework for enhancing grid stability, optimizing energy distribution, and reducing dependence on centralized power systems. |
| format | Article |
| id | doaj-art-bb950ef01b5141dea59f9e5c0b65e38c |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-bb950ef01b5141dea59f9e5c0b65e38c2025-08-20T03:40:44ZengMDPI AGApplied Sciences2076-34172025-03-01156305010.3390/app15063050Real-Time AI-Based Power Demand Forecasting for Peak Shaving and Consumption Reduction Using Vehicle-to-Grid and Reused Energy Storage Systems: A Case Study at a Business Center on Jeju IslandKibaek Kim0Dongwoo Ko1Juwon Jung2Jeng-Ok Ryu3Kyung-Ja Hur4Young-Joo Kim5Department of Mechanical Engineering, Yonsei University, Seoul 03722, Republic of KoreaDepartment of Mechanical Engineering, Yonsei University, Seoul 03722, Republic of KoreaDepartment of Mechanical Engineering, Yonsei University, Seoul 03722, Republic of KoreaSmart Grid Business Division/R&D Center, Daekyung Engineering Co., Ltd., Jeju 63309, Republic of KoreaSmart Grid Business Division/R&D Center, Daekyung Engineering Co., Ltd., Jeju 63309, Republic of KoreaDepartment of Mechanical Engineering, Yonsei University, Seoul 03722, Republic of KoreaThe increasing demand for electricity and the environmental challenges associated with traditional fossil fuel-based power generation have accelerated the global transition to renewable energy sources. While renewable energy offers significant advantages, including low carbon emissions and sustainability, its inherent variability and intermittency create challenges for grid stability and energy management. This study contributes to addressing these challenges by developing an AI-driven power consumption forecasting system. The core of the proposed system is a multi-cluster long short-term memory model (MC-LSTM), which combines k-means clustering with LSTM neural networks to enhance forecasting accuracy. The MC-LSTM model achieved an overall prediction accuracy of 97.93%, enabling dynamic, real-time demand-side energy management. Furthermore, to validate its effectiveness, the system integrates vehicle-to-grid technology and reused energy storage systems as external energy sources. A real-world demonstration was conducted in a commercial building on Jeju Island, where the AI-driven system successfully reduced total energy consumption by 21.3% through optimized peak shaving and load balancing. The proposed system provides a practical framework for enhancing grid stability, optimizing energy distribution, and reducing dependence on centralized power systems.https://www.mdpi.com/2076-3417/15/6/3050demand forecastingartificial intelligencevehicle-to-gridreused energy storage systempeak shavingenergy management |
| spellingShingle | Kibaek Kim Dongwoo Ko Juwon Jung Jeng-Ok Ryu Kyung-Ja Hur Young-Joo Kim Real-Time AI-Based Power Demand Forecasting for Peak Shaving and Consumption Reduction Using Vehicle-to-Grid and Reused Energy Storage Systems: A Case Study at a Business Center on Jeju Island Applied Sciences demand forecasting artificial intelligence vehicle-to-grid reused energy storage system peak shaving energy management |
| title | Real-Time AI-Based Power Demand Forecasting for Peak Shaving and Consumption Reduction Using Vehicle-to-Grid and Reused Energy Storage Systems: A Case Study at a Business Center on Jeju Island |
| title_full | Real-Time AI-Based Power Demand Forecasting for Peak Shaving and Consumption Reduction Using Vehicle-to-Grid and Reused Energy Storage Systems: A Case Study at a Business Center on Jeju Island |
| title_fullStr | Real-Time AI-Based Power Demand Forecasting for Peak Shaving and Consumption Reduction Using Vehicle-to-Grid and Reused Energy Storage Systems: A Case Study at a Business Center on Jeju Island |
| title_full_unstemmed | Real-Time AI-Based Power Demand Forecasting for Peak Shaving and Consumption Reduction Using Vehicle-to-Grid and Reused Energy Storage Systems: A Case Study at a Business Center on Jeju Island |
| title_short | Real-Time AI-Based Power Demand Forecasting for Peak Shaving and Consumption Reduction Using Vehicle-to-Grid and Reused Energy Storage Systems: A Case Study at a Business Center on Jeju Island |
| title_sort | real time ai based power demand forecasting for peak shaving and consumption reduction using vehicle to grid and reused energy storage systems a case study at a business center on jeju island |
| topic | demand forecasting artificial intelligence vehicle-to-grid reused energy storage system peak shaving energy management |
| url | https://www.mdpi.com/2076-3417/15/6/3050 |
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