Multi-step multivariate forecasting of transmission power in NPPs using operational and meteorological data
As the proportion of renewable energy has increased in the national power grid of Republic of Korea, various efforts are needed to maintain the stability of total power generation. All kinds of power plants, including nuclear power, must notify the grid operation organization of their expected trans...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Nuclear Engineering and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1738573324004170 |
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| author | Jaeseok Yoo Young-jin Oh Nam-hyun Kim Soo-ill Lee Jaepil Ko |
| author_facet | Jaeseok Yoo Young-jin Oh Nam-hyun Kim Soo-ill Lee Jaepil Ko |
| author_sort | Jaeseok Yoo |
| collection | DOAJ |
| description | As the proportion of renewable energy has increased in the national power grid of Republic of Korea, various efforts are needed to maintain the stability of total power generation. All kinds of power plants, including nuclear power, must notify the grid operation organization of their expected transmission power. Even in NPPs, the accuracy of transmission power forecasting can increase the plant owner's economic benefits as well as the stability of the power grid. The transmission power of a NPP is affected by various plant conditions and environmental conditions, including the temperature of circulating water (sea water). In this study, we explored how to effectively handle the long-term dependence problem and various data characteristics to increase the forecasting accuracy of transmission power in NPPs by introducing a Seq2Seq model with an encoder-decoder structure and an attention mechanism, beyond traditional time series deep learning models, especially LSTM. This approach will improve the accuracy of transmission power forecasting and contribute to a stable power supply. Additionally, the model is expected to provide a realistic and practical solution for the power demand response of power plants. |
| format | Article |
| id | doaj-art-b2a8f381c0df4923986000ec0a37782f |
| institution | Kabale University |
| issn | 1738-5733 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Nuclear Engineering and Technology |
| spelling | doaj-art-b2a8f381c0df4923986000ec0a37782f2025-01-31T05:10:58ZengElsevierNuclear Engineering and Technology1738-57332025-02-01572103169Multi-step multivariate forecasting of transmission power in NPPs using operational and meteorological dataJaeseok Yoo0Young-jin Oh1Nam-hyun Kim2Soo-ill Lee3Jaepil Ko4Smart Convergence Research Department, KEPCO Engineering and Construction Co., Ltd., Republic of Korea; Department of Computer Engineering, Kumoh National Institute of Technology, Republic of KoreaSmart Convergence Research Department, KEPCO Engineering and Construction Co., Ltd., Republic of Korea; Corresponding author.Digital Plant Technology Group, KHNP Central Research Institute, Republic of KoreaDigital Plant Technology Group, KHNP Central Research Institute, Republic of KoreaDepartment of Computer Engineering, Kumoh National Institute of Technology, Republic of Korea; Corresponding author.As the proportion of renewable energy has increased in the national power grid of Republic of Korea, various efforts are needed to maintain the stability of total power generation. All kinds of power plants, including nuclear power, must notify the grid operation organization of their expected transmission power. Even in NPPs, the accuracy of transmission power forecasting can increase the plant owner's economic benefits as well as the stability of the power grid. The transmission power of a NPP is affected by various plant conditions and environmental conditions, including the temperature of circulating water (sea water). In this study, we explored how to effectively handle the long-term dependence problem and various data characteristics to increase the forecasting accuracy of transmission power in NPPs by introducing a Seq2Seq model with an encoder-decoder structure and an attention mechanism, beyond traditional time series deep learning models, especially LSTM. This approach will improve the accuracy of transmission power forecasting and contribute to a stable power supply. Additionally, the model is expected to provide a realistic and practical solution for the power demand response of power plants.http://www.sciencedirect.com/science/article/pii/S1738573324004170Transmission powerMulti-step multivariate time series forecastingSequence to sequenceAttention mechanismOperational dataMeteorological data |
| spellingShingle | Jaeseok Yoo Young-jin Oh Nam-hyun Kim Soo-ill Lee Jaepil Ko Multi-step multivariate forecasting of transmission power in NPPs using operational and meteorological data Nuclear Engineering and Technology Transmission power Multi-step multivariate time series forecasting Sequence to sequence Attention mechanism Operational data Meteorological data |
| title | Multi-step multivariate forecasting of transmission power in NPPs using operational and meteorological data |
| title_full | Multi-step multivariate forecasting of transmission power in NPPs using operational and meteorological data |
| title_fullStr | Multi-step multivariate forecasting of transmission power in NPPs using operational and meteorological data |
| title_full_unstemmed | Multi-step multivariate forecasting of transmission power in NPPs using operational and meteorological data |
| title_short | Multi-step multivariate forecasting of transmission power in NPPs using operational and meteorological data |
| title_sort | multi step multivariate forecasting of transmission power in npps using operational and meteorological data |
| topic | Transmission power Multi-step multivariate time series forecasting Sequence to sequence Attention mechanism Operational data Meteorological data |
| url | http://www.sciencedirect.com/science/article/pii/S1738573324004170 |
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