Stochastic weather simulation based on gate recurrent unit and generative adversarial networks
Abstract The weather has a significant impact on power load and power system planning. Stochastic weather simulation is important in the field of power systems. However, due to factors such as long recording years, observation technology, and so on, the historical weather data often have the problem...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-11-01
|
| Series: | IET Power Electronics |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/pel2.12750 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850126999542038528 |
|---|---|
| author | Lingling Han Xueqian Fu Xinyue Chang Yixuan Li Xiang Bai |
| author_facet | Lingling Han Xueqian Fu Xinyue Chang Yixuan Li Xiang Bai |
| author_sort | Lingling Han |
| collection | DOAJ |
| description | Abstract The weather has a significant impact on power load and power system planning. Stochastic weather simulation is important in the field of power systems. However, due to factors such as long recording years, observation technology, and so on, the historical weather data often have the problem of missing or insufficient. Meteorological data are characterized by changeable, rapid change, and high dimensions. Therefore, it is a challenging task to accurately grasp the law of weather data. This article presents a random weather simulation model based on gate recurrent unit (GRU) and generative adversarial networks (GAN). GRU selectively learns or forgets what was in the previous moment during training; it can learn the previous and current data of the time series data. When combined with the GAN, it will produce data with the same distribution as the original weather data. The proposed method was evaluated on a real weather dataset, and the results show that the proposed method outperforms the other contrast algorithms. |
| format | Article |
| id | doaj-art-cb5bf010e35e4ed69eed6be511fe5479 |
| institution | OA Journals |
| issn | 1755-4535 1755-4543 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Power Electronics |
| spelling | doaj-art-cb5bf010e35e4ed69eed6be511fe54792025-08-20T02:33:47ZengWileyIET Power Electronics1755-45351755-45432024-11-0117152331234310.1049/pel2.12750Stochastic weather simulation based on gate recurrent unit and generative adversarial networksLingling Han0Xueqian Fu1Xinyue Chang2Yixuan Li3Xiang Bai4College of Information and Electrical Engineering China Agricultural University Beijing ChinaCollege of Information and Electrical Engineering China Agricultural University Beijing ChinaShanxi Energy Internet Research Institute Taiyuan ChinaShanxi Energy Internet Research Institute Taiyuan ChinaShanxi Energy Internet Research Institute Taiyuan ChinaAbstract The weather has a significant impact on power load and power system planning. Stochastic weather simulation is important in the field of power systems. However, due to factors such as long recording years, observation technology, and so on, the historical weather data often have the problem of missing or insufficient. Meteorological data are characterized by changeable, rapid change, and high dimensions. Therefore, it is a challenging task to accurately grasp the law of weather data. This article presents a random weather simulation model based on gate recurrent unit (GRU) and generative adversarial networks (GAN). GRU selectively learns or forgets what was in the previous moment during training; it can learn the previous and current data of the time series data. When combined with the GAN, it will produce data with the same distribution as the original weather data. The proposed method was evaluated on a real weather dataset, and the results show that the proposed method outperforms the other contrast algorithms.https://doi.org/10.1049/pel2.12750learning (artificial intelligence)photovoltaic cellsphotovoltaic power systemssampled data systems |
| spellingShingle | Lingling Han Xueqian Fu Xinyue Chang Yixuan Li Xiang Bai Stochastic weather simulation based on gate recurrent unit and generative adversarial networks IET Power Electronics learning (artificial intelligence) photovoltaic cells photovoltaic power systems sampled data systems |
| title | Stochastic weather simulation based on gate recurrent unit and generative adversarial networks |
| title_full | Stochastic weather simulation based on gate recurrent unit and generative adversarial networks |
| title_fullStr | Stochastic weather simulation based on gate recurrent unit and generative adversarial networks |
| title_full_unstemmed | Stochastic weather simulation based on gate recurrent unit and generative adversarial networks |
| title_short | Stochastic weather simulation based on gate recurrent unit and generative adversarial networks |
| title_sort | stochastic weather simulation based on gate recurrent unit and generative adversarial networks |
| topic | learning (artificial intelligence) photovoltaic cells photovoltaic power systems sampled data systems |
| url | https://doi.org/10.1049/pel2.12750 |
| work_keys_str_mv | AT linglinghan stochasticweathersimulationbasedongaterecurrentunitandgenerativeadversarialnetworks AT xueqianfu stochasticweathersimulationbasedongaterecurrentunitandgenerativeadversarialnetworks AT xinyuechang stochasticweathersimulationbasedongaterecurrentunitandgenerativeadversarialnetworks AT yixuanli stochasticweathersimulationbasedongaterecurrentunitandgenerativeadversarialnetworks AT xiangbai stochasticweathersimulationbasedongaterecurrentunitandgenerativeadversarialnetworks |