An FCM based weather type classification method considering photovoltaic output and meteorological characteristics and its application in power interval forecasting
Abstract With the increasing installation of photovoltaic (PV) systems, the impact of their randomness and volatility on power system has become a significant concern. To effectively quantify the uncertainty of PV output, it is crucial to develop reliable PV power interval forecasting technology. Ho...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-02-01
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| Series: | IET Renewable Power Generation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rpg2.12917 |
| _version_ | 1797326164061061120 |
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| author | Honglu Zhu Yahui Sun Tingting Jiang Xi Zhang Hai Zhou Siyu Hu Mingyuan Kang |
| author_facet | Honglu Zhu Yahui Sun Tingting Jiang Xi Zhang Hai Zhou Siyu Hu Mingyuan Kang |
| author_sort | Honglu Zhu |
| collection | DOAJ |
| description | Abstract With the increasing installation of photovoltaic (PV) systems, the impact of their randomness and volatility on power system has become a significant concern. To effectively quantify the uncertainty of PV output, it is crucial to develop reliable PV power interval forecasting technology. However, the complex relationship between PV output and meteorological factors makes it challenging for a single forecasting model to meet the forecasting demand. To solve this problem, this paper proposes a weather classification method that takes into account both PV output and meteorological characteristics. Initially, the relationship between PV output and meteorological factors is analyzed, and weather types are classified using fuzzy c‐means algorithm (FCM). Then, an extreme learning machine (ELM) is employed to establish point forecasting model. By combining kernel density estimation, a PV power generation interval forecasting model is derived. The results demonstrate that the FCM‐ELM model achieves higher forecasting accuracy and narrower interval width compared to traditional ELM models, with accuracy improvement of more than 2%. Additionally, the proposed method outperforms seasonal models with an accuracy improvement of more than 1%. The contribution of this paper includes identifying the limitations of traditional weather classification methods, proposing a novel multi‐model approach for PV interval forecasting. |
| first_indexed | 2024-03-08T06:19:36Z |
| format | Article |
| id | doaj.art-92565244021b4a569875a3b523fdde9a |
| institution | Directory Open Access Journal |
| issn | 1752-1416 1752-1424 |
| language | English |
| last_indexed | 2024-03-08T06:19:36Z |
| publishDate | 2024-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj.art-92565244021b4a569875a3b523fdde9a2024-02-04T08:16:58ZengWileyIET Renewable Power Generation1752-14161752-14242024-02-0118223826010.1049/rpg2.12917An FCM based weather type classification method considering photovoltaic output and meteorological characteristics and its application in power interval forecastingHonglu Zhu0Yahui Sun1Tingting Jiang2Xi Zhang3Hai Zhou4Siyu Hu5Mingyuan Kang6State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing ChinaChina Electric Power Research Institute Nanjing ChinaChina Electric Power Research Institute Nanjing ChinaBeijing Sifang Automation Co.,Ltd Beijing ChinaAbstract With the increasing installation of photovoltaic (PV) systems, the impact of their randomness and volatility on power system has become a significant concern. To effectively quantify the uncertainty of PV output, it is crucial to develop reliable PV power interval forecasting technology. However, the complex relationship between PV output and meteorological factors makes it challenging for a single forecasting model to meet the forecasting demand. To solve this problem, this paper proposes a weather classification method that takes into account both PV output and meteorological characteristics. Initially, the relationship between PV output and meteorological factors is analyzed, and weather types are classified using fuzzy c‐means algorithm (FCM). Then, an extreme learning machine (ELM) is employed to establish point forecasting model. By combining kernel density estimation, a PV power generation interval forecasting model is derived. The results demonstrate that the FCM‐ELM model achieves higher forecasting accuracy and narrower interval width compared to traditional ELM models, with accuracy improvement of more than 2%. Additionally, the proposed method outperforms seasonal models with an accuracy improvement of more than 1%. The contribution of this paper includes identifying the limitations of traditional weather classification methods, proposing a novel multi‐model approach for PV interval forecasting.https://doi.org/10.1049/rpg2.12917forecasting theorysolar powersolar power stations |
| spellingShingle | Honglu Zhu Yahui Sun Tingting Jiang Xi Zhang Hai Zhou Siyu Hu Mingyuan Kang An FCM based weather type classification method considering photovoltaic output and meteorological characteristics and its application in power interval forecasting IET Renewable Power Generation forecasting theory solar power solar power stations |
| title | An FCM based weather type classification method considering photovoltaic output and meteorological characteristics and its application in power interval forecasting |
| title_full | An FCM based weather type classification method considering photovoltaic output and meteorological characteristics and its application in power interval forecasting |
| title_fullStr | An FCM based weather type classification method considering photovoltaic output and meteorological characteristics and its application in power interval forecasting |
| title_full_unstemmed | An FCM based weather type classification method considering photovoltaic output and meteorological characteristics and its application in power interval forecasting |
| title_short | An FCM based weather type classification method considering photovoltaic output and meteorological characteristics and its application in power interval forecasting |
| title_sort | fcm based weather type classification method considering photovoltaic output and meteorological characteristics and its application in power interval forecasting |
| topic | forecasting theory solar power solar power stations |
| url | https://doi.org/10.1049/rpg2.12917 |
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