Academic Journal
An FCM based weather type classification method considering photovoltaic output and meteorological characteristics and its application in power interval forecasting
| العنوان: | An FCM based weather type classification method considering photovoltaic output and meteorological characteristics and its application in power interval forecasting |
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| المؤلفون: | Honglu Zhu, Yahui Sun, Tingting Jiang, Xi Zhang, Hai Zhou, Siyu Hu, Mingyuan Kang |
| المصدر: | IET Renewable Power Generation, Vol 18, Iss 2, Pp 238-260 (2024) |
| بيانات النشر: | Wiley, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Renewable energy sources |
| مصطلحات موضوعية: | forecasting theory, solar power, solar power stations, Renewable energy sources, TJ807-830 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1752-1424 1752-1416 |
| Relation: | https://doaj.org/toc/1752-1416; https://doaj.org/toc/1752-1424 |
| DOI: | 10.1049/rpg2.12917 |
| URL الوصول: | https://doaj.org/article/92565244021b4a569875a3b523fdde9a |
| رقم الانضمام: | edsdoj.92565244021b4a569875a3b523fdde9a |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 17521424 17521416 |
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| DOI: | 10.1049/rpg2.12917 |