A Novel Ultra-Short-Term PV Power Forecasting Method Based on DBN-Based Takagi-Sugeno Fuzzy Model
In: Energies; Volume 14; Issue 20; Pages: 6447, Jg. 14 (2021-10-09), Heft 6447, p 6447
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Zugriff:
Forecasting uncertainties limit the development of photovoltaic (PV) power generation. New forecasting technologies are urgently needed to improve the accuracy of power generation forecasting. In this paper, a novel ultra-short-term PV power forecasting method is proposed based on a deep belief network (DBN)-based Takagi-Sugeno (T-S) fuzzy model. Firstly, the correlation analysis is used to filter redundant information. Furthermore, a T-S fuzzy model, which integrates fuzzy c-means (FCM) for the fuzzy division of input variables and DBN for fuzzy subsets forecasting, is developed. Finally, the proposed method is compared to a benchmark DBN method and the T-S fuzzy model in case studies. The numerical results show the feasibility and flexibility of the proposed ultra-short-term PV power forecasting approach.
Titel: |
A Novel Ultra-Short-Term PV Power Forecasting Method Based on DBN-Based Takagi-Sugeno Fuzzy Model
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Autor/in / Beteiligte Person: | Liu, Ling ; Zheng, Yuling ; Liu, Fang |
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Zeitschrift: | Energies; Volume 14; Issue 20; Pages: 6447, Jg. 14 (2021-10-09), Heft 6447, p 6447 |
Veröffentlichung: | Multidisciplinary Digital Publishing Institute, 2021 |
Medientyp: | unknown |
ISSN: | 1996-1073 (print) |
DOI: | 10.3390/en14206447 |
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