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Photovoltaic Power Probabilistic Prediction Based on BRICH Clustering and Copula-Monte Carlo Simulation
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Mu-tao HUANG1a, Su-hua GAO1b, Yang WANG2, Ling-kang ZENG2, Cong-ying WEI2, Xing-bang CHEN1a
Water Resources and Power | 2023, 41(12) : 220 - 224
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Water Resources and Power | 2023, 41(12): 220-224
• ENERGY •
Photovoltaic Power Probabilistic Prediction Based on BRICH Clustering and Copula-Monte Carlo Simulation
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Mu-tao HUANG1a, Su-hua GAO1b, Yang WANG2, Ling-kang ZENG2, Cong-ying WEI2, Xing-bang CHEN1a
Affiliations
  • 1a.School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
  • 1b.School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
  • 2.Dispatching and Control Center of Central China Branch of State Grid Corporation of China, Wuhan 430077, China
Published: 2023-12-25 doi: 10.20040/j.cnki.1000-7709.2023.20230949
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Photovoltaic power generation is affected by the chaotic characteristics of meteorology, and its stochastic, volatile and intermittent characteristics affect the operation of power systems seriously. Aiming at the problem of large dimension of original PV power generation data and the vulnerability of power generation to weather conditions, a data processing method based on Principal Component Analysis (PCA) and BRICH clustering was proposed to reduce the dimensionality of model input variables and facilitate statistical modeling. Secondly, a Copula-Monte Carlo-based probabilistic PV power probabilistic prediction model was constructed to calculate the probabilistic interval prediction of PV power output given the future point prediction values. The model was evaluated based on interval coverage and average width of prediction interval. Finally, the summer data of the actual photovoltaic power station were taken as an example for verification analysis. The results show that the Copula-Monte Carlo method can intuitively show the fluctuation range and expected value of photovoltaic power generation, and is superior to other power prediction models.

probabilistic prediction  /  PV power generation  /  BRICH clustering  /  Copula function  /  Monte Carlo simulation  /  weather fractal
Mu-tao HUANG, Su-hua GAO, Yang WANG, Ling-kang ZENG, Cong-ying WEI, Xing-bang CHEN. Photovoltaic Power Probabilistic Prediction Based on BRICH Clustering and Copula-Monte Carlo Simulation[J]. Water Resources and Power, 2023 , 41 (12) : 220 -224 . DOI: 10.20040/j.cnki.1000-7709.2023.20230949
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Year 2023 volume 41 Issue 12
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Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20230949
  • Receive Date：2023-06-04
  • Online Date：2026-01-28
  • Published：2023-12-25
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  • Received：2023-06-04
  • Revised：2023-07-13
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Affiliations
    1a.School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    1b.School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    2.Dispatching and Control Center of Central China Branch of State Grid Corporation of China, Wuhan 430077, China
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https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20230949
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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