Uncertainty analysis of photovoltaic power generation system and intelligent coupling prediction

Guo-Feng Fan, Yi-Wen Feng, Li-Ling Peng, Hsin-Pou Huang and Wei-Chiang Hong

Renewable Energy, 2024, vol. 234, issue C

Abstract: Accurate prediction of photovoltaic power generation is essential to promoting the active consumption and low-carbon protection. The complex uncertainty of the photovoltaic system itself leads to the deviation in the photovoltaic power prediction. Therefore, we propose a new prediction model for coupled intelligence optimization. First, the photovoltaic power is decomposed into effective mode components using VMD optimized by GWO. Statistical techniques were used to analyze multidimensional uncertainty and extract features, then, optimize the performance of the coupled model. Second, the Zebra optimization (ZOA) establishes an appropriate balance between exploration and utilization to achieve the optimization of the model parameters. In addition, the CNN is used to extract complex features and enhance the correlation between input values and output values. Finally, the power was predicted using the BiLSTM. The results show that applying the statistical technique to the coupled prediction model not only reveals the uncertainty of photovoltaic systems but reduces the prediction error. Among them, the R2 increased by 0.42 %, the values of MAPE, MSE, RMSE, and MAE were reduced to different degrees. It can better optimize the allocation and reasonable consumption of renewable energy, which provides the decision basis for the adjustment of renewable energy structure.

Keywords: Zebra optimization (ZOA); Variational mode decomposition (VMD); Bi-directional long short term memory (BiLSTM); Uncertainty analysis; Coupling prediction (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:234:y:2024:i:c:s0960148124012424

DOI: 10.1016/j.renene.2024.121174

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