Performance ratio prediction of photovoltaic pumping system based on grey clustering and second curvelet neural network

Bin Zhao, Yi Ren, Diankui Gao and Lizhi Xu

Energy, 2019, vol. 171, issue C, 360-371

Abstract: Performance ratio is an important parameter of measuring the quality and efficiency of photovoltaic (PV) pumping system, which should be predicted correctly to provide guidance for regulating the measurements of reducing losses of every part, therefore this research proposed a prediction model of performance ratio of PV pumping system based on grey clustering and second curvelet neural network. The meaning of performance ratio is analyzed and the main affecting factors of the performance level for PV pumping system are also summarized. The second curvelet neural network is constructed combing the second curvelet transform and feed forward neural network, and the structure of second curvelet neural network is designed. The classification of training and testing samples are confirmed based on the improved grey clustering model, and the corresponding mathematical models are studied. The firefly algorithm is used to optimize the second curvelet neural network. The grey classifications of training samples are confirmed based on grey clustering, which are used to train the second curvelet neural network with different structure optimized by firefly with different parameters, and then the testing samples are used to carry out prediction analysis. Simulation results show that the second curvelet neural network has highest prediction precision and efficiency, which can correctly and efficiently predict the performance ratio of PV pumping system.

Keywords: Performance ratio; PV pumping system; Second curvelet neural network; Grey clustering (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:171:y:2019:i:c:p:360-371

DOI: 10.1016/j.energy.2019.01.028

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