期刊
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
卷 45, 期 1, 页码 6-35出版社
WILEY
DOI: 10.1002/er.5608
关键词
adaptive neuro fuzzy interface system; artificial intelligence techniques; artificial neural network; fuzzy logic; genetic algorithm; performance; solar photovoltaic system
资金
- Dong-A University research fund
This article reviews the application of artificial intelligence techniques in solar photovoltaic systems, focusing on design, modeling, fault detection, and output prediction. A total of 122 articles from 2009 to 2019 are analyzed, showing the suitability and reliability of ANN, FL, GA, and hybrid models for accurate prediction of solar radiation and system performance characteristics.
The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.
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