Predicting High or Low Transfer Efficiency of Photovoltaic Systems Using a Novel Hybrid Methodology Combining Rough Set Theory, Data Envelopment Analysis and Genetic Programming

Yi-Shian Lee and Lee-Ing Tong
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Yi-Shian Lee: Research Center for Psychological and Educational Testing, National Taiwan Normal University, HePing East Rd., Section 1, Taipei 106, Taiwan
Lee-Ing Tong: Department of Industrial Engineering Management, National Chiao Tung University, 1001 Ta-Hsuch Rd., Hsunchu 300, Taiwan

Energies, 2012, vol. 5, issue 3, 1-16

Abstract: Solar energy has become an important energy source in recent years as it generates less pollution than other energies. A photovoltaic (PV) system, which typically has many components, converts solar energy into electrical energy. With the development of advanced engineering technologies, the transfer efficiency of a PV system has been increased from low to high. The combination of components in a PV system influences its transfer efficiency. Therefore, when predicting the transfer efficiency of a PV system, one must consider the relationship among system components. This work accurately predicts whether transfer efficiency of a PV system is high or low using a novel hybrid model that combines rough set theory (RST), data envelopment analysis (DEA), and genetic programming (GP). Finally, real data-set are utilized to demonstrate the accuracy of the proposed method.

Keywords: photovoltaic systems; rough set theory; data envelopment analysis; genetic programming; hybrid model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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