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Energies 2012, 5(3), 545-560; doi:10.3390/en5030545

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

1
Research Center for Psychological and Educational Testing, National Taiwan Normal University, HePing East Rd., Section 1, Taipei 106, Taiwan
2
Department of Industrial Engineering Management, National Chiao Tung University, 1001 Ta-Hsuch Rd., Hsunchu 300, Taiwan
*
Author to whom correspondence should be addressed.
Received: 24 January 2012 / Revised: 13 February 2012 / Accepted: 20 February 2012 / Published: 27 February 2012
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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. View Full-Text
Keywords: photovoltaic systems; rough set theory; data envelopment analysis; genetic programming; hybrid model photovoltaic systems; rough set theory; data envelopment analysis; genetic programming; hybrid model
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Lee, Y.-S.; Tong, L.-I. Predicting High or Low Transfer Efficiency of Photovoltaic Systems Using a Novel Hybrid Methodology Combining Rough Set Theory, Data Envelopment Analysis and Genetic Programming. Energies 2012, 5, 545-560.

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