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

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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)

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.

Suggested Citation

  • Yi-Shian Lee & Lee-Ing Tong, 2012. "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, MDPI, vol. 5(3), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:5:y:2012:i:3:p:545-560:d:16342
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    References listed on IDEAS

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