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

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@Article{Lee:2012:Energies,

• author = "Yi-Shian Lee and Lee-Ing Tong",
• title = "Predicting High or Low Transfer Efficiency of Photovoltaic Systems Using a Novel Hybrid Methodology Combining Rough Set Theory, Data Envelopment Analysis and Genetic Programming",
• journal = "Energies",
• year = "2012",
• volume = "5",
• number = "3",
• pages = "545--560",
• publisher = "Molecular Diversity Preservation International",
• keywords = "genetic algorithms, genetic programming, photovoltaic systems, rough set theory, data envelopment analysis, hybrid model",
• ISSN = "1996-1073; 19961073",
• URL = "http://www.mdpi.com/1996-1073/5/3/545/pdf",
• URL = "http://www.mdpi.com/1996-1073/5/3/545/",
• broken = "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=19961073\&date=2012\&volume=5\&issue=3\&spage=545",
• DOI = "doi:10.3390/en5030545",
• size = "16 pages",
• 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 used to demonstrate the accuracy of the proposed method.",
• bibsource = "OAI-PMH server at www.doaj.org",
• language = "eng",
• oai = "oai:doaj-articles:601889dc955f0d7b09d556498b97d8da",

}

Genetic Programming entries for Yi-Shian Lee Lee-Ing Tong

Citations