A Novel Electric Power Plants Performance Assessment Technique Based on Genetic Programming Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Ahmad Attari Ghomi and Ayyub Ansarinejad and 
                 Hamid Razaghi and Davood Hafezi and Morteza Barazande",
  title =        "A Novel Electric Power Plants Performance Assessment
                 Technique Based on Genetic Programming Approach",
  publisher =    "Canadian Center of Science and Education",
  journal =      "Modern Applied Science",
  year =         "2014",
  number =       "3",
  volume =       "8",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1913-1844; 1913-1852",
  bibsource =    "OAI-PMH server at doaj.org",
  identifier =   "1913-1844; 1913-1852; 10.5539/mas.v8n3p43",
  language =     "English",
  oai =          "oai:doaj.org/article:b014fb4bffa34393b358de0db9db0008",
  pages =        "43",
  rights =       "CC BY",
  URL =          "http://www.ccsenet.org/journal/index.php/mas/article/view/35890",
  DOI =          "doi:10.5539/mas.v8n3p43",
  abstract =     "This paper presents a novel nonparametric efficiency
                 analysis technique based on the Genetic Programming
                 (GP) in order to measure efficiency of Iran electric
                 power plants. GP model was used to predict the output
                 of power plants with respect to input data. The method,
                 we presented here, is capable of finding a best
                 performance among power plant based on the set of input
                 data, GP predicted results and real outputs. The
                 advantage of using GP over traditional statistical
                 methods is that in prediction with GP, the researcher
                 does not need to assume the data characteristic of the
                 dependent variable or output and the independent
                 variable or input. In this proposed methodology to
                 calculate the efficiency scores, a novel algorithm was
                 introduced which worked on the basis of predicted and
                 real output values. To validate our model, the results
                 of proposed algorithm for calculating efficiency rank
                 of power plants were compared to traditional method.
                 Real data was presented for illustrative our proposed
                 methodology. Results showed that by using the
                 capability of input-output pattern recognition of GP,
                 this method provides more realistic results and
                 outperform in identification of efficient units than
                 the conventional methods.",

Genetic Programming entries for Ahmad Attari Ghomi Ayyub Ansarinejad Hamid Razaghi Davood Hafezi Morteza Barazande