The intelligent forecasting of the performances in PV/T collectors based on soft computing method

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@Article{Mojumder:2017:RSER,
  author =       "Juwel Chandra Mojumder and Hwai Chyuan Ong and 
                 Wen Tong Chong and Nima Izadyar and 
                 Shahaboddin Shamshirband",
  title =        "The intelligent forecasting of the performances in
                 PV/T collectors based on soft computing method",
  journal =      "Renewable and Sustainable Energy Reviews",
  volume =       "72",
  pages =        "1366--1378",
  year =         "2017",
  ISSN =         "1364-0321",
  DOI =          "doi:10.1016/j.rser.2016.11.225",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1364032116309972",
  abstract =     "Solar energy has been widely used in various aspects
                 as the greatest promising and pollution free energy
                 comparing with other available resources in nature.
                 Photovoltaic-thermal (PV/T) is the most generative
                 technology, which has been invented to use electrical
                 energy and heat from the solar system. The article
                 presents a novelty of using Extreme Learning Machine
                 (ELM) into the air type PV/T technology. For this
                 purposes, two air type PV/T designs were fabricated and
                 practiced for a cooling fin design in the collector and
                 finally, collected the experimental data, which was
                 adapted to estimate electrical and thermal efficiency
                 for the PV/T system. Then, the results of ELM
                 prediction model were compared with Genetic Programming
                 (GP) and Artificial Neural Networks (ANNs) models. The
                 experimental result was accommodated to improving the
                 predictive accuracy of the ELM approach in comparison.
                 Further, outcome results indicate that developed ELM
                 models can be used satisfactorily to formulate the
                 predictive algorithm for PV/T performances. The ELM
                 algorithm made a good generalization, which can learn
                 very faster comparing with other conventional popular
                 learning algorithms. The results revealed that the
                 improved ELM model is a well fitted tool to predict the
                 thermal and electrical efficiency with higher
                 accuracy.",
  keywords =     "genetic algorithms, genetic programming, Solar energy,
                 Photovoltaic-thermal, Soft computing, Extreme learning
                 machine (ELM), heat gain",
}

Genetic Programming entries for Juwel Chandra Mojumder Hwai Chyuan Ong Wen Tong Chong Nima Izadyar Shahaboddin Shamshirband

Citations