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Estimation of GSR to ascertain solar electricity cost in context of deregulated electricity markets

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  • Anamika,
  • Peesapati, Rajagopal
  • Kumar, Niranjan

Abstract

Solar energy is the most preferred among the other renewable energies throughout the world. The cost of Solar Electricity plays key role in deregulated Electricity Markets which gets affected by Global Solar Radiation (GSR). In this research, an integrated technique is used to estimate the mean monthly GSR for the four summer months over 14 Indian cities. The goal of this research work is to extract a significant training data set of the several environmental parameters, used for estimating the GSR through the application of Principal Component Analysis (PCA). Further an estimation of the mean monthly GSR will be completed using the significant training data set through the application of Artificial Neural Networks (ANNs). A multi layered, feed forward, standard ANN is considered in estimating the GSR. The performance of ANN was evaluated while it was combined with the statistical technique by calculating the error between estimated and measured values of GSR. Results show that the proposed model estimates GSR with less error and more appropriate than the other empirical models. This study gives a judgment for engineers and researchers on the installation of solar plants at the best suitable places and helps in minimizing the energy crisis in India.

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  • Anamika, & Peesapati, Rajagopal & Kumar, Niranjan, 2016. "Estimation of GSR to ascertain solar electricity cost in context of deregulated electricity markets," Renewable Energy, Elsevier, vol. 87(P1), pages 353-363.
    • Handle: RePEc:eee:renene:v:87:y:2016:i:p1:p:353-363
    DOI: 10.1016/j.renene.2015.10.038
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