Analysis of different combinations of meteorological parameters in predicting the horizontal global solar radiation with ANN approach: A case study

D.V. Siva Krishna Rao K, M. Premalatha and C. Naveen

Renewable and Sustainable Energy Reviews, 2018, vol. 91, issue C, 248-258

Abstract: Solar energy is a clean renewable energy source and availability of solar resources at a particular location depends on the local meteorological parameters. In the present study, prediction models using artificial neural networks (ANN) are developed by varying the meteorological parameters from one to six. A two year database of daily global solar radiation (GSR), daily minimum temperature (Tmin), daily maximum temperature (Tmax), difference of daily maximum and minimum temperature (DT), sunshine hours (S), theoretical sunshine hours (So) and extraterrestrial radiation (Ho) have been used to train the ANN. Six ANN models are developed (ANN-1 to ANN-6) with 32 possible combinations of inputs and are used to train the network to identify the best combination of inputs to estimate the monthly mean daily GSR accurately. All the models are validated and the performance of the models are analyzed by using the statistical tools.

Keywords: Artificial neural network; Global solar radiation; Temperature; Sunshine hours; Statistical tools (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:rensus:v:91:y:2018:i:c:p:248-258

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DOI: 10.1016/j.rser.2018.03.096

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