Solar Radiation Prediction using Artificial Neural Network

Shanmugapriya, S and Mohammad, Hashif Iqbal (2015) Solar Radiation Prediction using Artificial Neural Network. International Journal of Computer Applications, 116 (16). pp. 29-31. ISSN 0975 – 8887

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It is very important to analyze the influence of solar radiation for most of the application in solar energy research. Despite the great importance of Global Solar Radiation (GSR), the number of radiation stations are very less when compared to the stations that collect regular meteorological data like air temperature and humidity. The main objective of this paper is to study the feasibility of an Artificial Neural Network (ANN) based method to estimate and predict GSR. The studies shows that prediction using ANN are more accurate when compared to conventional models. The accuracy of prediction depends on input parameter combinations and training algorithm. The places that were selected for study was Bangalore, Thiruvananthapuram, Chennai and Hyderabad. Average temperature, maximum temperature, minimum temperature, and altitude were used as the input parameters. The ANN model was developed using ANN fitting tool (nftool) which consists of a standard two layers feed forward neural network trained with Lavenberg-Marquardt (LM) algorithm.

Item Type: Article
Uncontrolled Keywords: Artificial Neural Network, Solar Radiation, Mean Absolute Percentage Error.
Subjects: Engineering > MIT Manipal > Chemical
Depositing User: MIT Library
Date Deposited: 28 Apr 2015 10:32
Last Modified: 28 Apr 2015 10:32

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