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Journal of Solar Energy
Volume 2015, Article ID 410684, 13 pages
http://dx.doi.org/10.1155/2015/410684
Research Article

Spatial Approach of Artificial Neural Network for Solar Radiation Forecasting: Modeling Issues

1School of Engineering, Indian Institute of Technology Mandi (IIT Mandi), Room No. 106, Mandi Campus, Mandi 175005, India
2Mechanical Engineering Department, Indian Institute of Technology Roorkee (IITR), Roorkee 247667, India
3School of Computing and Electrical Engineering, Indian Institute of Technology Mandi (IIT Mandi), Mandi 175005, India

Received 25 September 2014; Revised 5 December 2014; Accepted 18 December 2014

Academic Editor: Jayasundera M. S. Bandara

Copyright © 2015 Yashwant Kashyap et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Design of neural networks architecture has been done on setting up the number of neurons, delays, and activation functions. The expected model was initiated and tested with Indian solar horizontal irradiation (GHI) metrological data. The results are assessed using the effect of different statistical errors. The effort is made to verify simulation capability of ANN architecture accurately, on hourly radiation data. ANN model is a well-organized technique to estimate the radiation using different meteorological database. In this paper, we have used nine spatial neighbour locations and 10 years of data for assessment of neural network. Hence, overall 90 different inputs are compared, on customized ANN model. Results show the flexibility with respect to spatial orientation of model inputs.