Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models Amit Kumar Yadav a , Hasmat Malik b , S.S. Chandel a,n a Centre for Energy and Environment, National Institute of Technology, Hamirpur,177005 Himachal Pradesh, India b Department of Electrical Engineering, Indian Institute of Technology Delhi 110016, India article info Article history: Received 9 August 2013 Received in revised form 22 November 2013 Accepted 18 December 2013 Available online 20 January 2014 Keywords: Solar energy Solar potential Solar radiation prediction Artificial neural network WEKA abstract The prediction of solar radiation is important for several applications in renewable energy research. Solar radiation is predicted by a number of solar radiation models both conventional and Artificial Neural Network (ANN) based models. There are a number of meteorological and geographical variables which affect solar radiation prediction, so identification of suitable variables for accurate solar radiation prediction is an important research area. With this main objective, Waikato Environment for Knowledge Analysis (WEKA) software is applied to 26 Indian locations having different climatic conditions to find most influencing input parameters for solar radiation prediction in ANN models. The input parameters identified are latitude, longitude, temperature, maximum temperature, minimum temperature, altitude and sunshine hours for different cities of India. In order to check the prediction accuracy using the identified parameters, three Artificial Neural Network (ANN) models are developed (ANN-1, ANN-2 and ANN-3). The maximum MAPE for ANN-1, ANN-2 and ANN-3 models are found to be 20.12%, 6.89% and 9.04% respectively, showing 13.23% improved prediction accuracy of the ANN-2 model which utilizes temperature, maximum temperature, minimum temperature, height above sea level and sunshine hours as input variables in comparison to the ANN-1 model. The WEKA identifies temperature, maximum temperature, minimum temperature, altitude and sunshine hours as the most relevant input variables and latitude, longitude as the least influencing variables in solar radiation prediction. The methodology is also used to identify the solar energy potential of Western Himalayan state of Himachal Pradesh, India. The results show good solar potential with yearly solar radiation variation as 3.59–5.38 kWh/ m 2 /day for a large number of solar applications including solar power generation in this region. & 2013 Elsevier Ltd. All rights reserved. Contents 1. Introduction ........................................................................................................ 509 2. Literature survey for identification of input parameters for ANN based solar radiation prediction ................................... 510 3. Methodology ....................................................................................................... 512 3.1. Source of solar radiation data .................................................................................... 512 3.2. Input variables selection using WEKA ............................................................................. 512 3.3. Solar radiation prediction models with selected inputs................................................................ 513 4. Results and discussion................................................................................................ 515 4.1. Solar radiation predicted by the ANN-3 model ...................................................................... 517 5. Conclusion ......................................................................................................... 517 Appendix A. MATLAB code for solar radiation using nftool ................................................................... 518 References ............................................................................................................. 518 1. Introduction Solar energy is a clean resource which has a vast potential to meet the energy needs. Solar potential assessment of a region requires information about the measured solar radiation at different locations. The solar radiation components are measured Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/rser Renewable and Sustainable Energy Reviews 1364-0321/$ - see front matter & 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.rser.2013.12.008 n Corresponding author. Tel.: þ91 1972 254748; fax: þ91 1972 223834. E-mail address: sschandel2013@gmail.com (S.S. Chandel). Renewable and Sustainable Energy Reviews 31 (2014) 509–519