Abstract
Solar radiation data is extremely useful for utilizing solar energy in applications like solar power plants and solar heating. As the fossil fuel resources are degrading, renewable sources of energy like solar energy can reduce our dependence on fossil fuels. Solar energy is also a clean form of energy. In this paper, we have made use of Artificial Neural Network (ANN) for Solar Radiation Prediction (SRP). The ANN network used is Feed Forward with Backpropagation (FFBP), Backpropagation being the learning algorithm. A three-layer network has been used with one hidden layer. The data used was from 67 cities in India, which was further divided in two sets, namely—training and testing. The testing data was not used to train the network. There were 19 input parameters for the network with one output parameter solar radiation.
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References
Azadeh, A., Maghsoudi, A., Sohrabkhani, S.: Using an integrated artificial neural networks model for predicting global radiation: the case study of Iran. Energy Convers. Manag. 50(6), 1497–1505 (2009)
Mellit, A., et al.: An adaptive model for predicting of global, direct and diffuse hourly solar irradiance. Energy Convers. Manag. 51, 771–782 (2010)
Hasni, A., et al.: Estimating global solar radiation using artificial neural network and climate data in the south-western region of Algeria. Energy Procedia 18, 531–537 (2012)
Yadav, A.K., Malik, H., Chandel, S.S.: Application of rapid miner in ANN based prediction of solar radiation for assessment of solar energy resource potential of 76 sites in Northwestern India. Renew. Sustain. Energy Rev. 52, 1093–1106 (2015)
https://eosweb.larc.nasa.gov–Atmospheric Science Data Center (ASDC) at NASA Langley Research Center
Malik, H., Mishra, S.: Artificial neural network and empirical mode decomposition based imbalance fault diagnosis of wind turbine using TurbSim, FAST and simulink. IET Renew. Power Gener. 11(6), 889–902 (2017). https://doi.org/10.1049/iet-rpg.2015.0382
Yadav, A.K., Malik, H., Chandel, S.S.: Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models. Renew. Sustain. Energy Rev. 31, 509–519 (2014). https://doi.org/10.1016/j.rser.2013.12.008
Yadav, A.K., Sharma, V., Malik, H., Chandel, S.S.: Daily array yield prediction of grid-interactive photovoltaic plant using relief attribute evaluator based radial basis function neural network. Renew. Sustain. Energy Rev. 81(2), 2115–2127 (2018). https://doi.org/10.1016/j.rser.2017.06.023
Malik, H., Sharma, R.: EMD and ANN based intelligent fault diagnosis model for transmission line. J. Intell. Fuzzy Sys. 32(4), 3043–3050 (2017). https://doi.org/10.3233/JIFS-169247
Malik, H., Yadav, A.K., Mishra, S., Mehto, T.: Application of neuro-fuzzy scheme to investigate the winding insulation paper deterioration in oil-immersed power transformer. Electr. Power Energy Sys. 53, 256–271 (2013). https://doi.org/10.1016/j.ijepes.2013.04.023
Arora, P., Malik, H., Sharma, R.: Wind speed forecasting model for Northern-Western region of India using decision tree and multi layer perceptron neural network approach. Interdis. Environ. Rev. 19(1), 13–30 (2018). https://doi.org/10.1504/IER.2018.089766
Yadav, A.K., Malik, H., Mittal, A.P.: Artificial neural network fitting tool based prediction of solar radiation for identifying solar power potential. J. Electr. Eng. 15(2), 25–29 (2015)
Yadav, A.K., Singh, A., Malik, H., Azeem, A.: Cost analysis of transformer’s main material weight with artificial neural network (ANN). In: Proceedings IEEE International Conference on Communication System’s Network Technologies, pp. 184–187 (2011). https://doi.org/10.1109/csnt.2011.46
Yadav, A.k., Singh, A., Malik, H., Azeem, A., Rahi, O.P.: Application research based on artificial neural network (ANN) to predict no load loss for transformer design. In: Proceedings IEEE International Conference on Communication System’s Network Technologies, pp. 180–183 (2011). https://doi.org/10.1109/csnt.2011.45
Rahi, O.P., Yadav, A.K., Malik, H., Azeem, A., Bhupesh, K.: Power system voltage stability assessment through artificial neural network. Elsevier Procedia Eng. 30, 53–60 (2012). https://doi.org/10.1016/j.proeng.2012.01.833
Yadav, A.K., Malik, H.: Comparison of different artificial neural network techniques in prediction of solar radiation for power generation using different combinations of meterological variables. In: Proceeding IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), pp. 1–5 (2014). https://doi.org/10.1109/pedes.2014.7042063
Yadav, A.K., Malik, H., Chandel, S.S.: ANN based prediction of daily global solar radiation for photovoltaics applications. In: Proceeding IEEE India Annual Conference (INDICON), pp. 1–5 (2015). https://doi.org/10.1109/indicon.2015.7443186
Malik, H.: Application of Artificial Neural Network for Long Term Wind Speed Prediction. In: Proceeding IEEE CASP-2016, pp. 217–222, 9–11 June 2016. https://doi.org/10.1109/casp.2016.7746168
Azeem, A., Kumar, G., Malik, H.: Artificial neural network based intelligent model for wind power assessment in India. In: Proceedings IEEE PIICON-2016, pp. 1–6, 25–27 Nov 2016. https://doi.org/10.1109/poweri.2016.8077305
Saad, S., Malik, H.: Selection of most relevant input parameters using WEKA for artificial neural network based concrete compressive strength prediction model. In: IEEE PIICON-2016, pp. 1–6, 25–27 Nov 2016. https://doi.org/10.1109/poweri.2016.8077368
Azeem, A., Kumar, G., Malik, H.: Application of Waikato environment for knowledge analysis based artificial neural network models for wind speed forecasting. In: Proceedings IEEE PIICON-2016, pp. 1–6, 25–27 Nov 2016. https://doi.org/10.1109/poweri.2016.8077352
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Malik, H., Garg, S. (2019). Long-Term Solar Irradiance Forecast Using Artificial Neural Network: Application for Performance Prediction of Indian Cities. In: Malik, H., Srivastava, S., Sood, Y., Ahmad, A. (eds) Applications of Artificial Intelligence Techniques in Engineering . Advances in Intelligent Systems and Computing, vol 697. Springer, Singapore. https://doi.org/10.1007/978-981-13-1822-1_26
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