Artificial Neural Networks Based Solar Radiation Estimation using Backpropagation Algorithm

Amar Choudhary, Deependra Pandey, Saurabh Bhardwaj

Abstract


Being at the cutting edge for a long time, solar energy has found several applications in various areas. Optimal harvesting of solar energy is one of the thrust areas of the researchers and developers in 21st century. Solar energy optimization solely depends on radiation received by the solar panels. Radiation is measured by various devices and it may be estimated by various estimation models. Solar energy needs to be estimated well in advance if the system to be designed has to be completely dependent on solar energy. This is a very challenging job since solar radiation depends on several parameters such as location changes and seasonal changes. Artificial Neural Network is one of the most preferred technique for the researchers in prediction related cases. This paper proposes a methodology to estimate solar radiation using feed forward back propagation neural network. A three-layer neural network is used here with one hidden layer. The data of 14 stations of Uttar Pradesh, India are obtained data from FAO, UN and it further divided into three sets of ‘Training’, Validation’ and ‘Testing’.  This study is based on nine input parameters and one output parameter. Feed forward back propagation is used here to estimate solar radiation. The proposed model is validated for Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient training algorithms. The obtained results of Mean Square Error (MSE), Regression Values (R), Slope Values (m) and Intercept Values (c) in all three cases are justifying the suitability of the proposed model.


Keywords


Solar Radiation; Artificial Neural Network; Machine Learning; Renewable and Sustainable Energy

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References


D. Pandey, A. Choudhary, “A Review of Potential, Generation and Factors of Solar Energyâ€, Journals of Thermal Engineering and Applications, STM journals, Volume 5, Issue 3, ISSN: 2349-8994 (Online) (2018), DOI: 10.37591/jotea.v5i2.1035

K. Kapoor, K. K. Pandey, A. K. Jain and A. Nandan, “Evolution of Solar Energy in India: A reviewâ€, Renewable and Sustainable Reviews, 40, 475-485 (2014).

A. B. Jemma, S. Rafa, N. Essounbouli, A. Hamazaoui, F. Hnaien, F. Yalaoui, “Estimation of Global Solar Radiation using Three Simple Methodsâ€, ELSEVIER, ScienceDirect, Energy Procedia, 42, 406-415 (2013). Jemaa, A. B., Rafa, S., Essounbouli, N., Hamzaoui, A., Hnaien, F., & Yalaoui, F., “Estimation of Global Solar Radiation Using Three Simple Methodsâ€, Energy Procedia, 42, 406–415, (2013), DOI :10.1016/j.egypro.2013.11.041

S. L. Goh, M. Chen, D. H. Popović, K. Aihara, D. Obradovic, D. P. Mandic, “Complex-valued forecasting of wind profile. Renewable Energyâ€, 31(11), 1733–1750 (2006). DOI: 10.1016/j.renene.2005.07.006

J. Mubiru, E. J. K. B. Banda, “Estimation of monthly average daily global solar irradiation using ANNâ€, ScienceDirect (Elsevier), Solar Energy, 82, 181-187 (2008).

A. K. Yadav, S. S. Chandel, “Artificial Neural Network based Prediction of Solar Radiation for Indian Stationsâ€, International Journal of Computer Applications (0975-8887), volume 50- No. 9 (2012).

H. Malik, S. Garg, “Long-Term Solar Irradiance Forecast Using Artificial Neural Network: Application for Performance Prediction of Indian Citiesâ€, Advances in Intelligent Systems and Computing, 697, (2019), DOI: 10.1007/978-981-13-18221-1_26

A. Kumar, R. Khatri, “Solar Energy Prediction Using Backpropagation in Artificial Neural Networksâ€, International Conference on Advanced Computing Networking and Informatics, Advances in Intelligent Systems and Computing, 870, (2019), DOI:10.1007/978-981-13-2673-8_4

D. Pandey, A. Choudhary, S. Bahrdwaj, “Overview of Solar Radiation Estimation Techniques with Development of Solar Radiation Model Using Artificial Neural Networkâ€, Advances in Science, Technology and Engineering Systems Journal Vol. 5, No. 4, 589-593 (2020), DOI: 10.25046/aj050469

M. Yesilbudak , M. Colak, R. Bayindir, “What are the Current Status and Future Prospects in Solar Irradiance and Solar Power Forecasting?â€, International Journal of Renewable Energy Research, Vol. 8, No. 1, pp. 635-648, March 2018

M. Ueshima, K. Yuasa and I. Omura, "Examination of Correction Method of Long-term Solar Radiation Forecasts of Numerical Weather Prediction." 8th International Conference on Renewable Energy Research and Applications (ICRERA), pp. 113-117, IEEE, 2019.

M. Colak, , M. Yesilbudak, R. Bayindir, “Forecasting of Daily Total Horizontal Solar Radiation Using Grey Wolf Optimizer and Multilayer Perceptron Algorithmsâ€, 8th International Conference on Renewable Energy Research and Applications (ICRERA), (pp. 939-942, IEEE, 2019.

A. Alkholidi, H. Hamam, “Solar Energy Potentials in Southeastern European Countries: A Case Studyâ€, International Journal of Smart Grid, Vol. 3, No. 2, pp. 109-119, June 2019.

R. AI-Hajj, A. Ali, M. M. Fouad, "Forecasting Solar Radiation Strength Using Machine Learning Ensemble.", 7th International Conference on Renewable Energy Research and Applications (ICRERA), pp. 184-188. IEEE, 2018.

M. M. Rafique, "Design and economic evaluation of a solar household electrification system." International Journal of Smart Grid, Vol. 2, no. 2, pp. 135-141, 2018.

N. Tohru, "Complex-valued neural networks: utilizing high-dimensional parameters" (2009).

M. Benghanem, A. Mellit, S. N. Alamri, "ANN-based modeling and estimation of daily global solar radiation data: A case study", Energy Conversion and Management (Elsevier), 50, 1644-1655, (2009), DOI: 10.1016/j.enconman.2009.03.035

S. S. D. Atsu, A . J. Joseph, A. I. L. Ali, “Solar Radiation Estimation Using Artificial Neural Networksâ€, Applied Energy, 307-319 (2002), DOI: 10.1016/s0306-2619(02)00016-8

A. K. Yadav, S. S. Chandel, “Solar Radiation Prediction using Artificial Neural Network Techniques: A Reviewâ€, Renewable and Sustainable Energy Reviews, 33, 772-781, (2013), DOI: 10.1016/j.rser.2013.08.055

Rajesh Kumar, R K Aggarwal, J D Sharma, “Solar Radiation Estimation Using Artificial Neural Network: A Reviewâ€, Asian Journal of Contemporary Sciences, Vol. 1, pp. 12-17 (2012).

S. A. Kalogirou, “Artificial Neural Networks in Renewable Energy Systems Applications: A Reviewâ€, Renewable and Sustainable Energy Reviews, 5(4), 373-401 (2001), Kalogirou, S. A. (2001). Artificial neural networks in renewable energy systems applications: a review. Renewable and Sustainable Energy Reviews, 5(4), 373–401. DOI: 10.1016/s1364-0321(01)00006-5

A. Choudhary, D. Pandey and A. Kumar, "A Review of Various Techniques for Solar Radiation Estimation," 3rd International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE), NOIDA, India, pp. 169-174 (2019), DOI: 10.1109/RDCAPE47089.2019.8979001

O. Muammer, B. Mehmet, S. Besir, “Estimation of global solar radiation using ANN over Turkeyâ€, Expert Syst Appl, 39(5):5043–51 (2012), DOI: 10.1016/j.eswa.2011.11.036

S. A. fetsos, A. H. Coonick, "Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques", Sol Energy; 68 (2):169–78 (2000), DOI: 10.1016/s0038-092x(99)00064-x

M. A. Behrang, E. Assareh, A. Ghanbarzadeh, A. R. Noghrehabadi, “The potential of different artificial neural network(ANN) techniques in daily global solar radiation modeling based on meteorological data", Sol Energy, 84(8):1468–80 (2010), DOI: 10.1016/j.solener.2010.05.009

M. Mohandes, S. Rehman, T. O. Halawani, “Estimation of global solar radiation using artificial neural networksâ€, Renewable Energy;14(1–4):179–84 (1998), DOI: 10.1016/s0960-1481(98)00065-2

J. Cao, L. Xingchun, “Study of hourly and daily solar irradiation forecast using diagonal recurrent wavelet neural networksâ€, Energy Convers Manage; 49(6):1396–406 (2008), DOI: 10.1016/j.enconman.2007.12.030

M. Benghanem, A. Mellit, S. N. Alamria, "ANN-based modeling and estimation of daily global solar radiation data: a case study", Energy Convers Manage; 50(7):1644–55 (2009), DOI: 10.1016/j.enconman.2009.03.035

L. Saad Saoud, F. Rahmoune, V. Tourtchine, K. Baddari, Fully Complex Valued Wavelet Neural Network for Forecasting the Global Solar Radiation.




DOI (PDF): https://doi.org/10.20508/ijrer.v10i4.11373.g8041

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