Abstract
Accurate forecasting of solar energy is a key issue for a meaningful integration of the solar power plants into the grid. Solar photovoltaic technology is most preferable and vital technology in comparison with all other sources of renewable energy. We know that the solar energy is very irregular so the output of solar photo voltaic system (SPV) is diverted by the atmospheric conditions like temperatures, humidity, wind velocity, solar irradiance and other climatological facts. It’s necessary to predict solar energy to minimize the uncertainty in power harnessing from solar photovoltaic system. In this work fuzzy logic model and ANFIS model have been developed for manipulating solar irradiation (w/m2) data to forecast short duration solar irradiation. Further the normalization in between the input and output is set in between 0.1 and 0.9 for reducing confluence problems. Acquired results are matched up to the manipulated data and valid results are found out.
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References
Ramedani, Z., Omid, M., Keyhani, A.: Modeling solar energy potential in a Tehran Province using artificial neural networks. Int. J. Green Energy 10(4), 427–441 (2013)
Rahoma, W.A., Rahoma, U.A., Hassan, A.H.: Application of neuro-fuzzy techniques for solar radiation (2011)
Iqdour, R., Zeroual, A.: A rule based fuzzy model for the prediction of solar radiation. Revue des Energies Renouv. 9(2), 113–120 (2006)
Sumithira, T.R., Kumar, A.N., Kumar, R.R.: An adaptive neuro-fuzzy inference system (ANFIS) based prediction of solar radiation: a case study. J. Appl. Sci. Res. 8(1), 346–351 (2012)
Yadav, A.K., Chandel, S.S.: Solar energy potential assessment of Western Himalayan Indian state of Himachal Pradesh using J48 algorithm of WEKA in ANN based prediction model. Renew. Energy 75, 675–693 (2015)
Mellit, A., Kalogirou, S.A., Shaari, S., Salhi, H., Arab, A.H.: Methodology for predicting sequences of mean monthly clearness index and daily solar radiation data in remote areas: application for sizing a stand-alone PV system. Renew. Energy 33(7), 1570–1590 (2008)
Bhardwaj, S., Sharma, V., Srivastava, S., Sastry, O.S., Bandyopadhyay, B., Chandel, S.S., Gupta, J.R.P.: Estimation of solar radiation using a combination of Hidden Markov Model and generalized Fuzzy model. Sol. Energy 93, 43–54 (2013)
Mohammadi, K., Shamshirband, S., Petković, D., Khorasanizadeh, H.: Determining the most important variables for diffuse solar radiation prediction using adaptive neuro-fuzzy methodology; case study: City of Kerman. Iran. Renew. Sustain. Energy Rev. 53, 1570–1579 (2016)
Mohanty, S., Patra, P.K., Sahoo, S.S.: Comparison and prediction of monthly average solar radiation data using soft computing approach for Eastern India. Computational Intelligence in Data Mining-Volume 3, pp. 317–326. Springer, New Delhi (2015)
Jafarkazemi, F., Moadel, M., Khademi, M., Razeghi, A.: Performance prediction of flat-plate solar collectors using MLP and ANFIS. J. Basic Appl. Sci. Res. 3(2), 196–200 (2013)
Hawlader, M.N.A., Chou, S.K., Ullah, M.Z.: The performance of a solar assisted heat pump water heating system. Appl. Therm. Eng. 21(10), 1049–1065 (2001)
Mohanty, S., Patra, P.K., Sahoo, S.S., Mohanty, A.: Forecasting of solar energy with application for a growing economy like India: survey and implication. Renew. Sustain. Energy Rev. 78, 539–553 (2017)
Mellit, A., Arab, A.H., Shaari, S.: An ANFIS-based prediction for monthly clearness index and daily solar radiation: application for sizing of a stand-alone photovoltaic system. J. Phys. Sci. 18(2), 15–35 (2007)
Mohanty, S.: ANFIS based prediction of monthly average global solar radiation over Bhubaneswar (State of Odisha). Int. J. Ethics Eng. Manag. Educ. ISSN 1(5), 2348–4748 (2014)
Yadav, A.K., Chandel, S.S.: Solar radiation prediction using artificial neural network techniques: a review. Renew. Sustain. Energy Rev. 33, 772–781 (2014)
Mohanty, S., Patra, P.K., Sahoo, S.S.: Prediction of global solar radiation using nonlinear auto regressive network with exogenous inputs (NARX). In: 2015 39th National Systems Conference (NSC), pp. 1–6. IEEE (2015)
Varzandeh, M.H.M., Rahbari, O., Vafaeipour, M., Raahemifar, K., Heidarzade, F.: Performance of wavelet neural network and ANFIS algorithms for short-term prediction of solar radiation and wind velocities. In: The 4th World Sustainability Forum (2014)
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Viswavandya, M., Sarangi, B., Mohanty, S., Mohanty, A. (2020). Short Term Solar Energy Forecasting by Using Fuzzy Logic and ANFIS. In: Behera, H., Nayak, J., Naik, B., Pelusi, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 990. Springer, Singapore. https://doi.org/10.1007/978-981-13-8676-3_63
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DOI: https://doi.org/10.1007/978-981-13-8676-3_63
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