Abstract
Renewable source of energy plays an important role in meeting the world’s power demands. The major factor that makes this source of energy to be important is due to its clean energy generation methodology. Hydro, wind and solar are effectively harnessed in almost every part of the world. However, solar energy, although has less environmental impacts, remains behind when compared to hydro and wind. For effective solar energy generation, it is necessary to know the amount of solar radiation that it receives in a month or a year at that location to harness and install photovoltaic (PV) system in that particular location. Solar radiation at any place can either be measured directly using instruments or empirically determined from global solar radiation. However, these methods are either expensive or not accurate. Artificial neural network (ANN) method provides a convenient approach to overcome the drawbacks of conventional instrumental or other empirical methods. ANN is analogous to human nervous system and are widely used for solving linear and nonlinear problems. This paper has developed neural network model for prediction of monthly solar radiation considering two places in Arunachal Pradesh, i.e., Itanagar and Pasighat. Multilayer feed forward network with back propagation has been proposed for the ANN model. Five-year meteorological data was collected and used for training the proposed network model. The network performance was checked by considering mean square error (MSE) and R (coefficient of correlation) which gave the value very close to zero above 92% for the considered location, respectively. Thus, the predicted output is almost in agreement with the actual output making this model applicable for prediction of monthly solar radiation for considered places. Apart for this prediction, performance of different combination of meteorological parameter were also evaluated and found that parameters such as temperature, humidity, sunshine hour and wind speed are found to be more influencing parameter for solar radiation estimation. This paper demonstrates that ANN has the potential to overcome many of the drawbacks and challenges in estimating the solar radiation, which is essential for designing solar energy generation panels. The findings are particularly useful in data sparse regions and highly applicable in regions where adequate energy supply is still a herculean task.
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References
Chaibi, Y., Allouhi, A., Malvoni, M., Salhi, M., Saadani, R.: Solar irradiance and temperature influence on the photovoltaic cell equivalent-circuit models. Sol. Energy 188, 1102–1110 (2019)
Li, D.H.W., Chen, W., Li, S., Lou, S.: Estimation of hourly global solar radiation using multivariate adaptive Regression spline (MARS)—a case study of Hong Kong. Energy 186, 1158575 (2019)
Antonopoulos, V.Z., Papamichail, D.M., Aschonitis, V.G., Antonopoulos, A.V.: Solar radiation estimation methods using ANN and empirical models. Comput. Electron. Agric. 160, 160–167 (2019)
Amrouche, B., Le Pivert, X.: Artificial neural network based daily local forecasting for global solar radiation. Appl. Energy 130, 333–341 (2014)
Qazi, A., Fayaz, H., et al.: The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review. J. Clean. Prod. 104, 1–12 (2015)
Sozena, A., et al.: Use of artificial neural networks for mapping of solar potential in Turkey. Appl. Energy 77(3), 273–286 (2000)
Federico, E., et al.: Artificial neural network optimisation for monthly average daily global solar radiation. Energy Convers. Manage. 120, 320–329 (2016)
Siva Krishna Rao K.D.V., Premalatha, M., Naveen, C.: Analysis of different combinations of meteorological parameters in predicting the horizontal global solar radiation with ANN approach: a case study. Renew. Sustain. Energy Rev. 91, 248–258 (2018)
Behrang, M.A., et al.: The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data. Sol. Energy 84(8), 1468–1480 (2010)
Marzouq, M., et al.: ANN-based modelling and prediction of daily global solar irradiation using commonly measured meteorological parameters. IOP Conf. Ser. Earth Environ. Sci. 161, 01 (2017)
Premalatha, N., Amirtham, V.A.: Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms. J. Appl. Res. Technol. 14(3), 206–214 (2016)
Mubiru, J., Banda, E.J.K.B.: Estimation of monthly average daily global solar irradiation using artificial neural networks. Sol. Energy 82(2), 181–187 (2008)
Boscha, J.L., et al.: Daily solar irradiation estimation over a mountainous area using artificial neural networks. Renew. Energy 33(7), 1622–1628 (2008)
Voyant, C., et al.: Machine learning methods for solar radiation forecasting: a review. Renew. Energy 105, 569–582 (2017)
Sivaneasan, B., Yu, C.Y., Goh, K.P.: Solar forecasting using ANN with fuzzy logic pre-processing. Energy Procedia 143, 727–732 (2017)
Solanki, C.S.: Solar Photovoltaics: Fundamentals, Technologies and Applications, 2nd edn. PHI Learning Private Limited, New Delhi (2011)
Quansah, E., et al.: Empirical models for estimating global solar radiation over the ashanti region of Ghana. J. Sol. Energy 2014, 6 (2014). Article ID 897970
Ramachandra, T.V., Jaina, R., Krishnadas, G.: Hotspots of solar potential in India. Renew. Sustain. Energy Rev. 15(6), 3178–3186 (2011)
Fadare, D.A., Irimisose, I., Oni, A.O., Falana, A.: Modeling of solar energy potential in Africa using an artificial neural network (2010)
Fadare, D.A.: Modelling of solar energy potential in Nigeria using an artificial neural network model. Appl. Energy 86(9), 1410–1422 (2009)
Rehmana, S., Mohandes, M.: Artificial neural network estimation of global solar radiation using air temperature and relative humidity. Energy Policy 36(2), 571–576 (2008)
Mellit, A., Pavan, A.M.: A 24-h forecast of solar irradiance using artificial neural network: application for performance prediction of a grid-connected PV plant at Trieste Italy. Sol. Energy 84(5), 807–821 (2010)
Soares, J., et al.: Modeling hourly diffuse solar-radiation in the city of Paulo using a neural-network technique. Appl. Energy 79(2), 201–214 (2004)
Mellit, A., et al.: An ANFIS-based forecasting for solar radiation data from sunshine duration and Ambient temperature. IEEE Power Eng. Soc. Gen. Meet. 24–28 (2007)
Iqdour, R., Zeroual, A.: A rule based fuzzy model for the prediction of solar radiation. IEEE Revue Des Energies Renouvelables 9, 113–120 (2006)
Almaraashi, M.: Short-term prediction of solar energy in Saudi Arabia using automated-design fuzzy logic systems. PLoS One 12(8), e0182429, (2017)
Alzahrani, A., et al.: Solar Irradiance forecasting using deep neural networks. Procedia Comput. Sci. 114, 304–313 (2017)
Sivanandam, S.N., Sumathi, S., Deepa, S.N.: Introduction to neural networks using Matlab 6.0. McGraw Hill Education (India) Private Limited (2006)
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Hashunao, S., Sunku, H., Mehta, R.K. (2021). Modelling and Forecasting of Solar Radiation Data: A Case Study. In: Das, B., Patgiri, R., Bandyopadhyay, S., Balas, V.E. (eds) Modeling, Simulation and Optimization. Smart Innovation, Systems and Technologies, vol 206. Springer, Singapore. https://doi.org/10.1007/978-981-15-9829-6_1
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