Approximation of Empirical and ANN Based Solar Radiation Models for Four Smart Cities in Tamil Nadu with Most Prompting Input Parameters
V.Annapeachi1, G.S.Gayathri2, P.S.Manoharan3
1Ms.V.ANNAPEACHI: Assistant professor at M.Kumarasamy College Of Engineering Karur, (Tamil Nadu) India.
2G.S.Gayathri assistant professor at M.Kumarasamy college of Engineering, karur, (Tamil Nadu) India.
3P.S.Manoharan Associate Professor in Thiagarajar College of Engineering, Madurai, India.

Manuscript received on November 17., 2019. | Revised Manuscript received on November 24 2019. | Manuscript published on 30 November, 2019. | PP: 12952-12956 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8770118419/2019©BEIESP | DOI: 10.35940/ijrte.D8770.118419

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Abstract: The abundant source of Solar Radiation is possible in different latitude of Tamil Nadu. The several Empirical models have developed and accuracy of the model is further validated with Artificial Neural Network models. The ANN and Empirical model is developed with different combination of input attributes. The Approximate Sunshine Solar Radiation, Temperature and Relative Humidity are the input attributes. In this paper, it has been evaluated that combination of Approximate Sunshine Solar radiation and Temperature ANN based model is best for prediction of Daily Global Solar Radiation when compared to ST based Empirical model. The Correlation analysis is performedfor selection of most relevant input attributes. And hence the estimation of daily global solar radiation is performed with minimum number of input attributes. The training and testing of each model is performed by ANN Simulink model. and from this  raining of inpu variables,  he over fitting of the model is reduced. The estimated GSR is validated with experimental data which is collected from Tamil Nadu Agriculture University.
Keywords: Approximate Sunshine Solar radiation, Artificial Neural Network, Empirical Models, Meteorological Parameters, Classification, South India.
Scope of the Article: Classification.