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Generalized Regression Neural Network Model Based Estimation of Global Solar Energy Using Meteorological Parameters

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Abstract

The global solar irradiance data plays a vital role in evaluating the performance of all the solar energy conversion devices. In general there are two methods to predict the performance of such irradiance, namely physical models and the machine learning models. This paper presents a generalized regression neural network model (a machine learning technique) for estimating the global solar irradiance using seasonal and meteorological factors as input parameters. Results obtained from this proposed generalized regression neural network approach are compared with the results estimated by extensively used machine learning based methodologies such as fuzzy and artificial neural network models. Such a comparative results clearly indicate that prediction accuracy of proposed generalized regression neural network model is in good agreement with experimentally measured values. The mean percentage error for using GRNN, fuzzy logic and artificial neural network are 3.55%, 4.64%, and 5.49%.

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Data Availability

http://www.indiaenvironmentportal.org.in/files/srd-sec.pdf.

Code Availability

The paper includes all the codes related to the prediction.

Abbreviations

D:

Day of the year

H:

Monthly mean daily irradiance on horizontal surface

\({\mathbf{H}}_{0}\) :

Mean clear sky daily irradiance

\({\raise0.7ex\hbox{${\mathbf{H}}$} \!\mathord{\left/ {\vphantom {{\mathbf{H}} {{\mathbf{H}}_{0} }}}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{${{\mathbf{H}}_{0} }$}}\) :

Clearness index

\({\mathbf{H}}_{{{\mathbf{meas}}}}\) :

Measured value of monthly mean daily irradiance on horizontal surface

\({\mathbf{H}}_{{{\mathbf{estim}}}}\) :

Predicted value of monthly mean daily irradiance on horizontal surface

S:

Monthly mean daily hours of bright sunshine

\({\mathbf{S}}_{0}\) :

Monthly mean of maximum possible daily hours of bright sunshine

\({\raise0.7ex\hbox{${\mathbf{S}}$} \!\mathord{\left/ {\vphantom {{\mathbf{S}} {{\mathbf{S}}_{0} }}}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{${{\mathbf{S}}_{0} }$}}\) :

Mean fraction possible sunshine hours

T:

Monthly mean hourly temperature (°C)

\({\mathbf{T}}_{0}\) :

Monthly mean hourly maximum possible temperature (°C)

\({\raise0.7ex\hbox{${\mathbf{T}}$} \!\mathord{\left/ {\vphantom {{\mathbf{T}} {{\mathbf{T}}_{0} }}}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{${{\mathbf{T}}_{0} }$}}\) :

Ratio of monthly mean hourly temperature to monthly mean hourly maximum possible temperature

\({\mathbf{T}}_{{\mathbf{a}}}\) :

Ambient temperature (°C)

ω:

Hour angle

\({{\varvec{\upomega}}}_{{\mathbf{s}}}\) :

Hour angle at sunset

Ø:

Latitude of location

δ:

Declination angle

FL:

Fuzzy logic

ANN:

Artificial neural network

GRNN:

Generalized regression neural network

NN:

Neural network

RMSE:

Root mean square error

K-NN:

K-nearest neighbor algorithm

SVM:

Support vector machine

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Authors and Affiliations

  1. K. Ramakrishnan College of Engineering, Tiruchirappalli, Tamilnadu, 621112, India

    M. Sridharan

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  1. M. Sridharan
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Appendices

Appendix 1

For the prediction of global solar irradiance at Jodhpur, Shillong, New Delhi, and Kolkata stations, Fuzzy logic based architecture has been developed and presented in Fig. 8. was proposed by [13] (Table 8).

Fig. 8
figure 8

Fuzzy logic based architecture suggested by [9] for prediction of global solar energy at Jodhpur, Shillong, New Delhi, and Kolkata stations

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Table 8 Comparison between GRNN model with and without spread function
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Appendix 2

For the prediction of global solar irradiance at Jodhpur, Shillong, New Delhi, and Kolkata stations, ANN logic based architecture has been developed and presented in Fig. 9. was proposed by [13].

Fig. 9
figure 9

ANN model based architecture suggested by [9] for prediction of global solar energy at Jodhpur, Shillong, New Delhi, and Kolkata stations

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Sridharan, M. Generalized Regression Neural Network Model Based Estimation of Global Solar Energy Using Meteorological Parameters. Ann. Data. Sci. 10, 1107–1125 (2023). https://doi.org/10.1007/s40745-020-00319-4

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