In this paper, a nonlinear symbolic regression technique using an evolutionary algorithm known as multi-gene genetic programming (MGGP) is applied for a data-driven modelling between the dependent and the independent variables. The technique is applied for modelling the measured global solar irradiation and validated through numerical simulations. The proposed modelling technique shows improved results over the fuzzy logic and artificial neural network (ANN) based approaches as attempted by contemporary researchers. The method proposed here results in nonlinear analytical expressions, unlike those with neural networks which is essentially a black box modelling approach. This additional flexibility is an advantage from the modelling perspective and helps to discern the important variables which affect the prediction. Due to the evolutionary nature of the algorithm, it is able to get out of local minima and converge to a global optimum unlike the back-propagation (BP) algorithm used for training neural networks. This results in a better percentage fit than the ones obtained using neural networks by contemporary researchers. Also a hold-out cross validation is done on the obtained genetic programming (GP) results which show that the results generalize well to new data and do not over-fit the training samples. The multi-gene GP results are compared with those obtained using its single-gene version and also the same with four classical regression models in order to show the effectiveness of the adopted approach.
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November 2013
Research Article|
December 20 2013
Global solar irradiation prediction using a multi-gene genetic programming approach
Indranil Pan;
Indranil Pan
a)
1
Centre for Energy Studies, Indian Institute of Technology Delhi
, Hauz Khas, New Delhi 110016, India
2
Energy, Environment, Modelling and Minerals (E2M2) Research Section, Department of Earth Science and Engineering
, Imperial College London, Exhibition Road, London SW7 2AZ, United Kingdom
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Daya Shankar Pandey;
Daya Shankar Pandey
b)
1
Centre for Energy Studies, Indian Institute of Technology Delhi
, Hauz Khas, New Delhi 110016, India
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Saptarshi Das
Saptarshi Das
c)
3
Department of Power Engineering, Jadavpur University
, Salt Lake Campus, LB-8, Sector 3, Kolkata 700098, India
4
Communications, Signal Processing and Control (CSPC) Group, School of Electronics and Computer Science, University of Southampton
, Southampton SO17 1BJ, United Kingdom
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a)
Author to whom correspondence should be addressed. Electronic addresses: [email protected] and [email protected]
b)
Electronic mail: [email protected]
c)
Electronic addresses: [email protected] and [email protected]
J. Renewable Sustainable Energy 5, 063129 (2013)
Article history
Received:
April 26 2013
Accepted:
December 02 2013
Citation
Indranil Pan, Daya Shankar Pandey, Saptarshi Das; Global solar irradiation prediction using a multi-gene genetic programming approach. J. Renewable Sustainable Energy 1 November 2013; 5 (6): 063129. https://doi.org/10.1063/1.4850495
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