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Estimation of Solar Potential for Thailand Using Adaptive Neurofuzzy Inference System Models
Author(s)
Nirudh, Jirasuwankul
Somchat, Jiriwibhakorn
Date Issued
December 1, 2019
Type
Article
Abstract
This research paper proposes a new method of global solar radiation prediction for Thailand using adaptive neurofuzzy inference system (ANFIS) models. Contrary to mathematical-based modeling approaches, the proposed models are able to estimate the monthly mean of daily global solar radiation at the ground level without using the earth's atmospheric layer model. The proposed technique alternately utilizes the 9-year long recorded spatiotemporal data of solar irradiance from meteorological ground stations in the modeling process. With a limited number of ground stations, it covered six regions of Thailand, ANFIS modeling; testing and restructuring have been performed repetitively; and finally, the best-fit models with minimum mean absolute percentage errors (MAPEs) corresponding to six regions of Thailand are obtained. Moreover, the ANFIS models have been tested comparatively to the measured data and the multilayer feed forward artificial neural network (ANN) models, which has a good agreement to real data for the proposed models, can be met with the average accuracy of 7.07% MAPE. By applying this model as a tool to estimate solar potential, the local government or the business sector can provide basic information, which is useful for solar energy system planning and project development.
Citation
Journal of Solar Energy Engineering Transactions of the ASME, 141(6), 2019