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Research Papers

Estimation of Solar Potential for Thailand Using Adaptive Neurofuzzy Inference System Models

[+] Author and Article Information
Jirasuwankul Nirudh

Department of Electrical Engineering, Faculty of Engineering,
King Mongkut’s Institute of Technology Ladkrabang,
1 Chalongkrung Rd., Ladkrabang,
Bangkok 10520, Thailand
e-mail: nirudh.ji@kmitl.ac.th

Jiriwibhakorn Somchat

Department of Electrical Engineering, Faculty of Engineering,
King Mongkut’s Institute of Technology Ladkrabang,
1 Chalongkrung Rd., Ladkrabang,
Bangkok 10520, Thailand
e-mail: somchat.ji@kmitl.ac.th

Contributed by the Solar Energy Division of ASME for publication in the Journal of Solar Energy Engineering: Including Wind Energy and Building Energy Conservation. Manuscript received December 27, 2018; final manuscript received May 9, 2019; published online June 5, 2019. Assoc. Editor: Ting Ma.

J. Sol. Energy Eng 141(6), 061009 (Jun 05, 2019) (8 pages) Paper No: SOL-18-1590; doi: 10.1115/1.4043826 History: Received December 27, 2018; Accepted May 10, 2019

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.

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References

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Figures

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Fig. 1

Meteorological stations in Thailand

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Fig. 2

Structure of the ANFIS model

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Fig. 3

(a) Designation of inputs and outputs of the ANFIS model and (b) ANFIS modeling process

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Fig. 4

(a) matlab gui: state diagram of processing flow, (b) matlab gui: the first page-area selection, and (c) matlab gui: second page-data inputting and calculation

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Fig. 8

Tested results between two estimators versus real data in 2016

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Fig. 7

Measured and estimated data of six areas in 2016

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Fig. 6

Nine-year measured data and estimated data by ANFIS models

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Fig. 5

Correlation between estimated and measured data of six regions

Tables

Errata

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