Estimating Solar Irradiance on Tilted Surface with Arbitrary Orientations and Tilt Angles

Hsu-Yung Cheng, Chih-Chang Yu, Kuo-Chang Hsu, Chi-Chang Chan, Mei-Hui Tseng and Chih-Lung Lin
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Hsu-Yung Cheng: Department of Computer Science and Information Engineering, National Central University, 300 Zhongda Rd., Zhongli District, Taoyuan 32001, Taiwan
Chih-Chang Yu: Department of Information and Computer Engineering, Chung Yuan Christian University, 200 Chung-Pei Rd., Zhongli District, Taoyuan 32023, Taiwan
Kuo-Chang Hsu: Green Energy and Environment Research Laboratories, Industrial Technology Research Institute, 195 Sec.4, Chung-Hsing Rd., Chutung, Hsinchu 31057, Taiwan
Chi-Chang Chan: Green Energy and Environment Research Laboratories, Industrial Technology Research Institute, 195 Sec.4, Chung-Hsing Rd., Chutung, Hsinchu 31057, Taiwan
Mei-Hui Tseng: Green Energy and Environment Research Laboratories, Industrial Technology Research Institute, 195 Sec.4, Chung-Hsing Rd., Chutung, Hsinchu 31057, Taiwan
Chih-Lung Lin: Department of Electronic Engineering, Hwa Hsia University of Technology, 111 Gon Jhuan Rd., Chung Ho dist., New Taipei City 23568, Taiwan

Energies, 2019, vol. 12, issue 8, 1-14

Abstract: Photovoltaics modules are usually installed with a tilt angle to improve performance and to avoid water or dust accumulation. However, measured irradiance data on inclined surfaces are rarely available, since installing pyranometers with various tilt angles induces high costs. Estimating inclined irradiance of arbitrary orientations and tilt angles is important because the installation orientations and tilt angles might be different at different sites. The goal of this work is to propose a unified transfer model to obtain inclined solar irradiance of arbitrary tilt angles and orientations. Artificial neural networks (ANN) were utilized to construct the transfer model to estimate the differences between the horizontal irradiance and the inclined irradiance. Compared to ANNs that estimate the inclined irradiance directly, the experimental results have shown that the proposed ANNs with differential outputs can substantially improve the estimation accuracy. Moreover, the trained model can successfully estimate inclined irradiance with tilt angles and orientations not included in the training data.

Keywords: photovoltaics; inclined solar irradiance; artificial neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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