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Energies 2019, 12(8), 1427; https://doi.org/10.3390/en12081427

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

1
Department of Computer Science and Information Engineering, National Central University, 300 Zhongda Rd., Zhongli District, Taoyuan 32001, Taiwan
2
Department of Information and Computer Engineering, Chung Yuan Christian University, 200 Chung-Pei Rd., Zhongli District, Taoyuan 32023, Taiwan
3
Green Energy and Environment Research Laboratories, Industrial Technology Research Institute, 195 Sec.4, Chung-Hsing Rd., Chutung, Hsinchu 31057, Taiwan
4
Department of Electronic Engineering, Hwa Hsia University of Technology, 111 Gon Jhuan Rd., Chung Ho dist., New Taipei City 23568, Taiwan
*
Author to whom correspondence should be addressed.
Received: 28 February 2019 / Revised: 29 March 2019 / Accepted: 10 April 2019 / Published: 13 April 2019
(This article belongs to the Special Issue Solar Energy Harvesting, Storage and Application)
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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. View Full-Text
Keywords: photovoltaics; inclined solar irradiance; artificial neural networks photovoltaics; inclined solar irradiance; artificial neural networks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Cheng, H.-Y.; Yu, C.-C.; Hsu, K.-C.; Chan, C.-C.; Tseng, M.-H.; Lin, C.-L. Estimating Solar Irradiance on Tilted Surface with Arbitrary Orientations and Tilt Angles. Energies 2019, 12, 1427.

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