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

• Main Authors: Hsu-Yung Cheng, Chih-Chang Yu, Kuo-Chang Hsu, Chi-Chang Chan, Mei-Hui Tseng, Chih-Lung Lin
• Format: Article
• Language: English
• Published: MDPI AG 2019-04-01
• Series: Energies
• Subjects: photovoltaics; inclined solar irradiance; artificial neural networks
• Online Access: https://www.mdpi.com/1996-1073/12/8/1427

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.