Cloud optical depth retrieval via sky’s infrared image for solar radiation prediction

Lai, Kok Yee and Tan, Lit Ken and Asako, Yutaka and Lee, Kee Quen and Chuan, Zun Liang and Wan Nur Syahidah, Wan Yusoff and Homma, Koji and Arada, Gerald Pacaba and Gan, Yee Siang and Tey, Wah Yen and Kong, Calvin Leng Sing and Kamadinata, Jane Oktavia and Taguchi, Akira (2019) Cloud optical depth retrieval via sky’s infrared image for solar radiation prediction. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 58 (1). pp. 1-14. ISSN 2289-7879. (Published)

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Photovoltaic (PV) system is developed to harness solar energy as an alternative energy to reduce the dependency on fossil fuel energy. However, the output of the PV system is not stable due to the fluctuation of solar radiation. Hence, solar radiation prediction in advanced is needed to make sure the tap changer in PV system has enough time to respond. In this research, the cloud base temperature is identified from the sky’s thermal image. From the cloud base temperature, cloud optical depth (COD) is calculated. Artificial neural network (ANN) models are established by using different combinations of current solar radiation and COD to predict the solar radiation several minutes in advanced. R-squared value is used to measure the accuracy of the models. For prediction in advanced for every minute, with COD as input, always show the highest R-squared value. The highest R-squared value is 0.8899 for the prediction for 1 minute in advanced and dropped to 0.5415 as the minute of prediction in advanced increase to 5. This shows that the proposed methodology is suitable for prediction of solar radiation for short term in advanced.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Cloud optical depth; Infrared image; Solar radiation prediction; Artificial neural network
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Industrial Sciences And Technology
Institute of Postgraduate Studies
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 30 Jun 2021 14:28
Last Modified: 18 Jan 2022 02:04
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