Cloud Optical Depth Retrieval via Sky’s Infrared Image For Solar Radiation Prediction

Journal of Advanced Research in Fluid Mechanics and Thermal Sciences
Volume 58, No. 1, June 2019, Pages 1-14

Lai Kok Yee1, Tan Lit Ken1,2,*, Yutaka Asako1, Lee Kee Quen1, Chuan Zun Liang3, Wan Nur Syahidah3, Koji Homma4, Gerald Pacaba Arada5, Gan Yee Siang6, Tey Wah Yen1, Calvin Kong Leng Sing1, Jane Oktavia Kamadinata1, Akira Taguchi2

1 Malaysia – Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya Petra (Jalan Semarak), 54100 Kuala Lumpur, Malaysia
2 Department of Computer Science, Faculty of Knowledge Engineering, Tokyo City University, 1-28-1, Tamazutsumi, Setagaya-ku, Tokyo, 158-8557, Japan
3 Faculty of Industrial Sciences & Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Pahang, Malaysia
4 International Center, Tokyo City University, 1-28-1, Tamazutsumi, Setagaya-ku, Tokyo, 158-8557, Japan
5 Electronics and Communications Engineering Department, Gokongwei College of Engineering, De La Salle University, Taft Avenue, Manila, Philippines
6 Research Center for Healthcare Industry Innovation, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan

*Corresponding author: tlken@utm.my

Cite this article
MLA
Lai, Kok Yee, et al. "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 (2019): 1-14.
APA

Lai, K. Y., Tan, L. K., Yutaka, A., Lee, K. Q., Chuan, Z. L., Wan Nur Syahidah, Koji, H., Gerald, P. A., Gan, Y. S., Tey, W. Y., Calvin, K. L. S., Jane, O. K., & Akira, T.(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), 1-14.
Chicago
Lai Kok Yee, Tan Lit Ken, Yutaka Asako, Lee Kee Quen, Chuan Zun Liang, Wan Nur Syahidah, Koji Homma, Gerald Pacaba Arada, Gan Yee Siang, Tey Wah Yan, Calvin Kong Leng Sing, Jane Oktavia Kamadinata, and Akira Taguchi."Cloud Optical Depth Retrieval via Sky’s Infrared Image For Solar Radiation Prediction." Journal of Advanced Research in Fluid Mechanics and Thermal Sciences. 58, no. 1 (2019): 1-14.
Harvard
Lai, K.Y., Tan, L.K., Yutaka, A., Lee, K.Q., Chuan, Z.L., Wan Nur Syahidah, Koji, H., Gerald, P.A., Gan, Y.S., Tey, W.Y., Calvin, K.L.S., Jane, O.K., Akira, T., 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.
Vancouver

Lai KY, Tan LK, Yutaka A, Lee KQ, Chuan ZL, Wan Nur Syahidah, Koji H, Gerald PA, Gan YS, Tey WY, Calvin KLS, Jane OK, Akira T. Cloud Optical Depth Retrieval via Sky’s Infrared Image For Solar Radiation Prediction. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences. 2019;58(1): 1-14.

KEYWORDS

cloud optical depth; infrared image; solar radiation prediction; artificial neural network

ABSTRACT

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.

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