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

Authors

  • Lai Kok Yee Malaysia – Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya Petra (Jalan Semarak), 54100 Kuala Lumpur, Malaysia
  • Tan Lit Ken Department of Computer Science, Faculty of Knowledge Engineering, Tokyo City University, 1-28-1, Tamazutsumi, Setagaya-ku, Tokyo, 158- 8557, Japan
  • Yutaka Asako Malaysia – Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya Petra (Jalan Semarak), 54100 Kuala Lumpur, Malaysia
  • Lee Kee Quen Malaysia – Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya Petra (Jalan Semarak), 54100 Kuala Lumpur, Malaysia
  • Chuan Zun Liang Faculty of Industrial Sciences & Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Pahang, Malaysia
  • Wan Nur Syahidah Faculty of Industrial Sciences & Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Pahang, Malaysia
  • Koji Homma International Center, Tokyo City University, 1-28-1, Tamazutsumi, Setagaya-ku, Tokyo, 158-8557, Japan
  • Gerald Pacaba Arada Electronics and Communications Engineering Department, Gokongwei College of Engineering, De La Salle University, Taft Avenue, Manila, Philippines
  • Gan Yee Siang Research Center for Healthcare Industry Innovation, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan
  • Tey Wah Yen Malaysia – Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya Petra (Jalan Semarak), 54100 Kuala Lumpur, Malaysia
  • Calvin Kong Leng Sing Malaysia – Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya Petra (Jalan Semarak), 54100 Kuala Lumpur, Malaysia
  • Jane Oktavia Kamadinata Malaysia – Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya Petra (Jalan Semarak), 54100 Kuala Lumpur, Malaysia
  • Akira Taguchi Department of Computer Science, Faculty of Knowledge Engineering, Tokyo City University, 1-28-1, Tamazutsumi, Setagaya-ku, Tokyo, 158- 8557, Japan

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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Published

2020-12-16

How to Cite

Kok Yee, L. ., Lit Ken, T. ., Asako, Y. ., Kee Quen, . L. ., Zun Liang, C. ., Nur Syahidah, W. ., Homma, . K. ., Arada, G. P. ., Siang, G. Y. ., Wah Yen, T. ., Kong Leng Sing, C. ., Kamadinata, J. O. ., & Taguchi, A. . (2020). 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. Retrieved from https://www.akademiabaru.com/submit/index.php/arfmts/article/view/2560
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