Cloud optical depth retrieval via sky’s infrared image for solar radiation prediction - Lai, Kok Yee; Tan, Lit Ken; Asako, Yutaka; Lee, Kee Quen; Chuan, Zun Liang; Wan Nur Syahidah, Wan Yusoff; Homma, Koji; Arada, Gerald Pacaba; Gan, Yee Siang; Tey, Wah Yen; Kong, Calvin Leng Sing; Kamadinata, Jane Oktavia; et al. (Akademi Baru, 2019)
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.