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Cloud optical depth retrieval via sky\u27s infrared image for solar radiation prediction
Authors
Gerald Pacaba Arada
Yutaka Asako
+11 more
Koji Homma
Jane Oktavia Kamadinata
Tan Lit Ken
Chuan Zun Liang
Lee Kee Quen
Gan Yee Siang
Calvin Kong Leng Sing
Wan Nur Syahidah
Akira Taguchi
Lai Kok Yee
Tey Wah Yen
Publication date
1 June 2019
Publisher
Animo Repository
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\u27s 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. © 2019 Penerbit Akademia Baru
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Universiti Teknologi Malaysia Institutional Repository
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Last time updated on 08/04/2021
Animo Repository - De La Salle University Research
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oai:animorepository.dlsu.edu.p...
Last time updated on 03/12/2021