USING ARTIFICIAL INTELLIGENCE TO ASSESS SOLAR RADIATION FROM THE TOTAL SKY IMAGES

Chi-Chang Chen; Chien-Hsing  Huang

doi:10.29121/ijetmr.v7.i5.2020.685

USING ARTIFICIAL INTELLIGENCE TO ASSESS SOLAR RADIATION FROM THE TOTAL SKY IMAGES

Authors

Chi-Chang Chen Department of Information Engineering, I-Shou University, Kaohsiung city 84001, Taiwan
Chien-Hsing Huang Department of Information Engineering, I-Shou University, Kaohsiung city 84001, Taiwan

DOI:

https://doi.org/10.29121/ijetmr.v7.i5.2020.685

Keywords:

Solar Radiation, Total Sky Images, Neural Network, Optical Flow, Global Horizontal Irradiance, Artificial Intelligence

Abstract

Solar power generation converts solar radiation into electrical energy. It is the most environmentally friendly green energy source in modern times, but the solar radiation reception rate is unstable due to weather. The general weather forecast is for the climate of a large area and cannot provide effective real-time prediction to the area where the power plant generating radiant energy from solar radiation. The sky imager can collect the sky image of the location of the solar power panel in real time, which can help to understand the weather conditions in real time, especially the dynamics of the clouds, which is the main reason for affecting the solar power generation. In this study, the optical flow method was used to analyze the motion vectors of clouds in the sky image, thereby estimating the changes of clouds in a short time, and the correlation between the distribution of clouds in the sky and the radiation of the whole sky images was analyzed through a neural network. The change further predicts the change in radiation across the sky, thereby effectively assessing the efficiency of solar power generation.

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References

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Published

2020-06-15

How to Cite

Chen, C.-C., & Huang, C.-H. . (2020). USING ARTIFICIAL INTELLIGENCE TO ASSESS SOLAR RADIATION FROM THE TOTAL SKY IMAGES. International Journal of Engineering Technologies and Management Research, 7(5), 64–71. https://doi.org/10.29121/ijetmr.v7.i5.2020.685

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Issue

Vol. 7 No. 5 (2020): IJETMR: Volume 7 Issue 5 - May, 2020

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Articles

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