USING ARTIFICIAL INTELLIGENCE TO ASSESS SOLAR RADIATION FROM THE TOTAL SKY IMAGES
In one sentence. Optical flow on sky images tracks cloud motion, and a neural network links it to short-term whole-sky solar radiation changes for plant efficiency estimates.
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.
Labels
- Topic
- Nowcasting
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 0–6 h
- Domain
- Local / station
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.29121/ijetmr.v7.i5.2020.685 OpenAlex:W3033799550
- Added
- 2026-10-22
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