A Research on Snow Cover Rate Estimation Method from Panel Images using Semantic Segmentation

被引:0
|
作者
Nishita Y. [1 ]
Izui Y. [1 ]
Suzuki K. [1 ]
Natsuume D. [1 ]
Tabata H. [1 ]
机构
[1] Kanazawa Institute of Techology, 7-1, Ohgigaoka, Ishikawa, Nonoichi
关键词
deep learning; panel image; photovoltaic power prediction; semantic segmentation; snow cover rate estimation; snowfall area;
D O I
10.1541/ieejeiss.143.985
中图分类号
学科分类号
摘要
In previous research, we estimated snow cover rate on the panel from panel image for the purpose of improving the prediction accuracy of the PV power considering the effect of the snow accumulation on the panel in the snowfall area. A method to predict the quantity was proposed. However, in previous research, the snow cover rate was estimated by dividing it into 11 categories from 0% to 100% in increments of 10%. There is a question as to whether or not it is possible, and we think that estimating with even finer granularity will lead to further improvement in the prediction accuracy of PV power. We will compare and evaluate the prediction of PV power generation using the snow cover rate predicted from panel image with related research and confirm the effectiveness of the proposed method. © 2023 The Institute of Electrical Engineers of Japan.
引用
收藏
页码:985 / 992
页数:7
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