Cloud tracking using clusters of feature points for accurate solar irradiance nowcasting

研究成果: 雜誌貢獻期刊論文同行評審

39 引文 斯高帕斯(Scopus)

摘要

In this work, we propose a system to track the clouds and predict relevant events based on all-sky images. To deal with the nature of variable appearance of clouds, we use clusters of feature points to perform tracking. We propose an enhanced clustering algorithm that does not require prior knowledge of number of clusters. The proposed clustering algorithm can successfully separate feature points into groups with reasonable sizes and ranges. In the tracking process, merging and splitting of clouds are handled via checking matched pairs of feature points among different clusters. Afterwards, the tracking information is utilized to predict if the sun will be covered or obscured by clouds within the prediction horizon. Features are extracted from the tracked feature points and a Markov chain model is designed to perform ramp-down event prediction. The obscuration and ramp-down events have an important impact on solar irradiance. The experiments have shown that the proposed system can substantially enhance the accuracy of solar irradiance nowcasting on a challenging dataset.

原文???core.languages.en_GB???
頁(從 - 到)281-289
頁數9
期刊Renewable Energy
104
DOIs
出版狀態已出版 - 2017

指紋

深入研究「Cloud tracking using clusters of feature points for accurate solar irradiance nowcasting」主題。共同形成了獨特的指紋。

引用此