Systematic Review on Ground-Based Cloud Tracking Methods for Photovoltaics Nowcasting  

作　　者：Juliana Marian Arrais Allan Cerentini Bruno Juncklaus Martins Thiago Zimmermann Loureiro Chaves Sylvio Luiz Mantelli Neto Aldo von Wangenheim Juliana Marian Arrais;Allan Cerentini;Bruno Juncklaus Martins;Thiago Zimmermann Loureiro Chaves;Sylvio Luiz Mantelli Neto;Aldo von Wangenheim(PPGCC-INE, UFSC Federal University of Santa Catarina, Florianpolis, Brazil;INPE Brazilian National Institute for Space Research, So Paulo, Brazil)

机构地区：[1]PPGCC-INE, UFSC Federal University of Santa Catarina, Florianpolis, Brazil [2]INPE Brazilian National Institute for Space Research, So Paulo, Brazil

出　　处：《American Journal of Climate Change》2024年第3期452-476,共25页美国气候变化期刊（英文）

摘　　要：Renewable energies are highly dependent on local weather conditions, with photovoltaic energy being particularly affected by intermittent clouds. Anticipating the impact of cloud shadows on power plants is crucial, as clouds can cause partial shading, excessive irradiation, and operational issues. This study focuses on analyzing cloud tracking methods for short-term forecasts, aiming to mitigate such impacts. We conducted a systematic literature review, highlighting the most significant articles on cloud tracking from ground-based observations. We explore both traditional image processing techniques and advances in deep learning models. Additionally, we discuss current challenges and future research directions in this rapidly evolving field, aiming to provide a comprehensive overview of the state of the art and identify opportunities for significant advancements in the next generation of cloud tracking systems based on computer vision and deep learning.Renewable energies are highly dependent on local weather conditions, with photovoltaic energy being particularly affected by intermittent clouds. Anticipating the impact of cloud shadows on power plants is crucial, as clouds can cause partial shading, excessive irradiation, and operational issues. This study focuses on analyzing cloud tracking methods for short-term forecasts, aiming to mitigate such impacts. We conducted a systematic literature review, highlighting the most significant articles on cloud tracking from ground-based observations. We explore both traditional image processing techniques and advances in deep learning models. Additionally, we discuss current challenges and future research directions in this rapidly evolving field, aiming to provide a comprehensive overview of the state of the art and identify opportunities for significant advancements in the next generation of cloud tracking systems based on computer vision and deep learning.

关 键 词：NOWCASTING PHOTOVOLTAIC Image Processing 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]