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Golestaneh, F., Pinson, P., & Gooi, H. B. (2016). Very Short-Term Nonparametric Probabilistic Forecasting of Renewable Energy Generation—With Application to Solar Energy. IEEE Transactions on Power Systems, 31, 3850-3863.
https://doi.org/10.1109/tpwrs.2015.2502423

has been cited by the following article:

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

  AUTHORS: Juliana Marian Arrais, Allan Cerentini, Bruno Juncklaus Martins, Thiago Zimmermann Loureiro Chaves, Sylvio Luiz Mantelli Neto, Aldo von Wangenheim

  KEYWORDS: Nowcasting, Photovoltaic, Image Processing

  JOURNAL NAME: American Journal of Climate Change, Vol.13 No.3, August 14, 2024

  ABSTRACT: 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.