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

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  • Cheng, Hsu-Yung

Abstract

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.

Suggested Citation

  • Cheng, Hsu-Yung, 2017. "Cloud tracking using clusters of feature points for accurate solar irradiance nowcasting," Renewable Energy, Elsevier, vol. 104(C), pages 281-289.
  • Handle: RePEc:eee:renene:v:104:y:2017:i:c:p:281-289
    DOI: 10.1016/j.renene.2016.12.023
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    1. Angelis-Dimakis, Athanasios & Biberacher, Markus & Dominguez, Javier & Fiorese, Giulia & Gadocha, Sabine & Gnansounou, Edgard & Guariso, Giorgio & Kartalidis, Avraam & Panichelli, Luis & Pinedo, Irene, 2011. "Methods and tools to evaluate the availability of renewable energy sources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(2), pages 1182-1200, February.
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    3. Cheng, Hsu-Yung, 2016. "Hybrid solar irradiance now-casting by fusing Kalman filter and regressor," Renewable Energy, Elsevier, vol. 91(C), pages 434-441.
    4. Cheng, Hsu-Yung & Yu, Chih-Chang & Lin, Sian-Jing, 2014. "Bi-model short-term solar irradiance prediction using support vector regressors," Energy, Elsevier, vol. 70(C), pages 121-127.
    5. Cheng, Hsu-Yung & Yu, Chih-Chang, 2015. "Multi-model solar irradiance prediction based on automatic cloud classification," Energy, Elsevier, vol. 91(C), pages 579-587.
    6. Lund, Henrik, 2007. "Renewable energy strategies for sustainable development," Energy, Elsevier, vol. 32(6), pages 912-919.
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    Cited by:

    1. Niu, Yinsen & Song, Jifeng & Zou, Lianglin & Yan, Zixuan & Lin, Xilong, 2024. "Cloud detection method using ground-based sky images based on clear sky library and superpixel local threshold," Renewable Energy, Elsevier, vol. 226(C).
    2. Terrén-Serrano, Guillermo & Martínez-Ramón, Manel, 2021. "Multi-layer wind velocity field visualization in infrared images of clouds for solar irradiance forecasting," Applied Energy, Elsevier, vol. 288(C).
    3. Muhammad Naveed Akhter & Saad Mekhilef & Hazlie Mokhlis & Ziyad M. Almohaimeed & Munir Azam Muhammad & Anis Salwa Mohd Khairuddin & Rizwan Akram & Muhammad Majid Hussain, 2022. "An Hour-Ahead PV Power Forecasting Method Based on an RNN-LSTM Model for Three Different PV Plants," Energies, MDPI, vol. 15(6), pages 1-21, March.
    4. Garcia, Dário & Liang, Dawei & Almeida, Joana & Catela, Miguel & Costa, Hugo & Tibúrcio, Bruno D. & Guillot, Emmanuel & Vistas, Cláudia R., 2023. "Lowest-threshold solar laser operation under cloudy sky condition," Renewable Energy, Elsevier, vol. 210(C), pages 127-133.
    5. Carpentieri, A. & Folini, D. & Nerini, D. & Pulkkinen, S. & Wild, M. & Meyer, A., 2023. "Intraday probabilistic forecasts of surface solar radiation with cloud scale-dependent autoregressive advection," Applied Energy, Elsevier, vol. 351(C).
    6. Han, Tian & Li, Ruimeng & Wang, Xiao & Wang, Ying & Chen, Kang & Peng, Huaiwu & Gao, Zhenxin & Wang, Nannan & Peng, Qinke, 2024. "Intra-hour solar irradiance forecasting using topology data analysis and physics-driven deep learning," Renewable Energy, Elsevier, vol. 224(C).
    7. Eşlik, Ardan Hüseyin & Akarslan, Emre & Hocaoğlu, Fatih Onur, 2022. "Short-term solar radiation forecasting with a novel image processing-based deep learning approach," Renewable Energy, Elsevier, vol. 200(C), pages 1490-1505.
    8. Guilherme Fonseca Bassous & Rodrigo Flora Calili & Carlos Hall Barbosa, 2021. "Development of a Low-Cost Data Acquisition System for Very Short-Term Photovoltaic Power Forecasting," Energies, MDPI, vol. 14(19), pages 1-28, September.
    9. Lin, Fan & Zhang, Yao & Wang, Jianxue, 2023. "Recent advances in intra-hour solar forecasting: A review of ground-based sky image methods," International Journal of Forecasting, Elsevier, vol. 39(1), pages 244-265.

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