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A Low Cost, Edge Computing, All-Sky Imager for Cloud Tracking and Intra-Hour Irradiance Forecasting

Author

Listed:
  • Walter Richardson

    (Department of Mathematics, University of Texas at San Antonio, San Antonio, TX 78249, USA)

  • Hariharan Krishnaswami

    (Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA)

  • Rolando Vega

    (CPS Energy, San Antonio, TX, 78205, USA)

  • Michael Cervantes

    (Merrick & Company, San Antonio, TX 78258, USA)

Abstract

With increasing use of photovoltaic (PV) power generation by utilities and their residential customers, the need for accurate intra-hour and day-ahead solar irradiance forecasting has become critical. This paper details the development of a low cost all-sky imaging system and an intra-hour cloud motion prediction methodology that produces minutes-ahead irradiance forecasts. The SkyImager is designed around a Raspberry Pi single board computer (SBC) with a fully programmable, high resolution Pi Camera, housed in a durable all-weather enclosure. Our software is written in Python 2.7 and utilizes the open source computer vision package OpenCV . The SkyImager can be configured for different operational environments and network designs, from a standalone edge computing model to a fully integrated node in a distributed, cloud-computing based micro-grid. Preliminary results are presented using the imager on site at the National Renewable Energy Laboratory (NREL) in Golden, CO, USA during the fall of 2015 under a variety of cloud conditions.

Suggested Citation

  • Walter Richardson & Hariharan Krishnaswami & Rolando Vega & Michael Cervantes, 2017. "A Low Cost, Edge Computing, All-Sky Imager for Cloud Tracking and Intra-Hour Irradiance Forecasting," Sustainability, MDPI, vol. 9(4), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:4:p:482-:d:93911
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    References listed on IDEAS

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    1. Voyant, Cyril & Soubdhan, Ted & Lauret, Philippe & David, Mathieu & Muselli, Marc, 2015. "Statistical parameters as a means to a priori assess the accuracy of solar forecasting models," Energy, Elsevier, vol. 90(P1), pages 671-679.
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    Cited by:

    1. Javier López Gómez & Ana Ogando Martínez & Francisco Troncoso Pastoriza & Lara Febrero Garrido & Enrique Granada Álvarez & José Antonio Orosa García, 2020. "Photovoltaic Power Prediction Using Artificial Neural Networks and Numerical Weather Data," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    2. Ariana Moncada & Walter Richardson & Rolando Vega-Avila, 2018. "Deep Learning to Forecast Solar Irradiance Using a Six-Month UTSA SkyImager Dataset," Energies, MDPI, vol. 11(8), pages 1-16, July.
    3. Qu, Yinpeng & Xu, Jian & Sun, Yuanzhang & Liu, Dan, 2021. "A temporal distributed hybrid deep learning model for day-ahead distributed PV power forecasting," Applied Energy, Elsevier, vol. 304(C).
    4. Manisha Sawant & Mayur Kishor Shende & Andrés E. Feijóo-Lorenzo & Neeraj Dhanraj Bokde, 2021. "The State-of-the-Art Progress in Cloud Detection, Identification, and Tracking Approaches: A Systematic Review," Energies, MDPI, vol. 14(23), pages 1-26, December.
    5. Alessandro Niccolai & Seyedamir Orooji & Andrea Matteri & Emanuele Ogliari & Sonia Leva, 2022. "Irradiance Nowcasting by Means of Deep-Learning Analysis of Infrared Images," Forecasting, MDPI, vol. 4(1), pages 1-11, March.
    6. Si, Zhiyuan & Yang, Ming & Yu, Yixiao & Ding, Tingting, 2021. "Photovoltaic power forecast based on satellite images considering effects of solar position," Applied Energy, Elsevier, vol. 302(C).
    7. Nie, Yuhao & Li, Xiatong & Paletta, Quentin & Aragon, Max & Scott, Andea & Brandt, Adam, 2024. "Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    8. Zheng, Lingwei & Liu, Zhaokun & Shen, Junnan & Wu, Chenxi, 2018. "Very short-term maximum Lyapunov exponent forecasting tool for distributed photovoltaic output," Applied Energy, Elsevier, vol. 229(C), pages 1128-1139.
    9. Kong, Weicong & Jia, Youwei & Dong, Zhao Yang & Meng, Ke & Chai, Songjian, 2020. "Hybrid approaches based on deep whole-sky-image learning to photovoltaic generation forecasting," Applied Energy, Elsevier, vol. 280(C).
    10. Francis M. Lopes & Ricardo Conceição & Hugo G. Silva & Thomas Fasquelle & Rui Salgado & Paulo Canhoto & Manuel Collares-Pereira, 2019. "Short-Term Forecasts of DNI from an Integrated Forecasting System (ECMWF) for Optimized Operational Strategies of a Central Receiver System," Energies, MDPI, vol. 12(7), pages 1-18, April.
    11. 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.
    12. Rosen, Karol & Angeles-Camacho, César & Elvira, Víctor & Guillén-Burguete, Servio Tulio, 2023. "Intra-hour photovoltaic forecasting through a time-varying Markov switching model," Energy, Elsevier, vol. 278(PB).
    13. 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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