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Very Short-Term Forecast: Different Classification Methods of the Whole Sky Camera Images for Sudden PV Power Variations Detection

Author

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  • Alessandro Niccolai

    (Department of Energy, Politecnico di Milano, Via Giuseppe La Masa, 34, 20156 Milan, Italy)

  • Emanuele Ogliari

    (Department of Energy, Politecnico di Milano, Via Giuseppe La Masa, 34, 20156 Milan, Italy)

  • Alfredo Nespoli

    (Department of Energy, Politecnico di Milano, Via Giuseppe La Masa, 34, 20156 Milan, Italy)

  • Riccardo Zich

    (Department of Energy, Politecnico di Milano, Via Giuseppe La Masa, 34, 20156 Milan, Italy)

  • Valentina Vanetti

    (Department of Energy, Politecnico di Milano, Via Giuseppe La Masa, 34, 20156 Milan, Italy)

Abstract

Solar radiation is by nature intermittent and influenced by many factors such as latitude, season and atmospheric conditions. As a consequence, the growing penetration of Photovoltaic (PV) systems into the electricity network implies significant problems of stability, reliability and scheduling of power grid operation. Concerning the very short-term PV power production, the power fluctuations are primarily related to the interaction between solar irradiance and cloud cover. In small-scale systems such as microgrids, the adoption of a forecasting tool is a brilliant solution to minimize PV power curtailment and limit the installed energy storage capacity. In the present work, two different nowcasting methods are applied to classify the solar attenuation due to clouds presence on five different forecast horizons, from 1 to 5 min: a Pattern Recognition Neural Network and a Random Forest model. The proposed methods are tested and compared on a real case study: available data consists of historical irradiance measurements and infrared sky images collected in a real PV facility, the SolarTech LAB in Politecnico di Milano. The classification output is a range of values corresponding to the future value assumed by the Clear Sky Index (CSI), an indicator allowing to account for irradiance variations only related to clouds passage, neglecting diurnal and seasonal influences. The developed models present similar performance in all the considered time horizons, reliably detecting the CSI drops caused by incoming overcast and partially cloudy sky conditions.

Suggested Citation

  • Alessandro Niccolai & Emanuele Ogliari & Alfredo Nespoli & Riccardo Zich & Valentina Vanetti, 2022. "Very Short-Term Forecast: Different Classification Methods of the Whole Sky Camera Images for Sudden PV Power Variations Detection," Energies, MDPI, vol. 15(24), pages 1-16, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9433-:d:1002079
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    References listed on IDEAS

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    1. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    2. Keda Pan & Changhong Xie & Chun Sing Lai & Dongxiao Wang & Loi Lei Lai, 2020. "Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems," Forecasting, MDPI, vol. 2(4), pages 1-18, November.
    3. Wang, Fei & Lu, Xiaoxing & Mei, Shengwei & Su, Ying & Zhen, Zhao & Zou, Zubing & Zhang, Xuemin & Yin, Rui & Duić, Neven & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "A satellite image data based ultra-short-term solar PV power forecasting method considering cloud information from neighboring plant," Energy, Elsevier, vol. 238(PC).
    4. Nespoli, Alfredo & Niccolai, Alessandro & Ogliari, Emanuele & Perego, Giovanni & Collino, Elena & Ronzio, Dario, 2022. "Machine Learning techniques for solar irradiation nowcasting: Cloud type classification forecast through satellite data and imagery," Applied Energy, Elsevier, vol. 305(C).
    5. Alessandro Niccolai & Alfredo Nespoli, 2020. "Sun Position Identification in Sky Images for Nowcasting Application," Forecasting, MDPI, vol. 2(4), pages 1-17, November.
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