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Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy

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  • Xwégnon Ghislain Agoua

    (Centre for Processes, Renewable Energies and Energy Systems (PERSEE), MINES ParisTech, PSL University, CS 10207, 1 rue Claude Daunesse, 06904 Sophia Antipolis, France)

  • Robin Girard

    (Centre for Processes, Renewable Energies and Energy Systems (PERSEE), MINES ParisTech, PSL University, CS 10207, 1 rue Claude Daunesse, 06904 Sophia Antipolis, France)

  • Georges Kariniotakis

    (Centre for Processes, Renewable Energies and Energy Systems (PERSEE), MINES ParisTech, PSL University, CS 10207, 1 rue Claude Daunesse, 06904 Sophia Antipolis, France)

Abstract

The efficient integration of photovoltaic (PV) production in energy systems is conditioned by the capacity to anticipate its variability, that is, the capacity to provide accurate forecasts. From the classical forecasting methods in the state of the art dealing with a single power plant, the focus has moved in recent years to spatio-temporal approaches, where geographically dispersed data are used as input to improve forecasts of a site for the horizons up to 6 h ahead. These spatio-temporal approaches provide different performances according to the data sources available but the question of the impact of each source on the actual forecasting performance is still not evaluated. In this paper, we propose a flexible spatio-temporal model to generate PV production forecasts for horizons up to 6 h ahead and we use this model to evaluate the effect of different spatial and temporal data sources on the accuracy of the forecasts. The sources considered are measurements from neighboring PV plants, local meteorological stations, Numerical Weather Predictions, and satellite images. The evaluation of the performance is carried out using a real-world test case featuring a high number of 136 PV plants. The forecasting error has been evaluated for each data source using the Mean Absolute Error and Root Mean Square Error. The results show that neighboring PV plants help to achieve around 10% reduction in forecasting error for the first three hours, followed by satellite images which help to gain an additional 3% all over the horizons up to 6 h ahead. The NWP data show no improvement for horizons up to 6 h but is essential for greater horizons.

Suggested Citation

  • Xwégnon Ghislain Agoua & Robin Girard & Georges Kariniotakis, 2021. "Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy," Energies, MDPI, vol. 14(5), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1432-:d:511370
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    References listed on IDEAS

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    Cited by:

    1. Llinet Benavides Cesar & Rodrigo Amaro e Silva & Miguel Ángel Manso Callejo & Calimanut-Ionut Cira, 2022. "Review on Spatio-Temporal Solar Forecasting Methods Driven by In Situ Measurements or Their Combination with Satellite and Numerical Weather Prediction (NWP) Estimates," Energies, MDPI, vol. 15(12), pages 1-23, June.
    2. Llinet Benavides Cesar & Miguel Ángel Manso Callejo & Calimanut-Ionut Cira & Ramon Alcarria, 2023. "CyL-GHI: Global Horizontal Irradiance Dataset Containing 18 Years of Refined Data at 30-Min Granularity from 37 Stations Located in Castile and León (Spain)," Data, MDPI, vol. 8(4), pages 1-21, March.
    3. Venizelos Efthymiou & Christina N. Papadimitriou, 2022. "Smart Photovoltaic Energy Systems for a Sustainable Future," Energies, MDPI, vol. 15(18), pages 1-3, September.
    4. He Yin & Hai Lan & Ying-Yi Hong & Zhuangwei Wang & Peng Cheng & Dan Li & Dong Guo, 2023. "A Comprehensive Review of Shipboard Power Systems with New Energy Sources," Energies, MDPI, vol. 16(5), pages 1-44, February.
    5. Daisuke Kodaira & Kazuki Tsukazaki & Taiki Kure & Junji Kondoh, 2021. "Improving Forecast Reliability for Geographically Distributed Photovoltaic Generations," Energies, MDPI, vol. 14(21), pages 1-15, November.
    6. Ladislav Zjavka, 2021. "Photovoltaic Energy All-Day and Intra-Day Forecasting Using Node by Node Developed Polynomial Networks Forming PDE Models Based on the L-Transformation," Energies, MDPI, vol. 14(22), pages 1-14, November.

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