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A method for detailed, short-term energy yield forecasting of photovoltaic installations

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  • Anagnostos, D.
  • Schmidt, T.
  • Cavadias, S.
  • Soudris, D.
  • Poortmans, J.
  • Catthoor, F.

Abstract

The global shift towards renewable energy production combined with the expected penetration of electric cars, increasing energy usage of cloud computing centers and the transformation of the electricity grid itself towards the “Smart Grid” requires novel solutions on all levels of energy production and management. Forecasting of energy production especially will become a major component for design and operation in all temporal and spatial scales, creating opportunities for optimized control of energy storage, local energy exchange etc. To this end, a method for the creation of detailed and accurate energy yield forecasts for PV installations is presented. Based on sky-imager information and using tailored neural networks, highly detailed energy yield forecasts are produced for a monitored test installation, for horizons up to 15 min and with a resolution of 1 s. Thermal effects are included in the calculations and error propagation is minimized by reducing the modeling steps. The described method manages to outperform state of the art models by up to 39% in forecast skill, while at the same time retaining temporal resolutions that enable control schemes and energy exchange in a local scale.

Suggested Citation

  • Anagnostos, D. & Schmidt, T. & Cavadias, S. & Soudris, D. & Poortmans, J. & Catthoor, F., 2019. "A method for detailed, short-term energy yield forecasting of photovoltaic installations," Renewable Energy, Elsevier, vol. 130(C), pages 122-129.
    • Handle: RePEc:eee:renene:v:130:y:2019:i:c:p:122-129
    DOI: 10.1016/j.renene.2018.06.058
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    References listed on IDEAS

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    1. Chu, Yinghao & Coimbra, Carlos F.M., 2017. "Short-term probabilistic forecasts for Direct Normal Irradiance," Renewable Energy, Elsevier, vol. 101(C), pages 526-536.
    2. Maria Avgerinou & Paolo Bertoldi & Luca Castellazzi, 2017. "Trends in Data Centre Energy Consumption under the European Code of Conduct for Data Centre Energy Efficiency," Energies, MDPI, vol. 10(10), pages 1-18, September.
    3. Barbieri, Florian & Rajakaruna, Sumedha & Ghosh, Arindam, 2017. "Very short-term photovoltaic power forecasting with cloud modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 242-263.
    4. Elsinga, Boudewijn & van Sark, Wilfried G.J.H.M., 2017. "Short-term peer-to-peer solar forecasting in a network of photovoltaic systems," Applied Energy, Elsevier, vol. 206(C), pages 1464-1483.
    5. Tzoumanikas, P. & Nikitidou, E. & Bais, A.F. & Kazantzidis, A., 2016. "The effect of clouds on surface solar irradiance, based on data from an all-sky imaging system," Renewable Energy, Elsevier, vol. 95(C), pages 314-322.
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    3. Jamal, Taskin & Carter, Craig & Schmidt, Thomas & Shafiullah, G.M. & Calais, Martina & Urmee, Tania, 2019. "An energy flow simulation tool for incorporating short-term PV forecasting in a diesel-PV-battery off-grid power supply system," Applied Energy, Elsevier, vol. 254(C).
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    5. Samu, Remember & Calais, Martina & Shafiullah, G.M. & Moghbel, Moayed & Shoeb, Md Asaduzzaman & Nouri, Bijan & Blum, Niklas, 2021. "Applications for solar irradiance nowcasting in the control of microgrids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    6. Zhang, Liwenbo & Wilson, Robin & Sumner, Mark & Wu, Yupeng, 2023. "Advanced multimodal fusion method for very short-term solar irradiance forecasting using sky images and meteorological data: A gate and transformer mechanism approach," Renewable Energy, Elsevier, vol. 216(C).
    7. Zhenxing Zhao & Kaijie Chen & Ying Chen & Yuxing Dai & Zeng Liu & Kuiyin Zhao & Huan Wang & Zishun Peng, 2021. "An Ultra-Fast Power Prediction Method Based on Simplified LSSVM Hyperparameters Optimization for PV Power Smoothing," Energies, MDPI, vol. 14(18), pages 1-15, September.

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