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Analog versus multi-model ensemble forecasting: A comparison for renewable energy resources

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  • Pappa, Areti
  • Theodoropoulos, Ioannis
  • Galmarini, Stefano
  • Kioutsioukis, Ioannis

Abstract

To satisfy the energy demand from renewable sources, accurate weather predictions are necessary. The Analog ensemble (AnEn) technique aims to correct a (weather) prediction given historical observational data. In this work, the AnEn is applied to the wind speed and solar radiation predictions used in the AQMEII multi-model ensemble, spanning a whole year, to produce probabilistic forecasts over Europe. The skill of each deterministic model in forecasting the wind speed, the solar radiation and the respective renewable energy potential is compared to the skill of the AnEn as well as to the skill of the multi-model ensemble mean, either unconstrained (mme) or analytically optimized (mmeW). Results show that the AnEn significantly improves the wind (radiation) forecast skill of the numerical models in the range 25–43% (13–24%), being larger for moderate or low skill models. Compared to mme, the AnEn improvement is larger across all quartiles except the upper one. AnEn and mme are mostly comparable with the mmeW at intermediate values of wind speed and solar radiation. At higher values, the AnEn should benefit from additional auxiliary inputs and a larger dataset. A hybrid model combining the advantages of AnEn and mmeW and providing even more accurate forecasts is proposed.

Suggested Citation

  • Pappa, Areti & Theodoropoulos, Ioannis & Galmarini, Stefano & Kioutsioukis, Ioannis, 2023. "Analog versus multi-model ensemble forecasting: A comparison for renewable energy resources," Renewable Energy, Elsevier, vol. 205(C), pages 563-573.
    • Handle: RePEc:eee:renene:v:205:y:2023:i:c:p:563-573
    DOI: 10.1016/j.renene.2023.01.030
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    References listed on IDEAS

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    1. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Nissen, J.N., 2015. "A novel application of an analog ensemble for short-term wind power forecasting," Renewable Energy, Elsevier, vol. 76(C), pages 768-781.
    2. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Cervone, G., 2015. "An analog ensemble for short-term probabilistic solar power forecast," Applied Energy, Elsevier, vol. 157(C), pages 95-110.
    3. AlSkaif, Tarek & Dev, Soumyabrata & Visser, Lennard & Hossari, Murhaf & van Sark, Wilfried, 2020. "A systematic analysis of meteorological variables for PV output power estimation," Renewable Energy, Elsevier, vol. 153(C), pages 12-22.
    4. Drisya, G.V. & Asokan, K. & Kumar, K. Satheesh, 2018. "Diverse dynamical characteristics across the frequency spectrum of wind speed fluctuations," Renewable Energy, Elsevier, vol. 119(C), pages 540-550.
    5. Zhang, Gang & Yang, Dazhi & Galanis, George & Androulakis, Emmanouil, 2022. "Solar forecasting with hourly updated numerical weather prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    6. Shahriari, M. & Cervone, G. & Clemente-Harding, L. & Delle Monache, L., 2020. "Using the analog ensemble method as a proxy measurement for wind power predictability," Renewable Energy, Elsevier, vol. 146(C), pages 789-801.
    7. Cervone, Guido & Clemente-Harding, Laura & Alessandrini, Stefano & Delle Monache, Luca, 2017. "Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble," Renewable Energy, Elsevier, vol. 108(C), pages 274-286.
    8. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
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