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A Multi-Step Ensemble Approach for Energy Community Day-Ahead Net Load Point and Probabilistic Forecasting

Author

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  • Maria da Graça Ruano

    (Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal
    CISUC, University of Coimbra, 3030-290 Coimbra, Portugal)

  • Antonio Ruano

    (Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal
    IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal)

Abstract

The incorporation of renewable energy systems in the world energy system has been steadily increasing during the last few years. In terms of the building sector, the usual consumers are becoming increasingly prosumers, and the trend is that communities of energy, whose households share produced electricity, will increase in number in the future. Another observed tendency is that the aggregator (the entity that manages the community) trades the net community energy in public energy markets. To accomplish economically good transactions, accurate and reliable forecasts of the day-ahead net energy community must be available. These can be obtained using an ensemble of multi-step shallow artificial neural networks, with prediction intervals obtained by the covariance algorithm. Using real data obtained from a small energy community of four houses located in the southern region of Portugal, one can verify that the deterministic and probabilistic performance of the proposed approach is at least similar, typically better than using complex, deep models.

Suggested Citation

  • Maria da Graça Ruano & Antonio Ruano, 2024. "A Multi-Step Ensemble Approach for Energy Community Day-Ahead Net Load Point and Probabilistic Forecasting," Energies, MDPI, vol. 17(3), pages 1-49, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:3:p:696-:d:1330901
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    References listed on IDEAS

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    1. Ufuk Beyaztas & Han Lin Shang, 2022. "Robust bootstrap prediction intervals for univariate and multivariate autoregressive time series models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 49(5), pages 1179-1202, April.
    2. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    3. Kath, Christopher & Ziel, Florian, 2021. "Conformal prediction interval estimation and applications to day-ahead and intraday power markets," International Journal of Forecasting, Elsevier, vol. 37(2), pages 777-799.
    4. van der Meer, D.W. & Widén, J. & Munkhammar, J., 2018. "Review on probabilistic forecasting of photovoltaic power production and electricity consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1484-1512.
    5. Grothe, Oliver & Kächele, Fabian & Krüger, Fabian, 2023. "From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting," Energy Economics, Elsevier, vol. 120(C).
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