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Six Days Ahead Forecasting of Energy Production of Small Behind-the-Meter Solar Sites

Author

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  • Hugo Bezerra Menezes Leite

    (Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada)

  • Hamidreza Zareipour

    (Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada)

Abstract

Due to the growing penetration of behind-the-meter (BTM) photovoltaic (PV) installations, accurate solar energy forecasts are required for a reliable economic energy system operation. A new hybrid methodology is proposed in this paper with a sequence of one-step ahead models to accumulate 144 h for a small-scale BTM PV site. Three groups of models with different inputs are developed to cover 6 days of forecasting horizon, with each group trained for each hour of the above zero irradiance. In addition, a novel dataset preselection is proposed, and neighboring solar farms’ power predictions are used as a feature to boost the accuracy of the model. Two techniques are selected: XGBoost and CatBoost. An extensive assessment for 1 year is conducted to evaluate the proposed method. Numerical results highlight that training the models with the previous, current, and 1 month ahead from the previous year referenced by the target month can improve the model’s accuracy. Finally, when solar energy predictions from neighboring solar farms are incorporated, this further increases the overall forecast accuracy. The proposed method is compared with the complete-history persistence ensemble (CH-PeEn) model as a benchmark.

Suggested Citation

  • Hugo Bezerra Menezes Leite & Hamidreza Zareipour, 2023. "Six Days Ahead Forecasting of Energy Production of Small Behind-the-Meter Solar Sites," Energies, MDPI, vol. 16(3), pages 1-14, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1533-:d:1057124
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    References listed on IDEAS

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    Cited by:

    1. Domenico Palladino & Nicolandrea Calabrese, 2023. "Energy Planning of Renewable Energy Sources in an Italian Context: Energy Forecasting Analysis of Photovoltaic Systems in the Residential Sector," Energies, MDPI, vol. 16(7), pages 1-28, March.
    2. Tomasz Popławski & Sebastian Dudzik & Piotr Szeląg, 2023. "Forecasting of Energy Balance in Prosumer Micro-Installations Using Machine Learning Models," Energies, MDPI, vol. 16(18), pages 1-24, September.
    3. Grzegorz Dudek & Paweł Piotrowski & Dariusz Baczyński, 2023. "Intelligent Forecasting and Optimization in Electrical Power Systems: Advances in Models and Applications," Energies, MDPI, vol. 16(7), pages 1-11, March.

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