Ghali Raja Yakoub

• Yakoub, Ghali; Mathew, Sathyajith & Leal, J B (2023). Direct and indirect short-term aggregated turbine- and farm-level wind power forecasts integrating several NWP sources. Heliyon. ISSN 2405-8440. 9(11). doi: 10.1016/j.heliyon.2023.e21479. Full text in Research Archive
• Yakoub, Ghali; Mathew, Sathyajith & Leal, João Gouveia Aparício Bento (2022). Intelligent estimation of wind farm performance with direct and indirect ‘point’ forecasting approaches integrating several NWP models. Energy. ISSN 0360-5442. 263(D). doi: 10.1016/j.energy.2022.125893. Full text in Research Archive Show summary

  Reliable wind power forecasting is essential for profitably trading wind energy in the electricity market and efficiently integrating wind-generated electricity into the power grids. In this paper, we propose short- and medium-term wind power forecasting systems targeted to the Nordic energy market, which integrate inputs on the wind flow conditions from three numerical weather prediction sources. A point forecasting scheme is adopted, which forecasts the power at the individual turbine level. Both direct and indirect forecasting approaches are considered and compared. An automated machine-learning pipeline, built and optimized using genetic programming, is implemented for developing the proposed forecasting models. The turbine level power forecasts using different approaches are then combined into a single forecast using a weighting method based on recent forecast errors. These are then aggregated for the wind farm level power estimates. The proposed forecasting schemes are implemented with data from a Norwegian wind farm. We found that in both the direct and indirect forecasting approaches, the forecasting errors could be reduced between 8% and 22%, while inputs from several NWP sources are used together. The wind downscaling model, which is used in the indirect forecasting approach, could significantly contribute to the model's accuracy. The performance of both the direct and indirect forecasting schemes is comparable for the studied wind farm.

• Yakoub, Ghali; Mathew, Sathyajith & Leal, J B (2022). Power production forecast for distributed wind energy systems using support vector regression. Energy Science & Engineering. ISSN 2050-0505. p. 1–12. doi: 10.1002/ese3.1295. Full text in Research Archive Show summary

  Due to the inherent intermittency in wind power production, reliable short-term wind power production forecasting has become essential for the efficient grid and market integration of wind energy. The current wind power production forecasting schemes are predominantly developed for wind farms. With the rapid growth in the microgrid sector and the increasing number of wind turbines integrated with these local grids, power production forecasting schemes are becoming essential for distributed wind energy systems as well. This paper proposes a power production forecasting scheme developed explicitly for distributed wind energy projects. The proposed system integrates two submodels based on support vector regression: one for downscaling the wind speed predictions to the hub coordinates of the turbine and the other for predicting the site-specific performance of the turbine under this wind condition. The forecasting horizons considered are the hour ahead (t + 1) and the day ahead (t + 36), which align with the Nord pool's energy market requirements. For the day-ahead scheme, a multistep recursive approach is adopted. The accuracy and adaptability of the proposed forecasting scheme are demonstrated in the case of a distributed wind turbine.

• Yakoub, Ghali; Mathew, Sathyajith & Leal, J B (2020). Downscaling and improving the wind forecasts from NWP for wind energy applications using support vector regression. Journal of Physics: Conference Series (JPCS). ISSN 1742-6588. 1618. doi: 10.1088/1742-6596/1618/6/062034. Full text in Research Archive
• Yakoub, Ghali; Leal, J B & Tunheim, Ketil (2019). A flood forecasting model for unregulated catchments based on Artificial Neural Networks: the October 2017 flood in Tovdal’s catchment. Vann. ISSN 0042-2592. p. 103–113.

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