Downscaling and improving the wind forecasts from NWP for wind energy applications using support vector regression

acceptedVersio

Power production forecast for distributed wind energy systems using support vector regression

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.publishedVersio

Direct and indirect short-term aggregated turbine- and farm-level wind power forecasts integrating several NWP sources

The wind power sector is experiencing rapid growth, which creates new challenges for its electricity grid integration. Accurate wind power forecasting (WPF) is crucial for trading, balancing, and dispatching wind energy. In this paper, we examine the use of aggregated turbine- and farmlevel WPFs in the Nordic energy market. The turbine-level WPFs were retrieved from a previous study, while the farm-level WPFs were developed using the same methodology, incorporating inputs from three different numerical weather predictions (NWPs) and implementing both direct and indirect forecasting approaches. In the indirect WPF approach, we explore the impact of using wind direction as an input for the wind farm-level power performance model. The different WPFs are combined into one using weights related to up-to-date forecast errors. An automated and optimized machine-learning pipeline using data from a Norwegian wind farm is used to implement the proposed forecasting methods. The indirect approach, that uses the wind-downscaling model, improves the wind speed forecast accuracy compared to raw forecasts from the relevant NWPs. Additionally, we observed that the farm-level downscaling model exhibited lower error than those developed at the turbine level. The combined use of multiple NWP sources reduced forecasting errors by 8 %–30 % for direct and indirect WPFs, respectively. Direct and indirect forecasting methods present similar performance. Finally, the aggregated turbine-level improved WPF accuracy by 10 % and 15 % for RMSE and MAE, respectively, compared to farm-level WPF.publishedVersio

Intelligent estimation of wind farm performance with direct and indirect ‘point’ forecasting approaches integrating several NWP models

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.publishedVersionPaid Open Acces

Downscaling and improving the wind forecasts from NWP for wind energy applications using support vector regression

acceptedVersio