Today, most branches of the energy business rely on an exact forecast of the future wind power input. Power plant scheduling, direct marketing, power trading and grid operations can only be carried out when an exact, reliable and permanently available prediction of wind power exists for the coming hours and days.
As a general rule, high forecast accuracy is dependant on an optimal combination of wind farm SCADA data representing current production and forecast data from weather services. In many practical situations, however, the online availability of SCADA data is problematic as it frequently arrives delayed at the forecasting system, does not have the necessary time resolution or needs correcting or other post-processing steps.
In ideal cases, in which SCADA data are both online and of high-quality, statistical prediction models generally produce the greatest degree of forecast accuracy. In all other cases, physical modelling approaches are generally superior as they are more robust when it comes to problematic measured data.
In developing the wind power prediction model OSHybrid, Overspeed has achieved a new hybrid model which combines the advantages of both physical and statistical modelling. The model's core is the description of a given wind farm and its surroundings with physical parameters such as power curves, terrain roughness and orography. The output of this model is corrected with a statistical model which takes the historic time series of the wind farm power into account. The optimization of the model is adaptive, for example monthly. As soon as the model detects larger deviations between model parameters and past tuning parameters, these changes are no longer integrated into the online forecast, but are rather first assessed by an expert and corrected if necessary.
This procedure insures that all advantages of physical and statistical approaches are combined: