The WPPT base module consists of the WPPT engine; a time controlled system with automatic retries which controls the internal and external data flows. The WPPT base module also includes the well proven WPPT power curve and dynamical models which are some of the most accurate forecast models using standard meteorological forecast input.
WPPT optimal forecast combination
Optimally combine MET forecasts from several providers in order to increase forecast performance and operational robustness. The module continuously measures forecast performance and correlation of forecast errors for the individual meteorological forecasts and based on this information an optimal weighting is derived and used to generate the final forecast.
WPPT high resolution forecasting
The module produce forecasts with high resolution in time, e.g. forecasts for time intervals of 5 minutes or even less. On such short time intervals it has been observed that in some situations the wind power production is quite stable over time, whereas in other situations the wind power production is quite variable. This variability can be measured and used to characterize the current situation. Based on the current variability of the wind power production and the meteorological (interpolated) wind speed forecast, the model derives the optimal weighting between a simple (interpolated) wind power forecast and the latest observed wind power production.
WPPT quantile regression
Using regular meteorological forecasts the WPPT quantile regression module produce forecasts of the future situation specific uncertainty as full probabilistic forecasts communicated as quantile forecasts. As an example the 20% quantile forecast is the level for which the actual production is lower in only 20% of all cases and the 80% quantile forecast is the level for which the actual production is higher in only 20% of all cases. Opposed to ensemble based quantile forecasts the quantile forecasts based on regular meteorological forecasts are preferable within the first few days where the ensemble spread is known to be unrealistic small.
WPPT ensemble forecasting
Using meteorological ensemble forecasts the WPPT ensemble forecasting module produce forecasts of the future situation specific uncertainty as full probabilistic forecasts communicated as quantile forecasts. Opposed to forecasts based on quantile regression the ensemble based quantile forecasts are preferable for longer horizons. Due to a number of factors related to scale and the particular methods behind meteorological ensemble forecasting the quantiles can not be derived from the meteorological ensemble forecasts in a simple way. Instead statistical methods are applied in order to ensure the reliability of the quantile forecasts. This module is under development. However, the models and meteorology are fully developed. Only outstanding issue is full integration into the WPPT framework.