Wind Power Prediction Tool

The Wind Power Prediction Tool or WPPT can be used for generating short-term (hours / days) predictions of the wind power production. The system is very flexible and it can be configured to cover the total wind power (eg. for Denmark), the total wind power in a region (like the Northern part of Jutland), or a single wind farm (like the Horns Rev off-shore wind farm).

History​

The development of WPPT started in 1992, and the system has been in used by a number of TSO's since 1996.

Input to WPPT​

WPPT is build on artificial intelligence, and hence the system automatically calibrates to the observed situation. In the minimal setup the system requires measurements of the wind power production. However depending on the configuration the following data is taking into account:

  • On-line measurements of wind power production.
  • Aggregated energy readings form all (or nearly all) turbines in a region (for regional forecasting).
  • Meteorological forecasts of wind speed and direction covering wind farms and regions.
  • Measurements of availability and curtailment
  • Schedules of availability and curtailment
  • Other measurements or predictions like local wind speed, stability, number of active turbines, etc. can be used if available.

The input can be given in a number of text-based formats.

Output from WPPT​

WPPT typically generates predictions of the wind power up to the configured horizon, say 120 hours. The system also provides reliable estimates of the uncertainty of the predictions - which is very important for optimal scheduling or trading.

The following paper and presentation from WIW-2011, Aarhus, Denmark discuss cases in which cases advanced forecast products such as probabilistic forecasts or scenarios are advantageous.

Methods used in WPPT​

WPPT is based on advanced non-linear statistical models and artificial intelligence. The set of models includes a semi-parametric power curve model for wind farms taking into account both wind speed and direction, and dynamical predictions models taking describing the dynamics of the wind power and any diurnal variation, etc.


The models are self calibrating and adaptive. Hence they automatically account for

  • Changes in the number of turbines and their characteristics
  • Changes in surroundings and non-explicit modelled characteristics like roughness and dirty blades.
  • Changes in the NWP models.


For a more rigorous description of the models and methods we refer to our technical papers or the following presentation.

​​​

WPPT modules​

WPPT consists of a number of modules which can be combined in order to achieve the desired functionality.Read more.

​​

WPPT as a web-service​

If desired WPPT can be installed as a service on servers managed by us. The following presentation from WIW-2011 in Aarhus, Denmark present an overview of pros and cons of this approach. Read more.​​

WPPT Configuration Examples

Example no. 1 - Large TSO​

​This configuration of WPPT is used by a large TSO. The following facts characterizes the installation:

  • A large number of wind farms and stand-alone wind turbines.
  • Frequent changes in the number of wind turbines and the layout of wind farms.
  • Off-line production data with a resolution of 15 min. is available for more that 99% of the wind turbines in the area.
  • No on-line data enters the model.

Example no. 2 - Large Wind Farm Owner​

his configuration is used by a large wind farm owner in Denmark, and the installation has the following characteristics:

  • A reasonable (less than 20) number of wind farms
  • On-line power production data is available for a number of wind farms.
  • Off-line production data with a resolution of 15 min. is available for almost all wind turbines. These off-line data is released with a delay of 3-5 weeks.

Example no. 3 - Very Large TSO​

​This configuration of WPPT is used by a very large TSO. The facts of the installation are the following:

  • A large number of wind farms and stand-alone wind turbines.
  • Frequent changes in the wind turbine population.
  • Off-line production data is available for more than 99% of the wind turbines in the area.
  • On-line data for a large number of wind farms is available. The number of online wind farms increases quite frequently.​

​Contact information

​ENFOR A/S

Lyngsø Allé 3

DK-2970 Hørsholm

​​Phone: (+45) 45 350 350

E-mail: info@enfor.dk

VAT no.: 29421633

ENFOR A/S
Lyngsø Allé 3, 2970 Hørsholm
Tlf.: 45 350 350
info@enfor.dk