No abstract

Short-term wind power prediction is a primary requirement for efficient large-scale integration of wind generation in power systems and electricity markets. The choice of an appropriate prediction model among the numerous available models is not trivial, and has to be based on an objective evaluation of model performance.This paper proposes a standardized protocol for the evaluation of short-term windpower prediction systems. A number of reference prediction models are also described, and their use for performance comparison is analysed. The use of the protocol is demonstrated, using results from both on-shore and offshore wind farms. The work was developed in the frame of the Anemos project (EU R&D project) where the protocol has been used to evaluate more than 10 prediction systems. NOMENCLATURE P inst , W ind farm installed capacity (in kW or MW) k = 1, 2, . . . , k max , Prediction time-step (also called lead time or look-ahead time) k max , M aximum prediction horizon N,Number of data used for the model evaluation P(t), Measured power at time t (in kW or MW), which usually corresponds to the average power over the previous time period P (t + k|t), Power forecast for time t + k made at time origin t (in kW or MW) e(t + k|t), Error corresponding to time t + k for the prediction made at time origin t (in kW or MW) ε(t + k|t),

For operational planning it is important to provide information about the situation‐dependent uncertainty of a wind power forecast. Factors which influence the uncertainty of a wind power forecast include the predictability of the actual meteorological situation, the level of the predicted wind speed (due to the non‐linearity of the power curve) and the forecast horizon. With respect to the predictability of the actual meteorological situation a number of explanatory variables are considered, some inspired by the literature. The article contains an overview of related work within the field. An existing wind power forecasting system (Zephyr/WPPT) is considered and it is shown how analysis of the forecast error can be used to build a model of the quantiles of the forecast error. Only explanatory variables or indices which are predictable are considered, whereby the model obtained can be used for providing situation‐dependent information regarding the uncertainty. Finally, the article contains directions enabling the reader to replicate the methods and thereby extend other forecast systems with situation‐dependent information on uncertainty. Copyright © 2005 John Wiley & Sons, Ltd.

No abstract

 Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  You may not further distribute the material or use it for any profit-making activity or commercial gain  You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.