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TitleA post-forecast weighing algorithm to improve wind power forecasting capabilities
AuthorPijnenburg, PORCID logo; Cao, BORCID logo; Chang, L; Kilpatrick, R; Levy, T
SourceIET Renewable Power Generation 2022, 2022 p. 1-9, Open Access logo Open Access
Alt SeriesNatural Resources Canada, Contribution Series 20220451
Mediapaper; digital; on-line
File formatpdf
SubjectsNature and Environment; Science and Technology; energy resources; energy; Wind energy; Forecasting; Weather forecasts
Illustrationsschematic diagrams; tables; flow diagrams; plots; graphs
ProgramEnergy Innovation Program
Released2022 09 01
AbstractWind power generation has had a profound impact on both the green power and traditional power sectors. As a result, wind power forecasting plays an immense role in effectively predicting and providing wind power generated for effective power dispatching for system operators. However, wind power forecasting is a challenging topic with accuracy issues between the predicted power and actual power generation at the point of common coupling. Furthermore, due to the variation of wind, effective dispatching through the utilisation of wind power production forecasting becomes a challenge. This issue is further compounded by the vast amount of data required to train and verify of these forecasting algorithms. This paper presents a fast acting post forecast weighing algorithm designed to evaluate the forecasted power output of a previously developed wind power forecasting package. The developed method is designed to gauge and improve the estimated output forecaster's approach in order to observe performance changes in the algorithm while using minimal data without changing the internal workings of the evaluated forecasting algorithm.
Summary(Plain Language Summary, not published)
This paper presents a new post-forecast weighing algorithm to improve the accuracy of wind power forecasts. The new algorithm uses a wind power forecasting tool previously developed by the research team, along with wind farm telemetry data, to provide a more accurate wind power production estimate. The performance of the algorithm was evaluated using production data from a real commercial wind farm, and forecast accuracy was improved compared to using the existing forecasting tool on its own.

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