Abstract
Abstract. Deterministic wind power forecasts enclose an inherent uncertainty due to several sources of error. In order to counterbalance this deficiency, an analysis of the error characteristics and construction of probabilistic forecasts with associated confidence levels is necessary for the quantification of the corresponding uncertainty. This work proposes a probabilistic forecasting method using an atmospheric model, optimization techniques for addressing the temporal error dependencies and Kalman filtering for eliminating systematic errors and enhancing the symmetry-normality of the shaped error distributions. The method is applied in case studies, using real time data from four wind farms in Greece. The performance is compared against a reference method as well as other common methods showing an improvement in the predictive reliability.
Highlights
During recent years, a substantial amount of energy stems from renewable applications, including wind farms of significant spatial extent
Deterministic wind power predictions are always susceptible to various sources of error
The construction of forecasting intervals with associated confidence levels allows the quantification of the inherent uncertainty and the provision of qualitative information on the proper utilization and decision-making issues of the generated energy yield
Summary
A substantial amount of energy stems from renewable applications, including wind farms of significant spatial extent. A way to counterbalance this shortcoming lies in the provision of a range of values, within which the observation is expected to occur This involves confidence intervals (CI) and quantification of the corresponding uncertainty for the forthcoming examined value and is defined as probabilistic forecast. This approach addresses specific drawbacks, such as the error of deterministic forecasts, rapid alterations in observations, inadequate system response and extreme values. The development of the probabilistic forecasting methodology is based on key features of the deterministic prediction error. The proposed methodology is applied to a number of wind power farms and the efficiency is examined through statistical indexes of performance for probabilistic forecasting and comparison against other common methods
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