Journal of Solar Energy Engineering · 2005 · 350 citations · 11 references
Natural SciencesEarth SciencesMeteorologyForecastingNumerical Weather PredictionUncertainty QuantificationForecast Error AnalysisForecast UncertaintyForecasting UncertaintyProbabilistic ForecastingWind Energy SystemsWind ForecastingForecast Uncertainty AssessmentNonlinearityOnshore Wind PowerPower Curve ModelingWind Forecast Uncertainty QuantificationWind Forecasting Error AnalysisWind Power ForecastingWind Speed Prediction
The study investigates wind power prediction uncertainty, emphasizing the nonlinear power curve’s impact. The authors assess accuracy using numerical predictions and measurements from six onshore wind farms, showing that the power curve inflates relative forecast error by 1.8–2.6×, and decompose RMS error to separate on‑site and NWP‑related sources. The relative forecast error increases by 1.8–2.6× due to the power curve, which can be treated as an effective nonlinearity factor; wind speed prediction error is Gaussian, whereas power prediction error is asymmetric, explainable and modelable via the power curve and conditional probability density functions, enabling estimation without measurement data.
In this work the uncertainty of wind power predictions is investigated with a special focus on the important role of the nonlinear power curve. Based on numerical predictions and measured data from six onshore wind farms the overall prediction accuracy is assessed and it is shown that due to the power curve the relative forecast error increases by a factor of 1.8–2.6 compared to the wind speed forecast. This factor can be considered as an effective nonlinearity factor. A decomposition of the commonly known root mean square error is beneficially used to distinguish different error sources related to either on-site conditions or global properties of the numerical weather prediction system. The statistical distribution of the wind speed prediction error is found to be Gaussian in contrast to the the one of power prediction error. Using the power curve and conditional probability density functions of the wind speed the unsymmetric distribution of the power prediction error can be explained and modeled such that it can be estimated even if no measurement data is available.
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Ulrich Focken, Matthias Lange, Kai Mönnich et al. · Journal of Wind Engineering and Industrial Aerodynamics · 2002 · 281 citations