On the Uncertainty of Wind Power Predictions—Analysis of the Forecast Accuracy and Statistical Distribution of Errors

By Matthias Lange. 2005

378 citations recorded by the source corpus.

Subfields: Atmospheric Science, Electrical and Electronic Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/9c386523190d47070896b8f99861feddddedf388

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In Atmospheric Science

In Electrical and Electronic Engineering

In Environmental Engineering

Citations in the corpus

Wind Energy: Forecasting Challenges for Its Operational Management (2013, methodology): “Consequently, conditional densities of wind power generation should be seen as non-Gaussian, with their moments of order greater than 1 directly influenced by their mean (Lange, 2005; Bludszuweit, Domínguez-Navarro and Llombart, 2008).”
Powering Up With Space-Time Wind Forecasting (2010, background): “Predictions of wind speed are ultimately for the purpose of predicting power; thus, assessing the quality of wind speed predictions should link speed and power (Lange 2005; Lange and Focken 2005).”
A Versatile Probability Distribution Model for Wind Power Forecast Errors and Its Application in Economic Dispatch (2013, background): “, a distortion appears in the PDF curve [9] because the wind power output should be bounded within 0 to 1 p.”
Setting the Operating Reserve Using Probabilistic Wind Power Forecasts (2011, background): “However, wind power forecast error is well known to have a non-Gaussian distribution [14]–[16].”
Probabilistic Model of Payment Cost Minimization Considering Wind Power and Its Uncertainty (2013, methodology): “Based on previous works [18], [19], a normal distribution is applied to the wind speed forecast error in this paper.”
Very‐short‐term probabilistic forecasting of wind power with generalized logit–normal distributions (2012, background): “Several researchers, e.g. Lange (2005) and Pinson (2006), have observed that the standard deviation of the distributions of forecast errors was directly linked to its conditional expectation, whatever the time of the year, horizon and forecast model.”
Wind power forecasting : state-of-the-art 2009. (2009, methodology): “A method that converts wind prediction errors into power output uncertainty relying on the derivative of the power curve is proposed by Lange in [154] and [275].”
Reliability diagrams for non‐parametric density forecasts of continuous variables: Accounting for serial correlation (2010, methodology): “For an overview of those methods, see e.g. Lahiri (2003).”