Time-adaptive quantile regression

Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik

doi:10.1016/j.csda.2007.06.027

Cited by 99 publications

(90 citation statements)

References 9 publications

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“…The very tails of predictive distributions are parameterized with exponential tails, thus reflecting the unlikeliness of extreme events. The quality of probabilistic forecasts produced with this method and for this particular case-study is evaluated and discussed in [18], while additional evaluation results may be found in [10]. Adaptive quantile regression has been shown to have an acceptable level of reliability, and a superior overall skill when compared with other nonparametric probabilistic forecasting methods of the state of the art.…”

Section: Description Of the Case-studymentioning

confidence: 99%

“…The dataset consists of 16900 point prediction series. Nonparametric probabilistic forecasts are produced with adaptive quantile regression [10]. Predictive distributions are given by 19 quantile forecasts whose nominal proportions range from 0.05 to 0.95 by 0.05 increments.…”

Section: Description Of the Case-studymentioning

confidence: 99%

“…In order to accommodate the nonstationary characteristics of the wind power forecast uncertainty (cf. discussion in [10]), this covariance matrix is tracked with a recursive estimation method. Results from the application of the described method are given subsequently.…”

Section: Introductionmentioning

confidence: 99%

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From probabilistic forecasts to statistical scenarios of short‐term wind power production

et al. 2008

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Short-term (up to 2-3 days ahead) probabilistic forecasts of wind power provide forecast users with a highly valuable information on the uncertainty of expected wind generation. Whatever the type of these probabilistic forecasts, they are produced on a per horizon basis, and hence do not inform on the development of the forecast uncertainty through forecast series. However, this additional information may be paramount for a large class of time-dependent and multi-stage decision-making problems e.g. optimal operation of combined wind-storage systems or multiple-market trading with different gate closures. This issue is addressed here by describing a method that permits the generation of statistical scenarios of short-term wind generation that accounts for both the interdependence structure of prediction errors and the predictive distributions of wind power production. The method is based on the conversion of series of prediction errors to a multivariate Gaussian random variable, the interdependence structure of which can then be summarized by a unique covariance matrix. Such matrix is recursively estimated in order to accommodate long-term variations in the prediction error characteristics. The quality and interest of the methodology are demonstrated with an application to the test case of a multi-MW wind farm over a period of more than two years.

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Section: Description Of the Case-studymentioning

confidence: 99%

Section: Description Of the Case-studymentioning

confidence: 99%

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From probabilistic forecasts to statistical scenarios of short‐term wind power production

et al. 2008

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“…here the NWPs, describes the distribution well over the entire range of global radiation. Finally, it is mentioned that for on-line operation the method can be implemented computationally very efficient using time-adaptive quantile regression (Møller et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

A non-parametric method for correction of global radiation observations

et al. 2013

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“…As wind-power generation is a nonlinear and bounded process, predictive densities may be highly skewed and with heavy tails (Lange, 2005), and hence be difficult to model accurately with known parametric families of density functions (see the discussion by Pinson, 2006). This has motivated the development of a large number of non-parametric methods for wind-power density forecasting, based on statistical methods and/or ensemble forecasts (see Bremnes, 2006;Møller, et al, 2008;Nielsen, et al, 2006;Pinson and Madsen, 2009a, among others).…”

Section: Application To the Reliability Assessment Of Density Forecasmentioning

confidence: 99%

Reliability diagrams for non‐parametric density forecasts of continuous variables: Accounting for serial correlation

Pinson

McSharry

Madsen

2010

Quart J Royal Meteoro Soc

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Reliability is seen as a primary requirement when verifying probabilistic forecasts, since a lack of reliability would introduce a systematic bias in subsequent decisionmaking. Reliability diagrams comprise popular and practical diagnostic tools for the reliability evaluation of density forecasts of continuous variables. Such diagrams relate to the assessment of the unconditional calibration of probabilistic forecasts. A reason for their appeal is that deviations from perfect reliability can be visually assessed based on deviations from the diagonal. Deviations from the diagonal may, however, be caused by both sampling effects and serial correlation in the forecastverification pairs. We build on a recent proposal, consisting of associating reliability diagrams with consistency bars that would reflect the deviations from the diagonal that are potentially observable even if density forecasts are perfectly reliable. Our consistency bars, however, reflect potential deviations originating from the combined effects of limited counting statistics and serial correlation in the forecastverification pairs. They are generated based on an original surrogate consistency resampling method. Its ability to provide consistency bars with a significantly better coverage against the independent and identically distributed (i.i.d.) resampling alternative is shown from simulations. Finally, a practical example of the reliability assessment of non-parametric density forecasts of short-term wind-power generation is given.

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Time-adaptive quantile regression

Cited by 99 publications

References 9 publications

From probabilistic forecasts to statistical scenarios of short‐term wind power production

From probabilistic forecasts to statistical scenarios of short‐term wind power production

A non-parametric method for correction of global radiation observations

Reliability diagrams for non‐parametric density forecasts of continuous variables: Accounting for serial correlation

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