A comparison of a few statistical models for making quantile wind power forecasts

By J. Bremnes. 2006

112 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/6bfc64d2610610d29cd2dff8cadd269572b56a58

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

In Environmental Engineering

Citations in the corpus

Probabilistic Forecast for Multiple Wind Farms Based on Regular Vine Copulas (2018, methodology): “Different quantile regression methods are investigated in [11], [12].”
From probabilistic forecasts to statistical scenarios of short-term wind power production (2009, background): “Probabilistic predictions can be either derived from meteorological ensembles [5], based on physical considerations [6], or finally produced from one of the numerous statistical methods that have appeared in the literature, see [4, 7, 8, 9, 10] among others.”
An analog ensemble for short-term probabilistic solar power forecast (2015, methodology): “In [34] QR is used to forecast errors of WPF but, as mentioned by the authors, this can lead to unrealistic quantiles (i.e., a lower quantile crossing a higher one).”
Very‐short‐term probabilistic forecasting of wind power with generalized logit–normal distributions (2012, background): “Density forecasts for these horizons are commonly based on statistical models for the ‘dressing’ of point predictions, conditional on weather conditions (Bremnes, 2006; Møller et al., 2008; Pinson and Kariniotakis, 2010).”
Wind power forecasting : state-of-the-art 2009. (2009, methodology): “Bremnes [277] presents a comparison of three statistical approaches: (i) local quantile regression; (ii) local Gaussian modeling; and (iii) the Nadaraya-Watson estimator [278].”
Reliability diagrams for non‐parametric density forecasts of continuous variables: Accounting for serial correlation (2010, methodology): “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).”