Probabilistic wind power forecasts using local quantile regression

By J. Bremnes. 2004

336 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/93d5943890890b51bb653c067487544f5e1129ae

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In Electrical and Electronic Engineering

In Statistics and Probability

Citations in the corpus

A survey on concept drift adaptation (2014, background): “This option rely in assumptions of stationarity of the wind electric power model, which must be strongly questioned [Bremnes 2004; Bessa et al. 2009].”
Trading Wind Generation From Short-Term Probabilistic Forecasts of Wind Power (2007, neutral): “Note that this assumption is implicit in the developments carried out in [8] and [9], while it is explicitly formulated in [6].”
Current status and future advances for wind speed and power forecasting (2014, methodology): “[108] Bremnes JB.”
Statistical Downscaling of Extreme Precipitation Events Using Censored Quantile Regression (2007, methodology): “We will present an alternative approach to Bremnes (2004a) where precipitation is represented by a socalled censored variable.”
Wind Energy: Forecasting Challenges for Its Operational Management (2013, methodology): “Maybe the most welldocumented and widely applied methods are the simple approach of Pinson and Kariniotakis (2010) consisting in dressing the available point forecasts with predictive densities of forecast errors made in similar conditions, as well as the local quantile regression of Bremnes (2004) ”
Pool Strategy of a Price-Maker Wind Power Producer (2013, background): “Considering the day-ahead and the balancing market stages only, it can be shown that the optimal day-ahead bid for a price-taker wind power producer is a certain quantile of the forecast wind power distribution, which is a function of the market prices, see [2], as well as [3] and [4] for the case w”
Wind Speed Interval Prediction Based on Lorenz Disturbance Distribution (2020, background): “regression [27], [28], the sparse Bayesian learning [29], [30] and so on.”
Wind power forecasting : state-of-the-art 2009. (2009, methodology): “113 If the probabilistic model is a quantile regression, Bremnes [264] suggests the following approach for the occasions when NWP ensembles are used as inputs:”
Quantile regression neural networks: Implementation in R and application to precipitation downscaling (2011, background): “…in the environmental sciences to climate trend detection (Koenker and Schorfheide, 1994), ozone pollution modelling (Baur et al., 2004), wind power forecasting (Bremnes, 2004b), ecological modelling (Cade and Noon, 2003), and precipitation downscaling (Bremnes, 2004a; Friederichs and Hense, 2007).”
Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine (2014, methodology): “The quantile regression can be used to estimate different wind power forecasting quantiles [5], [6].”