The State-Of-The-Art in Short-Term Prediction of Wind Power. A Literature Overview

By Gregor Giebel. 2003

621 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/45e83e5fac2bfa0bf999f3ec596302afae43cda4

How researchers see this paper

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

Citations in the corpus

Very‐short‐term probabilistic forecasting of wind power with generalized logit–normal distributions (2012, background): “Following the point of Gneiting (2011) among others, one should extract the optimal point forecasts from predictive densities based on the target evaluation score.”
Wind Energy: Forecasting Challenges for Its Operational Management (2013, background): “For an exhaustive review of the state of the art in that field, the reader is referred to Giebel et al. (2011), while a solid introduction to the physical concepts involved can be found in Lange and Focken (2006).”
An Advanced Statistical Method for Wind Power Forecasting (2007, methodology): “Furthermore, physical models must and statistical models may use numerical weather prediction (NWP) models [7]–[ 11 ].”
On the market impact of wind energy forecasts (2010, background): “In the case of wind power, production forecasting is a major and rapidly growing research field and has been the topic of countless papers (see e.g Giebel et al., 2003; Costa et al., 2008, for a state of the art review).”
Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine (2014, methodology): “As introduced in [1], statistical methods would outperform NWP-based methods for forecasting wind power with look-ahead times less than a few hours.”
Power Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms (2015, unknown): “first is to forecast wind power (Giebel et al. 2011; Monteiro et al. 2009) in two steps.”
Scenario Generation of Wind Power Based on Statistical Uncertainty and Variability (2013, methodology): “The time series methods, numerical weather prediction (NWP) and their combined approaches have become the major wind power forecast approaches [4], [5], most of which provide end-users with point forecasts for single turbines, wind farms, or for whole regions, from a few minutes up to a few days ahe”
On-line quantile regression in the RKHS (Reproducing Kernel Hilbert Space) for operational probabilistic forecasting of wind power (2016, background): “Finally, the decrease of the forecasting performance with 7 the prediction horizon is a classical result in wind power forecasting [30].”
Powering Up With Space-Time Wind Forecasting (2010, background): “Statistical models, especially those that incorporate expert knowledge of wind characteristics and geography, are unmatched in making short-term predictions ( Giebel, Brownsword, and Kariniotakis 2003 ).”
Spatio‐temporal analysis and modeling of short‐term wind power forecast errors (2011, background): “So far, state-ofthe-art prediction systems typically provide forecasts for a single wind turbine, for a wind farm, or over a region with significant installed wind power capacities [1, 2].”