scispace - formally typeset
Journal ArticleDOI

Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching Space-Time (RST) Method

TLDR
In this paper, the authors proposed a regime switching space-time (RST) model to forecast wind power and wind speed at wind farms in the U.S. Pacific Northwest.
Abstract
With the global proliferation of wind power, the need for accurate short-term forecasts of wind resources at wind energy sites is becoming paramount. Regime-switching space–time (RST) models merge meteorological and statistical expertise to obtain accurate and calibrated, fully probabilistic forecasts of wind speed and wind power. The model formulation is parsimonious, yet takes into account all of the salient features of wind speed: alternating atmospheric regimes, temporal and spatial correlation, diurnal and seasonal nonstationarity, conditional heteroscedasticity, and non-Gaussianity. The RST method identifies forecast regimes at a wind energy site and fits a conditional predictive model for each regime. Geographically dispersed meteorological observations in the vicinity of the wind farm are used as off-site predictors. The RST technique was applied to 2-hour-ahead forecasts of hourly average wind speed near the Stateline wind energy center in the U. S. Pacific Northwest. The RST point forecasts and ...

read more

Citations
More filters
Journal ArticleDOI

Probabilistic forecasts, calibration and sharpness

TL;DR: In this paper, a diagnostic approach to the evaluation of predictive performance that is based on the paradigm of maximizing the sharpness of the predictive distributions subject to calibration is proposed, which is illustrated by an assessment and ranking of probabilistic forecasts of wind speed at the Stateline wind energy centre in the US Pacific Northwest.
Journal ArticleDOI

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

TL;DR: In this paper, the authors present 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.
Journal ArticleDOI

Review on probabilistic forecasting of wind power generation

TL;DR: A review of state-of-the-art methods and new developments in wind power probabilistic forecasting is presented in this paper, where three different representations of wind power uncertainty are briefly introduced.
Journal ArticleDOI

Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules

TL;DR: In this article, the authors proposed a method for comparing density forecasts that is based on weighted versions of the continuous ranked probability score, which emphasizes regions of interest, such as the tails or the center of a variable's range, while retaining propriety, as opposed to a recently developed weighted likelihood ratio test.

Geostatistical Space-Time Models, Stationarity, Separability, and Full Symmetry

TL;DR: In this paper, the authors review recent advances in the literature on space-time covariance functions in light of the aforementioned notions, which are illustrated using wind data from Ireland, and suggest that the use of more complex and more realistic covariance models results in improved predictive performance.
References
More filters
Book

Time series analysis, forecasting and control

TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
Journal ArticleDOI

Generalized autoregressive conditional heteroskedasticity

TL;DR: In this paper, a natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in 1982 to allow for past conditional variances in the current conditional variance equation is proposed.
Journal ArticleDOI

Time Series Analysis Forecasting and Control

TL;DR: This revision of a classic, seminal, and authoritative book explores the building of stochastic models for time series and their use in important areas of application —forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control.
Related Papers (5)