Powering Up With Space-Time Wind Forecasting

By Amanda S. Hering, Marc G. Genton. 2010

192 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Environmental Engineering, Global and Planetary Change

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Citations in the corpus

Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting (2017, methodology): “This method was later improved by Hering and Genton (Hering & Genton, 2010) who incorporated wind direction in the forecasting process by introducing Trigonometric Direction Diurnal (TDD) model.”
Wind Energy: Forecasting Challenges for Its Operational Management (2013, background): “surements (Pinson and Madsen, 2012). Incorporatingoﬀ-site information in regime-switching models of the form of (4.4) was proposed by Gneiting et al. (2006) and subsequently in a more general form by Hering and Genton (2010), when focusing on a data set for the Columbia Basin of eastern Washington a”
Making and Evaluating Point Forecasts (2009, background): “Applications include those of Gneiting et al. (2008) and Hering and Genton (2010) to predictions of wind vectors, or that of Laurent, Rombouts, and Violante (2009) to forecasts of multivariate volatility, to name but a few.”
Short‐Term Wind Speed Forecasting for Power System Operations (2012, methodology): “Wind speeds are non-negative valued and usually right skewed due to the low probability of high values; see Figure 4 for illustration based on data in Hering & Genton (2010).”
Low-dimensional models in spatio-temporal wind speed forecasting (2015, unknown): “This method was later improved by Hering and Genton [14] who incorporated wind direction in the forecasting process by introducing Trigonometric Direction Diurnal (TDD) model.”
A Valid Matérn Class of Cross-Covariance Functions for Multivariate Random Fields With Any Number of Components (2012, background): “Accurate forecasts of wind speed are of crucial importance in many applications, such as harvesting electricity from wind energy (Genton and Hering 2007; Hering and Genton 2010) and severe weather warnings for the general public.”
Ensemble Model Output Statistics for Wind Vectors (2012, methodology): “normal or skew-T densities, similar to the wind vector time series methods proposed by Hering and Genton (2010). However, any such approach would be consid-”
Short-Term Spatio-Temporal Wind Power Forecast in Robust Look-ahead Power System Dispatch (2014, methodology): “Predictors in (2) are selected with the Bayesian Information Criteria (see [33]).”
Power Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms (2015, unknown): “…wind forecasting is beyond the focus of this article; it is a subfield of its own; for details on wind forecasting, interested readers should refer to Gneiting et al. (2006), Monteiro et al. (2009), Hering and Genton (2010), Zhu and Genton (2012), Pinson (2013), and Zhu, Bowman, and Genton (2014).”
The affinely invariant distance correlation (2012, methodology): “For our purposes, we follow Hering and Genton [8] in studying the time series at the original 10-minute resolution, and we restrict our analysis to the longest continuous record, the 75-day interval from August 14, 2002 to October 28, 2002.”
Short-term spatio-temporal prediction of wind speed and direction (2014, methodology): “Autoregressive models to predict the wind vector have been developed; two bivariate approaches are suggested in [14] while others rely on additional NWP results as inputs such as [29].”
Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform (2016, methodology): “This approach is enhanced by Hering and Genton [21] in the Trigonometric Direction Diurnal (TDD) model, incorporating wind direction85 in the model.”
Spatio‐temporal analysis and modeling of short‐term wind power forecast errors (2011, background): “Genton [ 7 ] for instance, potentially requiring significant expertise for identification of their struc-”