Short‐Term Wind Speed Forecasting for Power System Operations

By Xinxin Zhu, Marc G. Genton. 2012

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

Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting (2017, neutral): “For a more complete survey of wind speed forecasting methods see (Zhu & Genton, 2012) and (Tascikaraoglu & Uzunoglu, 2014), among others.”
Short-Term Spatio-Temporal Wind Power Forecast in Robust Look-ahead Power System Dispatch (2014, background): “For wind speed forecasting problems, more realistic metrics that have penalization on underestimates and forecasts for small true values are desired for model evaluation [18] instead of RMSE and mean absolute errors (MAE).”
Low-dimensional models in spatio-temporal wind speed forecasting (2015, unknown): “For a more complete survey of wind speed forecasting methods see [11] and [12] among others.”
Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform (2016, background): “This approach is more effective as, for example, wind turbines with different ratings in a wind farm might experience the same wind speed but have different wind power [8].”
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).”
A new high-dimensional time series approach for wind speed, wind direction and air pressure forecasting (2017, background): “In the recent past, miscellaneous statistical and hybrid structure approaches for wind speed prediction have been proposed, see for instance Zhu and Genton (2012), Guo et al. (2014) and Ambach and Schmid (2015).”
The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling (2020, background): “This encourages the use of the wind as a free, clean and sustainable source of energy across the world [3,4].”
Wind Energy: Forecasting Challenges for Its Operational Management (2013, methodology): “A more extensive review of alternatives statistical approaches to point prediction of wind speed and power can be found in Zhu and Genton (2012).”