Day-ahead wind speed forecasting using f-ARIMA models
By Rajesh G. Kavasseri, Krithika Seetharaman. 2009
941 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/3342d395fe6db3a7c830b1805144ff7cd8fd6d24
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Victor M. F. Mendes
- Qinghua Hu
- João P. S. Catalão
- Jing Shi
- Shiguang Zhang
- Jianzhou Wang
- Gong Li
- Man Yu
In Electrical and Electronic Engineering
- Daniel Ambach
- Florian Ziel
- Qinghua Hu
- Carsten Croonenbroeck
- Shiguang Zhang
- Man Yu
- Zongxia Xie
- Jing Shi
Citations in the corpus
- Forecasting wind power – Modeling periodic and non-linear effects under conditional heteroscedasticity (2016, background): “5). However, the class of statistical approaches also incorporates autoregressive (AR), autoregressive moving average (ARMA) and autoregressive fractionally integrated moving average (ARFIMA) models. Kavasseri and Seetharaman (2009) discuss these models in details. The literature on contributed mode”
- Wind power forecasting : state-of-the-art 2009. (2009, methodology): “[100] presented the fractional-ARIMA (f-ARIMA) model to forecast the wind speed day-ahead and two-days-ahead.”
- Short-Term Wind Speed or Power Forecasting With Heteroscedastic Support Vector Regression (2016, methodology): “It could be modeled by various methods, including the persistence method, autoregressive models [13], [14], artificial neural networks (ANNs) [15], SVR machines [16]–[18], and the hybrid method [19].”
- The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling (2020, methodology): “The implementation of ARIMA model for modeling wind energy prediction, various modified versions as well as its hybrid models are successfully reported in reducing prediction error [18,19].”
- On comparing three artificial neural networks for wind speed forecasting (2010, methodology): “Statistical methods, such as autoregressive integrated moving average (ARIMA) models, make forecasts by finding the relationship of the observed wind speed time series [13,14].”
- Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model (2012, background): “Under the short-term scales, accurate wind energy forecasts are critical to minimize scheduling errors which impact grid reliability and market based ancillary service costs [3].”