Forecast of hourly average wind speed with ARMA models in Navarre (Spain)

By J. Torres, A. García, M. D. Blas, A. Francisco. 2005

679 citations recorded by the source corpus.

Subfields: Atmospheric Science, Electrical and Electronic Engineering, Environmental Engineering

How researchers see this paper

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

In Atmospheric Science

1. Shmuel S. Oren
2. Anthony Papavasiliou

In Electrical and Electronic Engineering

1. Jing Shi
2. Gong Li
3. Qinghua Hu
4. Shiguang Zhang
5. Man Yu
6. Zongxia Xie
7. Junyi Zhou
8. Fuad Un-Noor

In Environmental Engineering

1. Jing Shi
2. Gong Li
3. Junyi Zhou
4. Shmuel S. Oren
5. Anthony Papavasiliou
6. Mujeeb Ur Rehman
7. Pierre Pinson

Citations in the corpus

• A review of combined approaches for prediction of short-term wind speed and power (2014, neutral): “[13].”
• Multiarea Stochastic Unit Commitment for High Wind Penetration in a Transmission Constrained Network (2013, methodology): “Early work on the time series modeling of wind power production in order to address short-term temporal correlation and the nonlinear relationship between wind speed and wind power was performed by Brown et al. (1984) and later by Torres et al. (2005).”
• Fine tuning support vector machines for short-term wind speed forecasting (2011, methodology): “Statistical methods [8,9] make forecasting based on the observed wind speed time series.”
• Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review (2019, methodology): “Previously, several prediction models such as fuzzy modeling [145], auto regressive moving average [146], artificial neural network [147], [148], K-nearest neighbor classification [149], computational fluid dynamics (CFD) pre-calculated flow fields [150], extreme learning machine (ELM) [151]–[153], ”
• A review on the young history of the wind power short-term prediction (2008, methodology): “Adjusting one model for each one of the 12 months of the year, they achieved significant improvements over persistence on the RMSE basis, observing that the RMSE presented a certain dependence on the wind speed values [78].”
• 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].”
• A Model Combining Convolutional Neural Network and LightGBM Algorithm for Ultra-Short-Term Wind Power Forecasting (2019, methodology): “…methods, commonly used single model prediction schemes include persistence methods [7], support vector machines [8]–[10], the linear model including ARMA, ARIMA and autoregressive model [11], [12], and nonlinear models such as Fuzzy model [13], wavelet-based model [14] and ANN [15]–[19].”