A comparison of various forecasting techniques applied to mean hourly wind speed time series

By A. Sfetsos. 2000

379 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/ff4c17c713de7c9aa84c963569e2a6bad0d46f68

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In Artificial Intelligence

In Electrical and Electronic Engineering

In Management Science and Operations Research

Citations in the corpus

A review of combined approaches for prediction of short-term wind speed and power (2014, methodology): “In one of the earliest review articles on comparison of several well-known forecasting approaches, especially based on ANNs, was reported by Sfetsos [26].”
Wind power forecasting : state-of-the-art 2009. (2009, methodology): “D three dimensional AC Alternating Current ACE Area Control Error AEC Adaptive Exponential Combination AESO Alberta Electric System Operator AGC Automatic Generation Control ANFIS Adaptive Neural Fuzzy Inference System ANN Artificial Neural Network AR Autoregressive ARIMA Autoregressive Integrated M”
On comparing three artificial neural networks for wind speed forecasting (2010, background): “In literature, some publications compared ANN models with the persistence model, and it was found that ANN models generally outperform the persistence model, but sometimes the advantage is not very significant [5,7,9].”
A review on the young history of the wind power short-term prediction (2008, result): “(i) the non-linear models overcame the linear models; (ii) all the non-linear models presented comparable RMSE; (iii) the neural logic network was slightly superior to the others [31].”
Time series analysis of wind speed with time‐varying turbulence (2006, neutral): “A number of researchers have modeled wind speed using time series analysis in the context of single equation models; however, these studies generally assume a constant variance (e.g. Huang and Chalabi, 1995; Sfetsos, 2000, 2002; More and Deo, 2003; Poggi et al., 2003; and Hussain et al., 2004).”
Adaptive neuro-fuzzy inference system for prediction of water level in reservoir (2006, background): “With the ability to combine the verbal power of a fuzzy system with the numeric power of a neural system adaptive network, ANFIS has been shown to be powerful in modeling numerous processes, such as motor fault detection and diagnosis [1], power systems dynamic load [12,28], wind speed [33], forecas”
Short‐Term Wind Speed Forecasting for Power System Operations (2012, methodology): “Still in terms of forecasting accuracy, Sfetsos (2000) compared some of these techniques and ARIMA models.”
Short-Term Wind Speed or Power Forecasting With Heteroscedastic Support Vector Regression (2016, background): “Wind speed or wind power forecasting is becoming increasingly important and wind speed prediction is crucial for the control, scheduling, maintenance, and resource planning of wind energy conversion systems [3].”