On comparing three artificial neural networks for wind speed forecasting

By Gong Li, Jing Shi. 2010

717 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

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Citations in the corpus

Modeling of financial incentives for investments in energy storage systems that promote the large-scale integration of wind energy (2013, background): “Demand side management techniques [16] along with improved wind speed forecasting methods [17–19] is another combined tool that could encourage the consumer side to cope with the variability of wind energy production.”
A Model Combining Convolutional Neural Network and LightGBM Algorithm for Ultra-Short-Term Wind Power Forecasting (2019, methodology): “ARMA, ARIMA and autoregressive model [11], [12], and nonlinear models such as Fuzzy model [13], wavelet-based model [14] and ANN [15]–[19].”
Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform (2016, methodology): “For instance, data-driven models are widely considered to be the most competitive methods for higher temporal resolutions due to 30 the slightly changing wind speeds during these periods [9].”
Forecasting wind power – Modeling periodic and non-linear effects under conditional heteroscedasticity (2016, methodology): “For the ANN benchmark, we consider a single feed-forward neural network and stick close to the setting as used by Li and Shi (2010). As its inputs, we consider the lagged values of the past 8 hours of observations.”
A new high-dimensional time series approach for wind speed, wind direction and air pressure forecasting (2017, methodology): “We include a single feed-forward neural network which is comparble to Li and Shi (2010).”
Current status and future advances for wind speed and power forecasting (2014, methodology): “[51] Li G, Shi J.”
Fine tuning support vector machines for short-term wind speed forecasting (2011, methodology): “Besides, some new methods based on artificial intelligence techniques have also been developed and investigated such as artificial neural networks [10–13] and fuzzy logic [14,15].”
A review of combined approaches for prediction of short-term wind speed and power (2014, background): “Then, a comprehensive study was presented by investigating the performances of three different ANN types, namely, Adaptive Linear Element (ADALINE), Feed Forward Back-Propagation (FFBP) and Radial Basis Function (RBF) for 1-h-ahead wind speed predictions and as a result, the authors indicated that d”
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].”