Forecasting wind with neural networks

By A. More, M. C. Deo. 2003

305 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Environmental Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/11a070e4893374b0f1c27ca6f12014a7159db808

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In Electrical and Electronic Engineering

In Environmental Engineering

In Management Science and Operations Research

Citations in the corpus

Forecasting production of fossil fuel sources in Turkey using a comparative regression and ARIMA model (2006, methodology): “On the other hand, the ARIMA model is most frequently used in forecasting of wind predictions (More and Deo, 2003), solid waste management (Navarro-Esbri et al.”
Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model (2012, background): “Many alternative training processes are available, out of which the present study adopts one popular scheme, namely back-propagation (BP) [25] which is presented in [26].”
On comparing three artificial neural networks for wind speed forecasting (2010, methodology): “Recently, some new methods based on artificial intelligence techniques have been developed, including the Artificial Neural Network (ANN) of Multi-Layer Perceptrons (MLP) [15,16], Radial Basis Function [17] and Recurrent Neural Networks [18,19], and Fuzzy Logic [20,21].”
Time series analysis of wind speed with time‐varying turbulence (2006, unknown): “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).”
Multiagent Genetic Algorithm: An Online Probabilistic View on Economic Dispatch of Energy Hubs Constrained by Wind Availability (2014, methodology): “Among the several introduced methods in prior works [15], [16], employing the probability distribution functions (pdfs) is commonly adopted to represent the wind speed variations in power system studies [17]–[19].”
Long-term wind speed and power forecasting using local recurrent neural network models (2006, methodology): “Furthermore, similar models have been applied for daily, weekly, or even monthly time series [5].”