Dynamic Neural Network Based Very Short-Term Wind Speed Forecasting

In this paper, the nonlinear autoregressive model with exogenous inputs (NARX) is proposed for wind speed forecast. Forecasting wind speed is a challenging task in wind energy research domain which influences the dynamic control of wind turbine and for system scheduling. The aim of this study is to...

Full description

Saved in:
Bibliographic Details
Published in:Wind engineering 2014-04, Vol.38 (2), p.121-128
Main Authors: N, Ramesh Babu, P, Arulmozhivarman
Format: Article
Language:English
Subjects:
Architectural models
Artificial neural networks
Back propagation
Commercial production
Dynamical systems
Forecasting
Forecasting models
Mathematical models
Neural networks
Neurons
Nonlinear dynamics
Renewable energy
Time series forecasting
Weather forecasting
Wind power
Wind speed
Wind velocity
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, the nonlinear autoregressive model with exogenous inputs (NARX) is proposed for wind speed forecast. Forecasting wind speed is a challenging task in wind energy research domain which influences the dynamic control of wind turbine and for system scheduling. The aim of this study is to obtain suitable forecast model for wind speed with time series input variables such as wind direction, humidity, pressure and time. The meteorological data observed with 15 minute time intervals is used for the model and the performance is evaluated and compared with the back propagation neural network (BPNN). The result shows that the proposed model outperforms the BPNN based on the metrics used.
ISSN:0309-524X
2048-402X
DOI:10.1260/0309-524X.38.2.121