A novel non-linear combination system for short-term wind speed forecast

Wind speed forecast is a non-linear and non-smooth problem. nonlinear and non-stationary are two kinds of mathematical problem, it is difficult to model with a single method, so that, a wavelet neural network model is set, the non-linear process of wind speed is forecast by neural networks and the non-stationary process of wind speed is decomposed into quasi-stationary at different frequency scales by multi-scale characteristics of wavelet transforms. wavelet combined with neural network model avoid the neural network model that can not handle non-stationary questions .while, the effect of indefinite inputs are removed by embedding dimension of phase space to determine neural networks inputs. The simulation results show that phase space reconstruction of wavelet neural network is more accuracy than the ordinary BP neural network. It could be well applied in wind speed forecasts.

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Short-Term Wind Speed Forecast Using Measurements From Multiple Turbines in A Wind Farm

Technometrics ◽

10.1080/00401706.2014.988291 ◽

2016 ◽

Vol 58 (1) ◽

pp. 138-147 ◽

Cited By ~ 16

Author(s):

Arash Pourhabib ◽

Jianhua Z. Huang ◽

Yu Ding

Keyword(s):

Wind Speed ◽

Wind Farm ◽

Short Term ◽

Wind Speed Forecast

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Short-Term Wind Speed Forecast Using Mathematical Morphology Decomposition and Support Vector Regression

2018 International Conference on Power System Technology (POWERCON) ◽

10.1109/powercon.2018.8601839 ◽

2018 ◽

Author(s):

Z. Y. Xue ◽

Z. M. Chen ◽

M. S. Li ◽

T. Y. Ji ◽

Q. H. Wu

Keyword(s):

Wind Speed ◽

Support Vector Regression ◽

Mathematical Morphology ◽

Support Vector ◽

Short Term ◽

Wind Speed Forecast

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Short-Term Wind Speed Forecasting by Using Chaotic Theory and SVM

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.300-301.842 ◽

2013 ◽

Vol 300-301 ◽

pp. 842-847 ◽

Cited By ~ 6

Author(s):

Cai Hong Zhu ◽

Ling Ling Li ◽

Jun Hao Li ◽

Jian Sen Gao

Keyword(s):

Phase Space ◽

Wind Speed ◽

Wind Farm ◽

Phase Space Reconstruction ◽

Percentage Error ◽

Support Vector ◽

Forecasting Model ◽

Particle Swarm Algorithm ◽

Short Term ◽

Wind Speed Forecast

The wind speed forecast is the basis of the wind power forecast. The wind speed has the characteristics of random non-smooth so obviously that its precise forecast is extremely difficult. Therefore, a forecasting method based on the theory of chaotic phase-space reconstruction and SVM was put forward in this paper and a forecasting model of Chaotic Support Vector Machine was built. In order to improve the precision and generalization ability, the key parameters in the phase space reconstruction and the key parameters of SVM were carried out joint optimization by using particle swarm algorithm in the paper. Then the optimal parameters were brought into the forecasting model to forecast short-term wind speed. The above method was applied to wind speed forecast of a wind farm in Inner Mongolia, China. In the experiments of computer simulation, the absolute percentage error of forecasting results was only 12.51%, which showed this method was effective for short-term wind speed forecast.

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