Short‐term wind speed forecasting using wavelet transformation and AdaBoosting neural networks in Yunnan wind farm

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an “optimal” weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

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An evolution-dependent multi-objective ensemble model of vanishing moment with adversarial auto-encoder for short-term wind speed forecasting in Xinjiang wind farm, China

Energy Conversion and Management ◽

10.1016/j.enconman.2019.111914 ◽

2019 ◽

Vol 198 ◽

pp. 111914 ◽

Cited By ~ 8

Author(s):

Zhu Duan ◽

Hui Liu

Keyword(s):

Wind Speed ◽

Wind Farm ◽

Ensemble Model ◽

Short Term ◽

Wind Speed Forecasting ◽

Multi Objective ◽

Vanishing Moment

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Short-Term Wind Speed Forecasting Using Decomposition-Based Neural Networks Combining Abnormal Detection Method

Abstract and Applied Analysis ◽

10.1155/2014/984268 ◽

2014 ◽

Vol 2014 ◽

pp. 1-21

Author(s):

Xuejun Chen ◽

Jing Zhao ◽

Wenchao Hu ◽

Yufeng Yang

Keyword(s):

Neural Networks ◽

Wind Speed ◽

Weighted Average ◽

Wind Farms ◽

Ensemble Empirical Mode Decomposition ◽

Short Term ◽

Wind Speed Forecasting ◽

Hybrid Approaches ◽

Mode Decomposition ◽

Nonlinear Autoregressive

As one of the most promising renewable resources in electricity generation, wind energy is acknowledged for its significant environmental contributions and economic competitiveness. Because wind fluctuates with strong variation, it is quite difficult to describe the characteristics of wind or to estimate the power output that will be injected into the grid. In particular, short-term wind speed forecasting, an essential support for the regulatory actions and short-term load dispatching planning during the operation of wind farms, is currently regarded as one of the most difficult problems to be solved. This paper contributes to short-term wind speed forecasting by developing two three-stage hybrid approaches; both are combinations of the five-three-Hanning (53H) weighted average smoothing method, ensemble empirical mode decomposition (EEMD) algorithm, and nonlinear autoregressive (NAR) neural networks. The chosen datasets are ten-minute wind speed observations, including twelve samples, and our simulation indicates that the proposed methods perform much better than the traditional ones when addressing short-term wind speed forecasting problems.

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Short-term wind speed forecasting in wind farm based on C-C and ELM

2017 3rd IEEE International Conference on Computer and Communications (ICCC) ◽

10.1109/compcomm.2017.8323049 ◽

2017 ◽

Author(s):

Dongshuai Kang ◽

Yingying Su ◽

Xinghua Liu ◽

Huabin Wang ◽

Cuiying Li ◽

...

Keyword(s):

Wind Speed ◽

Wind Farm ◽

Short Term ◽

Wind Speed Forecasting

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Ultra-Short-Term Wind Speed and Power Forecast Based on Dynamic Selective Neural Network Ensemble

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.568-570.868 ◽

2014 ◽

Vol 568-570 ◽

pp. 868-873

Author(s):

Yan Hua Liu ◽

Ze Dong

Keyword(s):

Neural Network ◽

Neural Networks ◽

Wind Speed ◽

Wind Power ◽

Wind Farm ◽

Stable Operation ◽

Neural Network Ensemble ◽

Short Term ◽

The Neural Networks ◽

Generalization Errors

With the scale of grid-connected wind farms increasing, accurate forecast of ultra-short-term wind speed and wind power is very important to the stable operation of power systems. This paper presents a dynamic selective neural network ensemble (DSNNE) forecast method, which makes use of K nearest neighbor algorithm to collect the generalization errors of certain different BP neural networks and RBF neural networks into a performance matrix and then the neural networks with low local generalization errors are dynamically selected and locally dynamic averaging is applied to the neural networks in order to conduct the final results of the ensemble. Then this method is applied to realize the wind speed and power ultra-short-term advance forecast, taking the wind speed and wind turbine power output from a wind farm in China as the original data. The research results show that DSNNE improves the generalization ability of the neural network system and the prediction accuracy of wind power and wind speed significantly. It proves the validity and effectiveness of the DSNNE with controlling the biggest mean relative error of 2 minutes ahead wind power and wind speed forecast as low as 25% and 16% respectively.

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