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

Haijian Shao; Haikun Wei; Xing Deng; Song Xing

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

机译：基于小波变换和AdaBoosting神经网络的风电场短期风速预测

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Wind speed presents a potential seasonal pattern revealed by the self-similarity in wavelet periodogram with various scales. The corresponding seasonal pattern will promote the improvement of the short-term wind speed forecasting accuracy. In this study, a novel method for short-term wind speed forecasting using wavelet transformation (WT) and AdaBoost technique is proposed to analyse the wind speeds distribution features and promote the model configuration. Power spectrum and seasonal pattern analysis using the WT are presented to investigate the wind speeds feature distribution based on the scalogram percentage of energy distribution in different seasons. This procedure contributes to perfecting the investigation of wind speed seasonal pattern characteristics over time and promotes the sample division by computing the statistics measurement based on the estimated frequencies interval. The model order estimation based on the information criteria is processed to reflect the systems dynamical sustainability between the current outputs and historical data. Finally, the experiments based on the real data from Yunnan wind farm are given to verify the effectiveness of the proposed approach.

机译：风速呈现出一种潜在的季节性模式，该趋势通过小尺度周期图中具有不同比例的自相似性揭示。相应的季节模式将促进短期风速预报准确性的提高。在这项研究中，提出了一种使用小波变换（WT）和AdaBoost技术进行短期风速预测的新方法，以分析风速分布特征并促进模型配置。提出了使用WT进行功率谱和季节模式分析的方法，以基于不同季节能量分布的比例图百分比来研究风速特征分布。此过程有助于完善随时间变化的风速季节性特征研究，并通过基于估计的频率间隔计算统计量来促进样本划分。处理基于信息标准的模型顺序估计，以反映当前输出和历史数据之间的系统动态可持续性。最后，基于云南风电场的真实数据进行了实验，以验证该方法的有效性。

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来源
《Renewable Power Generation, IET》 |2017年第4期|374-381|共8页
作者
Haijian Shao; Haikun Wei; Xing Deng; Song Xing;
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作者单位

Jiangsu University of Science and Technology, People's Republic of China;

Southeast University, People's Republic of China;

Southeast University, People's Republic of China;

California State University, USA;

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原文格式 PDF
正文语种 eng
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关键词
wind power plants; learning (artificial intelligence); neural nets; wavelet transforms; power engineering computing;

机译：风力发电厂;学习（人工智能）;神经网络;小波变换;电力工程计算;

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5. Short-term forecasting of wind speed and wind power based on BP and Adaboost_BP. [D] . Mi, Sibei. 2014

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6. Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting [O] . Zhongxian Men, Eugene Yee, Fue-Sang Lien, 2014

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7. Short-Term Wind Power Forecasting using Wavelet-based Hybrid Recurrent Dynamic Neural Networks [O] . Pavan Kumar Singh 2019

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

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