Short-term wind speed forecasting using STLSSVM hybrid model

Deyu Yuan, Zheng Qian, Bo Jing, Yan Pei
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引用次数: 3

Abstract

With the rapid growth of wind power, wind speed forecasting becomes more and more significant to ensure stable and efficient operations of wind power system. This paper proposes an improved hybrid methodology for short-term wind speed forecasting. After data preprocessing, MI algorithm is used to select proper wind speed features, then Ensemble Empirical Mode Decomposition (EEMD) is utilized to decompose the original wind speed series in order to make the chaotic series more stable. A novel model named ST-LSSVM is proposed to forecast the decomposed sub-series, which combines the Least Squares Support Vector Machine (LSSVM) and State Transition method (ST). In order to further enhance the model performance, Particle Swarm Optimization (PSO) is utilized to fine-tune the parameter values of the ST-LSSVM. Finally, real world wind speed data are used to estimate the proposed hybrid forecasting model. The results demonstrate that proposed ST-LSSVM hybrid model has the best prediction accuracy in one to six step’s forecasting, compared with Persistence, Autoregressive Integrated Moving Average (ARIMA), Back-Propagation Neutral Network (BPNN) and Least Squares Support Vector Machine (LSSVM) models.
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基于STLSSVM混合模型的短期风速预报
随着风电的快速增长,风速预测对于保证风电系统的稳定高效运行显得越来越重要。本文提出了一种改进的混合方法用于短期风速预报。在对数据进行预处理后,利用MI算法选择合适的风速特征,然后利用集成经验模态分解(Ensemble Empirical Mode Decomposition, EEMD)对原始风速序列进行分解,使混沌序列更加稳定。将最小二乘支持向量机(LSSVM)和状态转移方法(ST)相结合,提出了一种新的预测分解子序列的ST-LSSVM模型。为了进一步提高模型的性能,利用粒子群算法对ST-LSSVM的参数值进行微调。最后,利用实际风速数据对混合预测模型进行了估计。结果表明,与持久模型、自回归综合移动平均(ARIMA)模型、反向传播神经网络(BPNN)模型和最小二乘支持向量机(LSSVM)模型相比,ST-LSSVM混合模型在1 ~ 6步预测中具有最好的预测精度。
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