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Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization

机译：利用深度学习时间序列预测和极值优化的非线性学习集成进行风速预测

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摘要

As an essential issue in wind energy industry, wind speed forecasting plays a vital role in optimal scheduling and control of wind energy generation and conversion. In this paper, a novel method called EnsemLSTM is proposed by using nonlinear-learning ensemble of deep learning time series prediction based on LSTMs (Long Short Term Memory neural networks), SVRM (support vector regression machine) and EO (extremal optimization algorithm). First, in order to avert the drawback of weak generalization capability and robustness of a single deep learning approach when facing diversiform data, a cluster of LSTMs with diverse hidden layers and neurons are employed to explore and exploit the implicit information of wind speed time series. Then predictions of LSTMs are aggregated into a nonlinear-learning regression top-layer composed of SVRM and the EO is introduced to optimize the parameters of the top-layer. Lastly, the final ensemble prediction for wind speed is given by the fine-turning top-layer. The proposed EnsemLSTM is applied on two case studies data collected from a wind farm in Inner Mongolia, China, to perform ten-minute ahead utmost short term wind speed forecasting and one-hour ahead short term wind speed forecasting. Statistical tests of experimental results compared with other popular prediction models demonstrated the proposed EnsemLSTM can achieve a better forecasting performance.
机译：风速预测作为风能行业的重要问题，在风能发电和转换的最佳调度和控制中起着至关重要的作用。在本文中，通过使用基于LSTM（长期短期记忆神经网络），SVRM（支持向量回归机）和EO（极端优化算法）的深度学习时间序列预测的非线性学习集合，提出了一种称为EnsemLSTM的新方法。首先，为了避免单一深度学习方法在面对多样化数据时泛化能力和鲁棒性的缺点，我们采用了具有不同隐藏层和神经元的LSTM簇来探索和利用风速时间序列的隐式信息。然后将LSTM的预测汇总到由SVRM组成的非线性学习回归顶层中，并引入EO来优化顶层参数。最后，由微调的顶层给出风速的最终合奏预测。拟议的EnsemLSTM用于从中国内蒙古的一个风电场收集的两个案例研究数据，以进行最大提前短期风速预测10分钟和提前短期风速预测1小时。与其他流行的预测模型相比，实验结果的统计测试表明，提出的EnsemLSTM可以实现更好的预测性能。

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