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Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks

机译：基于分解的混合风速预测模型，使用深双向LSTM网络

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摘要

The goal of sustainable development can be attained by the efficient management of renewable energy resources. Wind energy is attracting attention worldwide due to its renewable and sustainable nature. Accurate wind speed prediction is essential for the stable functioning of wind turbines to generate wind power. However, the flexible and intermittent nature of wind speed makes accurate wind speed forecasting a challenging task. The proposed wind speed forecasting framework combines the features of various data decomposition techniques and Bidirectional Long Short Term Memory (BiDLSTM) networks. Presently, Data Decomposition models such as the Wavelet Transform are extensively employed for wind speed forecasting to improve the accuracy of the forecasting models. Hence, in this paper, various data decomposition techniques that can denoise the signal are investigated and applied to partition the input time series into several high and low-frequency signals. The data decomposition methods, namely, Wavelet Transform, Empirical Model Decomposition, Ensemble Empirical Mode Decomposition, and Empirical Wavelet Transform, have been applied to denoise the dataset. The low and high-frequency sub-series are forecasted separately using Bidirectional LSTM networks, and the forecasting outcomes of low and high-frequency signals are aggregated to get the final forecasting results. The empirical results establish that the proposed EWT- based hybrid model outperforms other decomposition-based models in accuracy and stability. The performance of the EWT-BiDLSTM model is further compared with Bidirectional LSTM networks with skip connections. The experimental results substantiate that the proposed decompositionbased hybrid deep BiDLSTM models with skip connections exhibit better prediction accuracy than other models.
机译：可持续发展的目标可以通过可再生能源资源的有效管理来实现。由于其可再生和可持续的性质，风能在全球范围内引起关注。精确的风速预测对于风力涡轮机产生风力的稳定功能至关重要。然而，风速的灵活性和间歇性质使得精确的风速预测是一个具有挑战性的任务。所提出的风速预测框架结合了各种数据分解技术和双向长期内记忆（BIDLSTM）网络的特征。目前，诸如小波变换的数据分解模型被广泛用于风速预测以提高预测模型的准确性。因此，在本文中，研究了可以欺骗信号的各种数据分解技术，并应用于将输入时间序列分配成几种高频信号。已经应用了数据分解方法，即小波变换，经验模型分解，集合经验模式分解和经验小波变换，以便代替数据集。使用双向LSTM网络单独预测低频和高频子系列，汇总低频和高频信号的预测结果以获得最终预测结果。经验结果确定所提出的基于EWT的混合模型在准确性和稳定性方面优于其他基于分解的模型。与具有跳过连接的双向LSTM网络相比，EWT-Bidlstm模型的性能进一步比较。实验结果证实，具有跳过连接的提出的分解基混合型深度Bidlstm模型比其他模型更好地预测精度。

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