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Gaussian mixture model coupled recurrent neural networks for wind speed interval forecast

机译：高斯混合模型耦合的风速间隔预测经常性神经网络

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摘要

The potential of long short-term memory network on ultra-short term wind speed forecast attracted attentions of researchers in recent years. Extending a probabilistic long short-term memory network model to provide an uncertainty estimation than to make a point forecast is more valuable in practice. However, due to complex recurrent structure and feedback algorithm, large scale ensemble forecast based on resampling faces great challenges in reality. Instead, a reliable forecast method needs to be devised. Gaussian process regression is a probabilistic regression model based on Gaussian Process prior. It is reasonable to integrate Gaussian process regression with long short-term memory network for probabilistic wind speed forecast to leverage the superior fitting ability of the deep learning methods and to maintain the probability characteristics of Gaussian process regression. Hence, avoid the repeated training and heavy parameter optimization. The method is evaluated for wind speed forecast using the monitoring dataset provided by the National Wind Energy Technology Center. The results indicated that the proposed method improves the point forecast accuracy by up to 17.2%, and improves the interval forecast accuracy by up to 18.5% compared to state-of-the-art models. This study is of great significance for improving the accuracy and reliability of wind speed prediction and the sustainable development of new energy sources.
机译：近年来，超短短期风速预测的长短期内记忆网络的潜力吸引了研究人员的注意。扩展概率的长短期内存网络模型，以提供不确定的估计，而不是使点预测在实践中更有价值。然而，由于复杂的经常性结构和反馈算法，基于重采采样的大规模集合预测面临着现实的巨大挑战。相反，需要设计可靠的预测方法。高斯进程回归是基于高斯过程的概率回归模型。将高斯进程回归与长短期内存网络集成了高斯过程回归，以利用深度学习方法的卓越拟合能力，并保持高斯过程回归的概率特性。因此，避免重复的培训和重度参数优化。使用国家风能技术中心提供的监测数据集进行了对风速预测的方法。结果表明，与最先进的模型相比，该方法提出了高达17.2％的点预测精度，可提高到18.5％的间隔预测精度。该研究对于提高风速预测的准确性和可靠性以及新能源的可持续发展具有重要意义。

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