Recently, wind speed forecasting as an effective computing technique plays an important role in advancing industry informatics, while dealing with these issues of control and operation for renewable power systems. However, it is facing some increasing difficulties to handle the large-scale dataset generated in these forecasting applications, with the purpose of ensuring stable computing performance. In response to such limitation, this paper proposes a more practical approach through the combination of extreme-learning machine (ELM) method and deep-learning model. ELM is a novel computing paradigm that enables the neural network (NN) based learning to be achieved with fast training speed and good generalization performance. The stacked ELM (SELM) is an advanced ELM algorithm under deep-learning framework, which works efficiently on memory consumption decrease. In this paper, an enhanced SELM is accordingly developed via replacing the Euclidean norm of the mean square error (MSE) criterion in ELM with the generalized correntropy criterion to further improve the forecasting performance. The advantage of the enhanced SELM with generalized correntropy to achieve better forecasting performance mainly relies on the following aspect. Generalized correntropy is a stable and robust nonlinear similarity measure while employing machine learning method to forecast wind speed, where the outliers may exist in some industrially measured values. Specifically, the experimental results of short-term and ultra-short-term forecasting on real wind speed data show that the proposed approach can achieve better computing performance compared with other traditional and more recent methods.

中文翻译：

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利用广义熵的堆叠极限学习机进行短期风速预测


最近，风速预测作为一种有效的计算技术，在处理可再生能源系统的控制和运行问题时，在推进行业信​​息学方面发挥着重要作用。然而，处理这些预测应用中生成的大规模数据集以确保稳定的计算性能面临着越来越多的困难。针对这种局限性，本文通过极限学习机（ELM）方法和深度学习模型的结合提出了一种更实用的方法。 ELM 是一种新颖的计算范式，能够实现基于神经网络（NN）的学习，具有快速的训练速度和良好的泛化性能。 Stacked ELM（SELM）是深度学习框架下的一种先进的ELM算法，可以有效降低内存消耗。本文相应地开发了一种增强型SELM，通过用广义相关熵准则替换ELM中均方误差（MSE）准则的欧几里德范数，以进一步提高预测性能。广义相关熵增强SELM获得更好预测性能的优势主要依赖于以下几个方面。广义相关熵是利用机器学习方法预测风速时一种稳定、鲁棒的非线性相似性测度，在一些工业测量值中可能存在异常值。具体来说，对真实风速数据的短期和超短期预测的实验结果表明，与其他传统和较新的方法相比，该方法可以实现更好的计算性能。