Wind power forecasting - A data-driven method along with gated recurrent neural network
Wind power forecasting - A data-driven method along with gated recurrent neural network
复制标题
DOI：
10.1016/j.renene.2020.10.119
复制
发表时间：
2021-01-01
期刊：
影响因子：
8.7
通讯作者：
Liu, Xiaolei
中科院分区：
文献类型：
--
作者：
Kisvari, Adam;Lin, Zi;Liu, Xiaolei
有效的风电预测将有助于实现世界可持续发展的长期目标。然而，风作为一种能源的缺点在于它的高可变性，导致在风功率预测的一个具有挑战性的研究。为了解决这个问题，提出了一种新的数据驱动的方法，通过集成数据预处理和重新采样，异常检测和处理，特征工程，并基于门控递归深度学习模型的超参数调整，这是第一次系统地提出。此外，成功开发了一种新型的深度学习神经网络GRU，并与长短期记忆算法LSTM进行了比较。最初，12个特征被设计到预测模型中，这些特征是四个不同高度的风速、发电机温度和齿轮箱温度。仿真结果表明，在风电功率预测方面，所提出的方法可以捕获在较低的计算成本的高精度。还可以得出结论，在所有观察到的测试中，GRU在预测准确性方面优于LSTM，这提供了更快的训练过程，并且对所使用的监控和数据采集（SCADA）数据集的噪声敏感性更低。(C)2020爱思唯尔有限公司保留所有权利。
Effective wind power prediction will facilitate the world's long-term goal in sustainable development. However, a drawback of wind as an energy source lies in its high variability, resulting in a challenging study in wind power forecasting. To solve this issue, a novel data-driven approach is proposed for wind power forecasting by integrating data pre-processing & re-sampling, anomalies detection & treatment, feature engineering, and hyperparameter tuning based on gated recurrent deep learning models, which is systematically presented for the first time. Besides, a novel deep learning neural network of Gated Recurrent Unit (GRU) is successfully developed and critically compared with the algorithm of Long Short-term Memory (LSTM). Initially, twelve features were engineered into the predictive model, which are wind speeds at four different heights, generator temperature, and gearbox temperature. The simulation results showed that, in terms of wind power forecasting, the proposed approach can capture a high degree of accuracy at lower computational costs. It can also be concluded that GRU outperformed LSTM in predictive accuracy under all observed tests, which provided faster training process and less sensitivity to noise in the used Supervisory Control and Data Acquisition (SCADA) datasets. (C) 2020 Elsevier Ltd. All rights reserved.