利用特征选择和深度学习的新型混合预报模型用于风速研究

IF 3.4 3区 经济学 Q1 ECONOMICS Journal of Forecasting Pub Date : 2024-02-28 DOI:10.1002/for.3098
Xuejun Chen, Ying Wang, Haitao Zhang, Jianzhou Wang
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引用次数: 0

摘要

准确的风速预测对风电场的运行非常重要,人们一直在努力开发这方面的有效预测方法。然而,数据输入的特征选择以及深度学习模型的优化相对较少受到关注,导致预测结果不可靠。本研究提出了一种新型混合模型,该模型将数据预处理、特征选择和优化预测整合在一起,以改进风速预测。具体来说,利用强大的预处理技术减少数据噪声干扰,同时设计创新的两阶段特征选择,以实现预报目的的最佳输入数据格式。此外,还开发了基于长短期记忆的混合预报模块,并通过贝叶斯优化算法进行了优化,以提高模型的效率和可靠性。实证研究使用了四季 10 分钟间隔的风速数据进行演示,评估结果表明其在有效学习风速序列的波动性和不规则性特征方面表现出色,为风力发电系统的实际应用奠定了坚实的基础。
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A novel hybrid forecasting model with feature selection and deep learning for wind speed research

Accurate wind speed prediction is of great importance for the operation of wind farms, and extensive efforts have been made to develop effective forecasting methods in this regard. However, the feature selection of data input as well as optimization of deep learning models have received comparatively less attention, leading to unreliable forecasting results. This research proposes a novel hybrid model that integrates data preprocessing, feature selection, and optimized forecasting for improved wind speed prediction. Specifically, a powerful preprocessing technique is utilized to reduce data noise disturbances, while an innovative two-stage feature selection is designed to achieve the optimal input data format for forecasting purposes. Moreover, a hybrid forecasting module based on long-short term memory, which is optimized by the Bayesian optimization algorithm, has been developed to enhance the efficiency and reliability of the model. The empirical study used 10-min interval wind speed data of four seasons for presentation and evaluation results demonstrated its superior performance in effectively learning the volatility and irregularity features of wind speed series, which established a solid foundation for practical applications in wind power systems.

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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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