| 基于统计模型的短期风能预测方法研究进展 |
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| 引用本文： | 赵泽妮,云斯宁,贾凌云,史加荣,贺宁,杨柳.基于统计模型的短期风能预测方法研究进展[J].太阳能学报,2022,43(11):224-234. |
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| 作者姓名： | 赵泽妮 云斯宁 贾凌云 史加荣 贺宁 杨柳 |
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| 作者单位： | 1.西安建筑科技大学材料科学与工程学院,西安 710055; 2.西安建筑科技大学理学院,西安 710055; 3.西安建筑科技大学机电工程学院,西安 710055; 4.西安建筑科技大学建筑学院,西安 710055 |
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| 基金项目： | 国家重点研发计划（2018YFB1502902） |
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| 摘 要： | 以确定性短期风能预测为出发点,综述常用的4种统计模型的研究进展,包括时间序列方法、人工神经网络、支持向量机和深度学习。针对基础统计模型预测效果不佳的问题,提出各类混合模型。数据预处理、优化算法与基础统计模型之间的组合,或人工神经网络与卷积神经网络、循环神经网络等深度学习模型之间的组合,对预测水平都有很好的提升作用。
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| 关 键 词： | 风力发电 机器学习 预测 数据处理 混合系统 |
| 收稿时间： | 2021-05-10 |
RECENT PROGRESS IN SHORT-TERM FORECASTING OF WIND ENERGY BASED ON STATISTICAL MODELS |
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| Zhao Zeni,Yun Sining,Jia Lingyun,Shi Jiarong,He Ning,Yang Liu.RECENT PROGRESS IN SHORT-TERM FORECASTING OF WIND ENERGY BASED ON STATISTICAL MODELS[J].Acta Energiae Solaris Sinica,2022,43(11):224-234. |
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| Authors: | Zhao Zeni Yun Sining Jia Lingyun Shi Jiarong He Ning Yang Liu |
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| Affiliation: | 1. School of Materials Science and Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China; 2. School of Science, Xi'an University of Architecture and Technology, Xi'an 710055, China; 3. School of Mechanical and Electrical Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China; 4. School of Architecture, Xi'an University of Architecture and Technology, Xi'an 710055, China |
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| Abstract: | Considering the low prediction performances achieved by statistical models, various hybrid models have been proposed. By combining data preprocessing and optimization algorithms with basic statistical models or integrating artificial neural networks, convolutional neural networks, and recurrent neural networks, researchers can significantly improve the performance of short-term forecasting of wind energy. |
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| Keywords: | wind power machine learning forecasting data processing hybrid systems |
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