基于统计模型的短期风能预测方法研究进展

赵泽妮; 云斯宁; 贾凌云; 史加荣; 贺宁; 杨柳

doi:10.19912/j.0254-0096.tynxb.2021-0500

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太阳能学报 ›› 2022, Vol. 43 ›› Issue (11) : 224-234. DOI: 10.19912/j.0254-0096.tynxb.2021-0500

基于统计模型的短期风能预测方法研究进展

赵泽妮¹, 云斯宁¹, 贾凌云¹, 史加荣², 贺宁³, 杨柳⁴

作者信息 +

RECENT PROGRESS IN SHORT-TERM FORECASTING OF WIND ENERGY BASED ON STATISTICAL MODELS

Zhao Zeni¹, Yun Sining¹, Jia Lingyun¹, Shi Jiarong², He Ning³, Yang Liu⁴

Author information +

文章历史 +

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。

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.

导出引用

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 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0500

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 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0500

中图分类号： TP183

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