水准点(测量)
期限(时间)
风力发电
集合预报
分解
计算机科学
风电预测
希尔伯特-黄变换
特质
功率(物理)
算法
电力系统
人工智能
数据挖掘
数学
统计
工程类
能量(信号处理)
程序设计语言
地理
电气工程
物理
生物
量子力学
生态学
大地测量学
作者
Lean Yu,Yixiang Ma,Yueming Ma,Guoxing Zhang
标识
DOI:10.1016/j.seta.2021.101794
摘要
In order to improve the accuracy of the short-term wind power forecasting, a novel complexity-trait-driven rolling decomposition-reconstruction-ensemble forecasting model is proposed to predict short-term wind power. In this model, four steps are involved, i.e., data decomposition, mode reconstruction, component prediction and ensemble prediction, which are all driven by complexity trait. In addition, rolling mechanism is applied to the decomposition-reconstruction-ensemble model to solve the problem of the misuse of future information. For verification, the proposed model is used to predict the total wind power with 5-minute interval data. The empirical result demonstrates that the proposed model has better prediction performance than the benchmark models. Compared with the benchmark models, the average improvement percentage of the proposed model is 46.819%, in terms of the mean absolute percentage error. This indicates that the proposed complexity-trait-driven rolling decomposition-reconstruction-ensemble model can be used as an effective tool for short-term wind power forecasting.
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