.png){kind=icon}
返回
Wind forecasting using Principal Component Analysis
.png){kind=icon}
PRE
AI
DOI：10.1016/j.renene.2014.03.068.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We present a new statistical wind forecasting tool based on Principal Component Analysis (PCA), which is trained on past data to predict the wind speed using an ensemble of dynamically similar past events. At the same time the method provides a prediction of the likely forecasting error. The method is applied to Meteorological Office wind speed and direction data from a site in Edinburgh. For the training period, the years 2008-2009 were used, and the wind forecasting was tested for the data from 2010 for that site. Different parameter values were also used in the PCA analysis to explore the sensitivity analysis of the results. The forecasting results demonstrated that the technique can be used to forecast the wind up to 24 h ahead with a consistent improvement over persistence for forecasting more than 10 h ahead. The comparison of the forecasting error with the uncertainty estimated from the error growth in the ensemble forecast showed that the forecasting error could be well predicted. (C) 2014 Elsevier Ltd. All rights reserved.
Keyword:
Wind energy resource
Principal Component Analysis (PCA)
Forecasting
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.1
论文数:
2.6W
被引数:
13.3W
机构
引用论文
.png){kind=icon}
分享收藏
Evaluation of hybrid forecasting approaches for wind speed and power generation time series风速和发电时间序列混合预测方法的评估
PREAI
分享收藏
查看更多内容