| 关于利用空间相关性预测风速的评述 |
| |
| 引用本文： | 薛禹胜,陈宁,王树民,文福拴,林振智,汪震.关于利用空间相关性预测风速的评述[J].电力系统自动化,2017,41(10):161-169. |
| |
| 作者姓名： | 薛禹胜 陈宁 王树民 文福拴 林振智 汪震 |
| |
| 作者单位： | 南瑞集团公司(国网电力科学研究院), 江苏省南京市 211106; 智能电网保护和运行控制国家重点实验室, 江苏省南京市 211106,东南大学电气工程学院, 江苏省南京市 210096; 新能源与储能运行控制国家重点实验室(中国电力科学研究院), 江苏省南京市 210003,中国神华能源股份有限公司, 北京市100011,浙江大学电气工程学院, 浙江省杭州市 310027; 文莱科技大学电机与电子工程系, 斯里巴加湾 BE1410, 文莱,浙江大学电气工程学院, 浙江省杭州市 310027,浙江大学电气工程学院, 浙江省杭州市 310027 |
| |
| 基金项目： | 国家自然科学基金重点项目(61533010);NSFC-NRCT(中泰)合作研究项目(51561145011);国家电网公司科技项目 |
| |
| 摘 要： | 归纳了空间相关性风速预测的现状;引入条件相关性及相应的可信相关度概念,以代替常规的相关性;基于大数据思维,提出将数据驱动与因果驱动相结合的预测框架。从历史数据中挖掘相关性,利用空间相关性增加风速预测的数据源,部分克服历史数据缺失的困难;利用大时滞的空间相关性,有助于预测下游风速的突变。最后,依托该框架展望了空间相关性风速预测的前景。
|
| 关 键 词： | 风速预测 空间相关性 动态特征 离线分类建模 在线特征匹配 |
| 收稿时间： | 2017/1/9 0:00:00 |
| 修稿时间： | 2017/4/3 0:00:00 |
Review on Wind Speed Prediction Based on Spatial Correlation |
| |
| XUE Yusheng,CHEN Ning,WANG Shumin,WEN Fushuan,LIN Zhenzhi and WANG Zhen.Review on Wind Speed Prediction Based on Spatial Correlation[J].Automation of Electric Power Systems,2017,41(10):161-169. |
| |
| Authors: | XUE Yusheng CHEN Ning WANG Shumin WEN Fushuan LIN Zhenzhi and WANG Zhen |
| |
| Affiliation: | NARI Group Corporation(State Grid Electric Power Research Institute), Nanjing 211106, China; State Key Laboratory of Smart Grid Protection and Control, Nanjing 211106, China,School of Electrical Engineering, Southeast University, Nanjing 210096, China; State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems(China Electric Power Research Institute), Nanjing 210003, China,China Shenhua Energy Company Limited, Beijing 100011, China,School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; Department of Electrical & Electronic Engineering, Universiti Teknologi Brunei, Bandar Seri Begawan BE1410, Brunei,School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China and School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China |
| |
| Abstract: | The state-of-the-art development of spatial correlation based wind speed prediction is reviewed. And the concepts of conditional correlation and its corresponding confidence correlation are introduced to improve traditional spatial correlation. Based on big-data thinking, a framework of integrating data-driven with causality-driven wind speed prediction is proposed. In the framework, correlation is mined from historical data for wind speed prediction. Spatial correlation is employed to import data sources for wind speed prediction to overcome the shortage of historical data in part. Furthermore, spatial correlation with long time lag can be used to predict drastic and sudden change in downstream wind speed. Finally, suggestions for future research under the proposed framework can be made with confidence. This work is supported by the State Key Program of National Natural Science Foundation of China(No. 61533010), NSFC-NRCT(Sino Thai)Cooperation Research Project(No. 51561145011)and State Grid Corporation of China. |
| |
| Keywords: | wind speed prediction spatial correlation dynamic features offline modeling by classification online feature matching |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《电力系统自动化》浏览原始摘要信息 |
|
点击此处可从《电力系统自动化》下载全文 |
|
