风能发电预测技术研究的现状与发展  

The status and research progress of prediction technology of wind power generation

作　　者：陈垣毅[1,2] 郑增威[1] 霍梅梅[1] 赵波[3] 

机构地区：[1]浙江大学城市学院,浙江杭州310015 [2]浙江大学计算机学院,浙江杭州310027 [3]浙江省电力试验研究院,浙江杭州310014

出　　处：《能源工程》2012年第6期18-22,34,共6页Energy Engineering

基　　金：浙江省自然科学基金项目(Y1111254);浙江省公益技术应用研究计划项目(2010C31021)

摘　　要：对过去10年来主要的风能发电预测技术进行了概述和追踪,重点阐述了基于统计学(如卡尔曼滤波、数据挖掘和小波变换等)和人工智能(如神经网络、模糊推理和生物智能算法等)技术的发电预测方案。最后指出了各种预测技术存在的不足及改进方向,以帮助相关领域的研究者提出更好的发电预测模型。The main prediction technology of wind power generation during the past 10 years was outlined and tracked,and these prediction models based on statistics(such as kalman filtering,data mining and wavelet transform,etc) and artificial intelligence(such as neural networks,fuzzy inference and biological intelligence algorithm,etc.) were introduced.Finally,the shortcomings and improved directions of various forecasting techniques were pointed out to help researchers in related fields propose better prediction model of power generation forecast.

关 键 词：微电网 风速预测 卡尔曼滤波 数据挖掘 人工神经网络 模糊推理 生物进化算法 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]