文献类型：期刊文献

中文题名：采用贝叶斯–克里金–卡尔曼模型的多风电场风速短期预测

英文题名：Short-Term Wind Speed Forecasting For Multiple Wind Farms Using Bayesian Krigedkalman Mode

作者：卿湘运[1];杨富文[1];王行愚[1]

机构：[1]华东理工大学自动化系,上海市徐汇区200237

年份：2012

卷号：32

期号：35

起止页码：107

中文期刊名：中国电机工程学报

外文期刊名：Proceedings of the CSEE

收录：CSTPCD;;EI(收录号：20130315910058);Scopus;北大核心:【北大核心2011】；CSCD:【CSCD2011_2012】；

基金：中央高校基础研究基金项目(WH1114059;WJ0913001);国家自然科学基金项目(61074113;61174064);国家重点基础研究发展计划项目(973计划)(2012CB720502)~~

语种：中文

中文关键词：风电场;短期风速预测;克里金卡尔曼滤波;变分贝叶斯;时空模型;概率图模型

外文关键词：wind farms; short-term wind speed forecast; Kriged Kalman filtering; variational Bayes; spatio temporal model; probabilistic graphical model

摘要：精确的短期风速预测对可靠安全的电力系统运行很重要。传统的预测方法没有考虑空间相邻风电场的信息。然而,多个风电场的风速在时间和空间上是相关的。该文给出了一个采用贝叶斯克里金卡尔曼模型的短期风速预测方法。由主克里金函数构成的空域结构使用贝叶斯层次结构进行建模,同时应用状态空间模型对时域动态性进行建模。采用计算速度更有效的变分贝叶斯方法来逼近推断和学习模型参数。在公开的多风电场数据集上评估提前1h的风速预测性能,与持续预测算法进行比较的结果显示了该文提出的方法在均方根误差评价指标上的改善。
Accurate short-term wind speed forecasting is critical to reliable and secure power system operations.Traditional forecasting approaches did not take account of the spatial neighborhoods information.However,wind speeds in multiple wind farms are correlated both in time and space.This paper introduced a short-term wind speed forecasting approach using Bayesian KrigedKalman model in which spatial structure with principal Kriging functions was modeled by a Bayesian hierarchical structure and time dynamic component was modeled by a state space process.Variational Bayesian method was applied to inference approximately and learn parameters of the model,which was a computationally efficient deterministic approach.One hour ahead forecasting was evaluated on publicly available wind data of multiple wind farms.The results were compared to persistence forecasting approach to show its improvement in terms of root mean square errors.