Multi-dimensional scenario forecast for generation of multiple wind farms  

作　　者：Ming YANG You LIN Simeng ZHU Xueshan HAN Hongtao WANG 

机构地区：[1]Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education,Laboratory of Electric Vehicles Engineering of Shandong Province,Shandong University,Jinan 250061,China [2]Zaozhuang Power Supply Company,Zaozhuang 277800,China

出　　处：《Journal of Modern Power Systems and Clean Energy》2015年第3期361-370,共10页现代电力系统与清洁能源学报（英文）

基　　金：This work is supported by National Natural Science Foundation of China(No.51007047,No.51077087);Shandong Provincial Natural Science Foundation of China(No.20100131120039);National High Technology Research and Development Program of China(863 Program)(No.2011AA05A101).

摘　　要：A novel multi-dimensional scenario forecast approach which can capture the dynamic temporal-spatial interdependence relation among the outputs of multiple wind farms is proposed.In the proposed approach,support vector machine(SVM)is applied for the spot forecast of wind power generation.The probability density function(PDF)of the SVM forecast error is predicted by sparse Bayesian learning(SBL),and the spot forecast result is corrected according to the error expectation obtained.The copula function is estimated using a Gaussian copula-based dynamic conditional correlation matrix regression(DCCMR)model to describe the correlation among the errors.And the multidimensional scenario is generated with respect to the estimated marginal distributions and the copula function.Test results on three adjacent wind farms illustrate the effectiveness of the proposed approach.

关 键 词：Wind power generation forecast Multidimensional scenario forecast Support vector machine(SVM) Sparse Bayesian learning(SBL) Gaussian copula Dynamic conditional correlation matrix 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]