可再生能源2017，Vol.35Issue(7)：1081-1087,7.
基于经验模态分解和马尔科夫链的弃风电量预测
A hybrid model for wind farm abandoned wind power forecasting based on empirical mode decomposition and Markov chain
摘要
Abstract
Taking wind power history data and wind turbines power data into consideration,take the difference between the two groups of the data as wind farm abandoned wind power time series,which is predicted using the algorithm that can be achieved by combinating empirical mode decomposition with Markov chain. First of all, using EMD to decompose wind farm abandoned wind power time series to improve stability and periodicity and generate 9 sequence components;Then,dividing the state of each sequence component,using Markov chain to generate 9 sequence components;Finally,the results of each component forecasting are superimposed to obtain the final forecasting result. Take one of Xinjiang wind farms as an example to simulate and verify the algorithm. The research results show that the hybrid model has better performance to track the trend of abandoned wind power,the prediction accuracy can meet the corresponding requirements.关键词
弃风电量/经验模态分解/马尔科夫链/预测Key words
abandoned wind power/empirical mode decomposition (EMD)/Markov chain/prodiction分类
能源科技引用本文复制引用
高磊,谢丽蓉,晁勤,牛永朝..基于经验模态分解和马尔科夫链的弃风电量预测[J].可再生能源,2017,35(7):1081-1087,7.基金项目
国家自然科学基金项目(51667021). （51667021）
