电力系统及其自动化学报2017，Vol.29Issue(7)：41-45,67,6.DOI:10.3969/j.issn.1003-8930.2017.07.007
熵判别人工蜂群算法优化的风电功率组合预测模型
Combined Wind Power Prediction Model Optimized by Entropy Criterion Artificial Bee Colony Algorithm
摘要
Abstract
In order to improve the prediction precision of wind power,a combined prediction model optimized by entro?py criterion artificial bee colony(ECABC)algorithm is proposed. With the minimum relative error as an objective func?tion,the optimal discount factor can be selected by using ECABC algorithm,and weight coefficients can be determined to further improve the model performance. The proposed algorithm can adjust the diversity of the population and dynami?cally adjust the weights of bees'searching by computing the entropy of bees. In the meantime,the bees that have inferi?or fitness values are moved to improve the searching capability dynamically. Experiments show that the proposed com?bined model can determine the weight coefficients smartly;and compared with other traditional combined models,the prediction precision is improved obviously.关键词
熵判别人工蜂群算法/权重系数/组合模型/风电功率预测Key words
entropy criterion artificial bee colony(ECABC)algorithm/weight coefficient/combined model/wind pow⁃er prediction分类
信息技术与安全科学引用本文复制引用
陈国初,公维祥,冯兆红..熵判别人工蜂群算法优化的风电功率组合预测模型[J].电力系统及其自动化学报,2017,29(7):41-45,67,6.基金项目
上海市教委科研创新资助项目(13YZ140) （13YZ140）
上海市自然科学基金资助项目(11ZR1413900) （11ZR1413900）
上海市教委重点学科资助项目(J51901) （J51901）
