| 基于改进ABC算法的中长期电力负荷组合预测 |
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| 引用本文： | 陈强,金小明,姚建刚,杨胜杰,龚磊,吴兆刚.基于改进ABC算法的中长期电力负荷组合预测[J].电力系统保护与控制,2014,42(23):113-117. |
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| 作者姓名： | 陈强 金小明 姚建刚 杨胜杰 龚磊 吴兆刚 |
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| 作者单位： | 湖南大学电气与信息工程学院，湖南 长沙 410082;南方电网技术研究中心，广东 广州 510620;湖南大学电气与信息工程学院，湖南 长沙 410082;湖南湖大华龙电气与信息技术有限公司，湖南 长沙 410082;湖南大学电气与信息工程学院，湖南 长沙 410082;湖南大学电气与信息工程学院，湖南 长沙 410082 |
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| 基金项目： | 国家自然科学基金项目(51277059) |
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| 摘 要： | 将人工蜂群(ABC)算法应用到中长期电力负荷预测中,通过与组合预测模型相结合,对组合预测目标函数进行优化权重求解。另外针对该算法的早期收敛速度慢、后期容易陷入局部最优的缺点,通过引入扰动项,并进行最坏蜜源替代予以解决。实例分析证明该改进算法收敛速度快,全局寻优能力强。利用它求得的组合预测值,相对于单一模型的预测结果,精度有较大的提高,说明该改进算法应用到中长期电力负荷预测中是可行的。
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| 关 键 词： | ABC算法 中长期电力负荷 组合预测 扰动项 OBL策略 |
| 收稿时间： | 2014/1/20 0:00:00 |
| 修稿时间： | 2014/3/31 0:00:00 |
Improved artificial bee colony algorithm applied to medium and long-term load combination forecasting |
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| CHEN Qiang,JIN Xiao-ming,YAO Jian-gang,YANG Sheng-jie,GONG Lei and WU Zhao-gang.Improved artificial bee colony algorithm applied to medium and long-term load combination forecasting[J].Power System Protection and Control,2014,42(23):113-117. |
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| Authors: | CHEN Qiang JIN Xiao-ming YAO Jian-gang YANG Sheng-jie GONG Lei and WU Zhao-gang |
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| Affiliation: | College of Electrical and Information Engineering, Hunan University, Changsha 410082, China;Electric Power Research Institute, CSG, Guangzhou 510620, China;College of Electrical and Information Engineering, Hunan University, Changsha 410082, China;Hunan HDHL Electrical & Information Technology Co., Ltd., Changsha 410082, China;College of Electrical and Information Engineering, Hunan University, Changsha 410082, China;College of Electrical and Information Engineering, Hunan University, Changsha 410082, China |
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| Abstract: | Artificial bee colony (ABC) algorithm is applied to the medium and long term power load forecasting. Combined with the combination forecasting model, it optimizes the weights of combination prediction in objective function. A disturbing term and worst honey substitution are introduced to overcome the problems of slow convergence speed in the early stage and easy falling to local optimum in the late stage of the existing ABC algorithm. Case analysis shows that the improved method has rapid convergence and strong global optimization. Compared with the forecasting result of single model, the combination forecasting value by using the improved method is more accurate, which shows it is feasible in the medium and long term power load forecasting. |
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| Keywords: | artificial bee colony algorithm medium and long-term electricity load combination forecasting disturbing term OBL strategy |
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