摘要
为解决单一预测模型难以适应西南电网不同区域的负荷变化问题,针对西南电网各区域年负荷变化的特点,提出采用计及综合指标评价的负荷变权组合预测模型。首先引入改进灰色关联度指标、预测有效度指标和模型冗余检验指标作为模型选择依据,形成适合不同区域的历史负荷变化的基模型库。然后引入自适应变权重算子对基模型进行组合预测,获得西南电网区域年负荷预测值。算例利用四川省、重庆市以及西藏自治区2006—2019年的电力消费量进行测试,结果表明所提预测方法能够有效预测西南电网负荷变化,相比最优权重模型、等权模型和最优单一模型算法具有更高的预测精度。
In view the fact that a single forecasting model could hardly adapt to the load changes in different regions of Southwest China grid,in allusion to characteristics of annual load changes in various regions in Southwest China grid,it was proposed to adopt a load variable weight combination forecasting model,in which the comprehensive index evaluation was taken into account.Firstly,the improved grey relation index,the predictive validity index and the model redundancy test index were led in and taken as the basis for the model selection to form the base model library suitable to historical load changes in different regions.Secondly,the adaptive variable weight operator was introduced to conduct the combinational forecasting for base model to obtain regional annual load forecasting values of Southwest China grid.The consumption of electric power in Sichuan province,Chongqing city and Tibet Autonomous Region from 2006 to 2019 was utilized in the computing example for the test,and testing results show that the proposed forecasting method can effectively forecast the load change in Southwest China grid,and the obtained forecasting result by the proposed method is more accurate than those by optimal weight model,equal weight model and optimal single model.
作者
蔡绍荣
江栗
张姝
郑瑞骁
CAI Shaorong;JIANG Li;ZHANG Shu;ZHENG Ruixiao(State Grid Southwest Branch Co.,Ltd.,Chengdu 610031,Sichuan Province,China;School of Electrical Engineering,Sichuan University,Chengdu 610031,Sichuan Province,China;Power Dispatching Control Center of State Grid Chengdu Power Supply Company,Chengdu 610095,Sichuan Province,China)
出处
《现代电力》
北大核心
2022年第5期562-569,共8页
Modern Electric Power
基金
国家自然科学基金资助项目(52007126)
国家电网科技项目(SGSW0000GHJS1900107)。
关键词
西南电网
中长期负荷预测
综合指标
变权重
组合预测
Southwest China grid
medium-and long-term load forecasting
comprehensive index
variable weight
combination forecasting
