摘要
常规的月售电量预测线性回归模型存在两点影响预测精度的问题:在考虑温度的影响时忽略了舒适温度区间内不存在采暖和制冷措施的事实;由于随机变动不易量化而忽略了随机变动的影响。为解决上述两点问题,提出两种改进措施:分别选择低温阈值温度与高温阈值温度,且仅当实际温度低于低温阈值温度或高于高温阈值温度才产生采暖措施或制冷措施;提出将随机变动量化的方法,并将其量化值作为月售电量影响因素纳入预测模型。常规的月售电量预测线性回归模型经过改进后,能更好地建立温度与月售电量的关系,同时能合理地考虑随机变动对月售电量的影响,有利于提高预测精度。用重庆市铜梁区实际数据仿真分析,验证了两种改进措施的有效性。
There are two problems in conventional linear regression model for forecasting monthly electricity sales that affect precision： the model ignores the fact that there are no heating measures and cooling measures in comfortable temperature range; the model also ignores the influence of random factors on monthly electricity sales because of random factors are difficult to quantify. To solve the above two problems, this paper puts forward two improvement measures： selecting low threshold temperature and high threshold temperature, and producing heating or cooling measures only when the actual temperature is below low threshold temperature or above high threshold temperature; proposing a method to quantify factors and putting the quantization value into prediction model as a factor of monthly electricity sales. The improved model can not only establish the relationship between temperature and monthly electricity sales preferably but also can account for the effect of random factors on monthly electricity sales reasonably, so the proposed measures are useful to improve precision. Making simulation analysis with actual data of Tongliang district in Chongqing, and the results show that the two kinds of improvement measures are effective.
作者
薛斌
程超
欧世其
刘安祥
王顺昌
XUE Bin CHENG Chao OU Shiqil LIU Anxiang WANG Shunchang(Tongliang Power Supply Limited Liability Company, Chongqing 402560, China Yalong River Hydropower Development Company, Ltd., Chengdu 610056, China)
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2017年第1期15-20,共6页
Power System Protection and Control
关键词
月售电量预测
线性回归模型
影响因素
温度
随机变动
prediction of monthly electricity sales
linear regression model
influence factor
temperature
random factors
