夏季日最大降温负荷的估算和预测方法  

An estimating and forecasting method for daily maximum cooling load in summer

作　　者：刘思捷[1] 张海鹏[2] 林舜江[2] 刘明波[2] 李嘉龙[1] 王一[1] 孙谦 

机构地区：[1]广东电网公司电力调度控制中心,广东广州510600 [2]华南理工大学电力学院,广东广州510640

出　　处：《电力系统保护与控制》2016年第5期75-81,共7页Power System Protection and Control

基　　金：国家自然科学基金项目(51207056);广东电网公司电力调度控制中心科技项目(GDDW2020130303SC00044)~~

摘　　要：随着空调等降温设备的大量使用,降温负荷对电力系统安全经济运行的影响越来越显著。以广州市的历史负荷和气温数据为基础,分析了广州市夏季降温负荷与气温之间相关性。首先,考虑到夏季基准负荷逐日的增长量,提出利用灰色系统GM(1,1)模型预测出电网夏季的日基准负荷曲线,进而准确剥离出夏季的日降温负荷曲线,并分析了日降温负荷曲线的"W"型变化特征。其次,基于日最大降温负荷与日最高温度的相关性分析,建立了日最大降温负荷与日最高温度之间关系的分段回归模型,并对日最大降温负荷进行预测。最后考虑温度累积效应的影响,对分段回归模型进行了修正,进一步提高了预测精度,从而为准确预测电网夏季日高峰负荷提供依据。With the widely use of cooling equipment such as air conditioner, the influence of cooling-load on security and economy operation of power system is increasingly obvious. According to the historical load data and temperature data, this paper analyses the correlation between cooling-load and temperature in summer in Guangzhou city. First, considering the daily increment of base load in summer, daily base load curve of power grid in summer is predicted by using GM（1,1） model, and then the cooling-load curve in summer is separated accurately, and the ‘W＇ changing feature of daily cooling-load curve is also analyzed. Second, based on the correlation analysis between daily maximum cooling-load and daily maximum temperature, this paper establishes piecewise regression model between them, and predicts the daily maximum cooling-load. Finally, considering the influence of accumulative effect of temperature, this paper improves the piecewise regression model to further increase the prediction accuracy, which can provide basis to accurately predict the daily peak load in summer of power grid.

关 键 词：降温负荷 GM(1 1)模型 相关性分析 分段回归模型 温度累积效应 

分 类 号：TM714[电气工程—电力系统及自动化]