摘要
根据2002-2004年5月1日至9月1日期间的日最大电力负荷与相应日期的日平均气温、相对湿度和风速资料，分析了北京地区夏季电力负荷的变化特征，并将2002年和2003年数据作为训练样本，2004年数据作为测试样本，应用多元回归、非线性回归和BP神经网络方法对每日电力最大负荷进行模拟。结果表明，几种方法均能对电力负荷进行较好地模拟，其中神经网络方法的模拟能力最强，其次为非线性回归和多元回归分析方法。
The daily power peak loads are highly correlated with weather conditions. Base on the analysis of daily meteorological data and power peak load data in the summer （from 1 May to 30 September） of 2002 to 2004 in the Beijing area, the temporal characteristics of the power peak loads are studied. The power peak loads in the Beijing area have obvious seasonal and weekly variation trends. By using the regression and artificial neural network methods, the power peak loads are simulated and forecasted with weather data. The results show that these methods can be used to forecast the daily power ：peak loads, and the artificial neural networks method is better.
出处
《气象科技》
2008年第6期801-805,共5页
Meteorological Science and Technology
基金
"北京市城市生命线气象服务系统"项目资助
关键词
电力负荷
回归分析
BP神经网络
气象要素
北京
power peak load, regression analysis, artificial neural network, meteorological element, Beijing
