西安市日最大电力负荷率与气象因子相关关系分析预报模型的建立

基于西安市2010—2013年逐日最大电力负荷和同期的气象资料,分析了日最大电力负荷的变化规律,利用最小二乘法,除去日最大负荷的节假日效应和周末效应后,将气象负荷从日最大电力负荷中分离出来,建立西安气象负荷率与气温、相对湿度、总云量、降水量、风速的相关关系,并基于2010—2012年的11月—次年2月和6—8月的资料,分别采用逐步回归、多元线性回归和BP神经网络方法建立最大气象负荷和主要气象影响因子之间的预报模型,将2013年对应时间的日最大气象负荷率作为预报效果的独立样本检验。结果显示:2010—2013年西安的日最大电荷存在明显的增长趋势,且存在明显的周末效应和节假日效应;气温是影响气象负荷率的最显著因子,引入温湿指数(THI)的BP神经网络算法对气象负荷率的拟合和预测效果最优。

Establishment of prediction model of relationship between daily maximum power load rate and meteorological factors in Xi'an

Based on the daily maximum power load and meteorological data of Xi''an from 2010 to 2013, the variation law of daily maximum power load was analyzed, and the meteorological load was separated from daily maximum power load using the least square method after eliminating the holiday effect and weekend effect of daily maximum load. Then the correlation between meteorological load rate and temperature, relative humidity, total cloud, precipitation and wind speed in Xi''an was established. Based on the data of the period from November to February and the period from June to August from 2010 to 2012, the forecast models between the maximum meteorological load and the main meteorological factors were established by stepwise regression, multiple linear regression and BP neural network, respectively. The daily maximum meteorological load rate of the corresponding time in 2013 was taken as the independent sample test of the forecast effect. The results show that the daily maximum load in Xi''an from 2010 to 2013 has an obvious increasing trend year by year, and there are obvious weekend effect and holiday effect. Temperature is the most significant factor affecting the meteorological load rate, and the BP neural network algorithm with Temperature and Humidity Index (THI) is the best for fitting and forecasting the meteorological load rate.