基于气象信息和熵权理论的降温负荷估算方法

空调数量增长和极端高温天气使得夏季降温负荷大幅增长,已成为最大负荷屡创新高的重要原因。为了更准确估算降温负荷大小,提出一种基于气象信息和熵权理论的降温负荷估算方法。该方法采用全年最大负荷日负荷曲线与不含降温负荷的基准负荷曲线对应相减后取最大值,从而得到年最大降温负荷。在计算基准负荷曲线时,以气温、相对湿度、降水量等多种气象为轴建立气象坐标系统,通过确定基准气象象限以筛选无降温负荷的基准工作日;根据基准工作日的日最大负荷与气温、相对湿度、降水量等气象信息的相关系数,利用熵权理论确定各基准工作日负荷曲线相对基准负荷曲线权值。最后,利用广州市2009至2013年负荷数据及气象数据估算广州市年最大降温负荷。

Temperature-lowering Load Estimation Method Based on Meteorological Data and Entropy Weight Theory

Growing quantity of air-conditioners and extremely hot weather has made the temperature-lowing load increase as never before. To estimate temperature-lowering load more accurately, an estimation method based on meteorological data and entropy weight theory is proposed. The maximum value of annual maximum load curve load date less the benchmark curve without temperature-lowering load is annual maximum temperature-lowering load. A meteorological axis system is built based on temperature, relative humidity, precipitation and other meteorological data to determine base meteorological quadrant and filtrate benchmark weekday when calculating base load curve. According to the relevant coefficients between maximum daily load and temperature, relative humidity or precipitation on benchmark weekdays, the weights of benchmark weekday load curves are calculated using the entropy weight theory. Finally, the annual maximum temperature-lowering load is estimated according to Guangzhou 2009-2013 load and meteorological data. The result shows that the new method is more appropriate. This work is supported by National Natural Science Foundation of China(No. 50907023)and China Southern Power Grid Company Limited(No. K-GD2012-006).