杭州市燃气负荷与气象条件的响应关系及其预测模型

Responses and Forecast Model of Gas Load to Meteorological Conditions in Hangzhou

利用杭州市2008-2011年逐日燃气负荷和气象要素资料,分析了燃气负荷的变化规律及气象条件对燃气负荷的影响,在此基础上,利用Elman神经网络建立燃气负荷预测模型.结果表明,研究时段内,杭州市燃气负荷逐年显著增长且具有显著的季节变化特征,春节假日期间会出现明显的燃气负荷谷值,年燃气负荷峰值点通常出现在春节前1个月.平均气温与平均气压是影响燃气负荷波动最主要的气象因子,且均在冬季相关最显著.平均气温与燃气负荷在各个季节呈一致负相关,平均气压成正相关,燃气负荷对平均气温的响应敏感区间为6-15℃.在考虑春节假期影响的基础上,筛选相关气象因子,利用Elman神经网络建立杭州市冬半年燃气负荷预测模型.预测结果表明,一般情况下,模型精度较高,但当燃气负荷出现大的波动时,模拟结果呈现一定程度的滞后性.

Using the daily gas load data and corresponding meteorological observational datasets in Hangzhou from 2008 to 2011, the variation characteristics of daily gas load and the impacts of meteorological conditions on gas loads are analyzed. Based on this, the daily gas load is predicted with the Elman network. The results show that the gas load increased significantly from 2008 to 2011 in Hangzhou. The peak point appeared usually one month before the Spring Festival, and the variation of the gas load decreased obviously during Spring Festival holidays. Temperature and pressure were the main factors responsible for the variation of gas load, with the correlation being most significant in winter. The gas load was significantly correlated to temperature negatively and pressure positively, most sensitive to the average air temperature from 6 ℃ to 15 ℃. According to the influence of meteorological factors and the Spring Festival, the daily gas load forecasting model in the wintertime is established with the Elman network. Based on the indexes such as mean relative error and correlation coefficient, it is illustrated that the Elman network has a satisfactory precision. While there appears noticeable fluctuations in the gas load, the result of simulation lags behind the observation.