基于气温的城市燃气短期日负荷预测模型——以四川省成都地区为例

A short-term forecasting model of city gas daily load based on air temperature

随着城市燃气用气规模的不断增长,加上近年来冬季长期低温、气温骤降等极端天气屡屡出现,因气温变化导致的日用气峰谷差有扩大的趋势,采用中长期数据进行趋势预测可能存在较大误差。而利用短期的气温、用气负荷数据,结合较为准确的一周内气象预报对未来几天内的城市燃气日负荷进行预测,对上游供气企业、城市燃气公司合理安排销售计划和日调峰等具有重要的现实意义。为此,通过分析气温对城市燃气短期日负荷的影响机理,利用统计学和化学动力学理论,基于阿累尼乌斯方程反应的温度—化学反应速率关系,考虑气温突变、低温累积等因素构建了气温对城市燃气短期日负荷的预测模型,并利用成都地区短期的气温、用气负荷数据,测算了模型参数并对模型进行了验证(最大误差为2.53%)。结果表明,该模型的预测精度较高,可为燃气公司或上游供气企业合理安排销售计划和日调峰等提供决策支撑。

The need for city gas consumption is rapidly increasing in present China. Moreover, such extreme weather as long-term low temperatures or sudden drop in temperature frequently strikes in winter times in recent years. As a result, the valley-to-peak distance of daily gas consumption in cities is extending due to a great temperature change. In this case, if the previous medium- and long-term data is adopted, a big error will occur in the tendency prediction of city gas consumption. Therefore, this paper, by use of the short-term data including air temperature and gas load, suggests a method of predicting the city gas daily load in the coming days according to the relatively accurate weather forecast within a week. This will be of great significance to reasonable sales planning and daily peak shaving for either upstream gas suppliers or city gas operators. To this end, how the air temperature affects the short- term daily load of city gas was first discussed~ then the theories like statistics and chemical kinetics were used as well as the temperature - chemical reaction rate relationship reflected from the Arrhenius law~ and finally a short-term prediction model was established for the city gas daily load taking into account many factors such as sudden change in temperature, low temperature cumulative effect, etc. In a case study performed in Chengdu, a big city in Southwest China, this model was also validated with the maximum error of 2.53M. The result shows that this model with a high accuracy of prediction will provide robust support for urban gas companies or upstream gas suppliers to make right decision in their sales planning and daily peak shaving.