基于水气用量的配电网线路负荷精准预测

Accurate Prediction of Distribution Network Load Based on Water and Gas Consumption

在电网迎峰度夏和度冬时,空调负荷急剧变化,部分输电线路出现过载、越限等情况,严重时甚至造成主变压器过载,影响电网安全运行;因此,有必要对配电网线路的空调负荷进行预测,进而实现配电网线路短期负荷的精准预测。为此,调用水务公司的用水量数据和燃气公司的用气量数据,以供电台区为分析单元,利用大数据挖掘方法分析人口迁移变化情况,采用偏最小二乘回归分析方法研究配电网线路的空调负荷与水、气用量的关系,结合ARMA(自回归滑动平均)时间序列分析法综合预测配电网线路负荷。该方法极大地提升了负荷预测的准确性,对于电网的规划改造、安全运行以及提升供电服务质量都具有重要的意义。

The abrupt change of air conditioning load in summer and winter load peak results in overload and overlimit of transmission lines, which leads to main transformer overload and poses threats to operation safety of power grid. Therefore, it is necessary to forecast the air conditioning load of the distribution network lines to accurately forecast the short-term load of the distribution network lines. Therefore, the water consumption data of the water company and the gas consumption data of the gas company are acquired to analyze population migration in each supply area via big data mining;the relationship between air conditioning load, water and gas consumption is studied by partial least squares regression and distribution network load is jointly forecasted through ARMA(auto-regressive moving average model). The method greatly improves load forecast accuracy and is of significance to grid planning and renovation, operation safety and power supply service quality improvement.