摘要
基于决策树技术,对气象因子和日电力负荷的最高、最低值、平均值进行联合建模,量化气象因子对电力负荷的影响,从而确立一种有效的基于气象因子的短期电力负荷预测方法,用以生成日特征负荷决策树预测模型。通过该模型,结合预测日的气象、属性(日期、节日等)等信息,可进行日特征负荷的预测。预测结果表明,该模型具有自动化程度高、预测结果准确率高的特性。以河北省保定市气象数据和电力负荷数据为例进行了训练和预测,研究结果证明这种方法能较大地提高日电力负荷预测的精度。
Based on the decision tree and combined with the meteorological factors and the highest, lowest and average of electrical load, a model was established to measure the influence of me- teorological factors on the load. An effective short-term forecasting method was put forward to establish the forecasting model of daily-characteristic-load decision tree. Daily characteristic load is forecasted according to the model and the date-forecasted information such as weather, attributes （date, workday or weekend）. The forecasted results show that the proposed method has high-automation and high-accuracy. The method applies the data of meteorological factors and load of Baoding in Hebei Province to train and forecast, the results show that the new method can largely improve the precision of load forecasts.
出处
《气象》
CSCD
北大核心
2008年第3期106-111,共6页
Meteorological Monthly
关键词
决策树
气象因子
负荷预测
ID3算法
decision tree
meteorological factor
load forecasting
ID3 algorithm
