摘要
气象因素是短期负荷预测中的重要因素,考虑气象累积效应选取相似日作为训练样本,提出基于改进粒子群优化算法的BP神经网络负荷预测方法(IPSO-BP)。首先通过相关性分析得出与日负荷相关程度较大的气象因素;在此基础上,采用加权几何距离选取与待预测日关联度较大的历史日作为相似日,并对IPSO-BP神经网络模型进行训练和预测。实际应用结果表明,所提出的预测模型和数据处理方法能够得到更加精确的预测结果。
Meteorological factors are important in short-term load forecasting.Considering the cumulative effect of meteorology,similar days are selected as training samples,a BP neural network load forecasting method based on improved particle swarm optimization algorithm(IPSO-BP)is proposed.Firstly,the meteorological factors more relevant to daily loads are determined by means of correlation analysis.On this basis,the weighted geometric distance is used to select the historical days which have a greater correlation with the predicted day as the similar day,and the IPSO-BP neural network model is trained and used in short-term load forecasting.The practical application results show that the proposed prediction model and data processing method can achieve more accurate prediction results.
作者
张宜忠
杨旭东
张正卫
刘丽新
Yizhong;Yang Xudong;Zhang Zhengwei;Liu Lixin(State Grid Sichuan Ya′an Electric Power (Group) Co.,Ltd.,Ya′an 625000,Sichuan,China;Beijing Tsingsoft Technology Co.,Ltd.,Beijing 100085,China)
出处
《四川电力技术》
2019年第3期1-5,共5页
Sichuan Electric Power Technology
基金
国家自然科学基金资助项目(51777196)
关键词
短期负荷预测
气象累积效应
相似日选取
改进粒子群优化算法
BP神经网络
short-term load forecasting
meteorological cumulative effect
similar day selection
improved particle swarm optimization algorithm
BP neural network
