考虑气象累积效应的IPSO-BP神经网络短期负荷预测算法

Short － term Load Forecasting Method Based on IPSO － BP Neural Network Considering Meteorological Cumulative Effect

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。

Abstract:

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．