基于BP神经网络的用户侧用电负荷自适应预测方法  

Adaptive prediction method for user side electricity load based on BP neural network

作　　者：张传远 陈亚天 高振伟 齐永忠 杨夏祎 ZHANG Chuan-yuan;CHEN Ya-tian;GAO Zhen-wei;QI Yong-zhong;YANG Xia-yi(Beijing Guodiantong Networks Technology Co.,Ltd.,Beijing 100192,China)

机构地区：[1]北京国电通网络技术有限公司,北京100192

出　　处：《信息技术》2025年第2期187-192,共6页Information Technology

摘　　要：为了提高电力系统运行的可靠性和稳定性,准确预测用户侧用电负荷,提出了基于BP神经网络的用户侧用电负荷自适应预测方法。通过构建用户侧用电负荷数据分析模型,采集用户侧用电负荷数据并进行数据分类和分析。使用线性内插方法,对用户侧用电负荷残缺数据和误差数据进行修补。基于BP神经网络,采用粒子群算法对BP神经网络的初始权重和门限进行优化,实现用户侧用电负荷自适应预测。实验结果表明,文中方法的负荷预测结果更加接近于实际值,能够准确预测用户侧用电负荷。In order to improve the reliability and stability of the power system and accurately predict the user side electricity load,a user side electricity load adaptive prediction method based on BP neural network is proposed.By constructing a user side electricity load data analysis model,the user side electricity load data is collected and data classification and analysis is perfomed.Linear interpolation method is used to repair the incomplete data and error data of user side electricity load.Based on the BP neural network,particle swarm optimization algorithm is used to optimize the initial weights and thresholds of the BP neural network,achieving adaptive prediction of user side electricity load.The experiment results show that the load prediction results of this method are closer to the actual values and can accurately predict the electricity load on the user side.

关 键 词：BP神经网络 用户侧 用电负荷 自适应预测 粒子群算法 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]