基于时频特征的电力用户用电分析与研究  

Analysis and Research on Electricity Usage of Electricity Customers Based on Time-Frequency Characteristics

作　　者：刘俊 刘卓 LIU Jun;LIU Zhuo(Luohu Power Supply Bureau,Shenzhen Power Supply Bureau Co.,Ltd.,Shenzhen 518000,China)

机构地区：[1]深圳供电局有限公司罗湖供电局,广东深圳518000

出　　处：《自动化仪表》2024年第11期85-90,共6页Process Automation Instrumentation

摘　　要：针对目前电力数据维度高、特征复杂、难以进行有效分析等问题,提出了基于时频域特征提取的家庭用电特征识别混合模型。首先,为了减少信息冗余和高维数据带来的维度爆炸或噪声干扰影响,基于小波变换和随机森林(RF)算法提取用户家庭用电时频域特征。其次,将特征代入分类预测模型进行训练,并通过动态改变粒子群优化(PSO)算法中惯性因子确定反向传播神经网络(BPNN)中的权值,从而提高网络训练的性能。仿真阶段以某电力公司提供的数据为基础,对电力用户家庭用电特征进行分类分析。分析结果表明,经时频域特征提取后,所提模型平均准确率为0.7968,与时域特征和频域特征提取方法相比分别提高5.56%和8.87%。此外,所提模型训练性能较传统PSO算法和无优化模型分别提升4.75倍和2.58倍。所提模型为电力用户家庭用电特征分析提供了借鉴。Aiming at the current problems of high dimensionality,complex features,and difficulty in effective analysis of electric power data,a hybrid model of household electricity usage feature recognition based on time-frequency characteristics extraction is proposed.Firstly,to reduce the impact of the information redundancy and dimensional explosion or noise interference caused by high-dimensional data,time-frequency characteristics of users’household electricity usage are extracted based on wavelet transform and random forest(RF)algorithm.Secondly,the features are substituted into the classification prediction model for training,and the weights in the back propagation neural network(BPNN)are determined by dynamically changing the inertia factor in the particle swarm optimization(PSO)algorithm,to improve the performance of network training.The simulation stage is based on the data provided by an electric power company to classify and analyze the characteristics of electricity users’household electricity usage.The analysis results show that after the time-frequency characteristics extraction,the proposed model has an average accuracy of 0.7968,which is 5.56%and 8.87%higher than the time-domain feature and frequency-domain feature extraction methods,respectively.In addition,the training performance of the proposed model is improved by 4.75 times and 2.58 times compared with the traditional PSO algorithm and the unoptimized model,respectively.The proposed model provides a reference for the characterization analysis of household electricity usage of power customers.

关 键 词：电力数据 智能电表 用户特征 时频域分析 小波变换 随机森林 反向传播神经网络 粒子群优化算法 

分 类 号：TH-39[机械工程]