基于电力数据与机器学习的家庭收入估计方法

Household Income Estimation Method Based on Power Data and Machine Learning

摘要:

合理利用从智能电表获取的家庭用电量数据，就可以推断出一个家庭的收入情况，从而有利于商家对消费群 体的把控，使商家更有针对性地为用户提供需要的服务和产品。为了提高推断的准确率，提出了一种利用家庭总用 电量和房屋面积信息的数据融合方法来估计家庭收入情况。研究运用几种不同的机器学习分类算法对数据进行训 练和分析，最终使得对家庭年收入的分类准确率可以达到 81% ，相比于只利用家庭总用电量的信息，分类准确率提高 了 15% 。可见，增加房屋面积信息的方法能够达到一定的评估目的，为商家和用户提供帮助，使人们享受更加智能和 优质的生活。

Abstract:

Reasonable use of household electricity consumption data obtained from smart meter can infer household income， which is conducive to the control of consumer groups，so that businesses can provide more targeted services and products for users． In order to improve the accuracy of inference，a data fusion method is proposed to estimate household income based on the information of total household electricity consumption and housing area． Several different machine learning classification algorithms are used to train and analyze the data． Finally，the classification accuracy of annual household income can reach 81% ． Compared to using the information of total household electricity consumption only，the classification accuracy is improved by 15% ． It can be seen that the method of increasing housing area information can achieve certain evaluation purposes， provide help for businesses and users，and enable people to enjoy a more intelligent and quality life．