基于一維卷積神經(jīng)網(wǎng)絡(luò)的家庭用戶特征識別方法

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中圖分類號：TB9;TM933 文獻標志碼：A 文章編號：1674-5124（2025）06-0025-06

Household characteristics identification method based on one-dimensional convolution neural network

XU Jihe1，ZHU Liang2，YAN Yi2，ZHOUJianan3，WENHe3 （1. Pingxiang Power Suply Company， State Grid Jiangxi Power Company，Pingxiang 330ooo， China; 2.Power Supply Service Management Center，State Grid Jiangxi Power Company，Nanchang 33o077，China; 3.College ofElectrical and Information Engineering， Hunan University， Changsha 41oooo， China）

Abstract： Users’electricity consumption datasets provided by smart energy meters can reflect the users ’ electricity consumption characteristics，which provides a basis for analyzing the household characteristics. Aimingat the effcient classification of household characteristics，this paper studies a household characteristics identification method based on one-dimensional smart energy meter electricity consumption data. In this paper， a one-dimensional convolution neural network suitable for the time series data of smart electric energy meter is designed. Taking the user's electric energy consumption data （one-dimensional time series） measured by the smart electric energy meter as the input， and the pooling layer is removed after the first two convolution operations of the network to achieve the preservation of early features and to achieve accurate classification of the household characteristics.In order to prove the effectiveness of the method proposed in this paper，this paper conducts comparative experiments on public datasets.The experiments show that our method achieves 55%～78% accuracy in the classification of several the household characteristics.

Keywords： deep leaming; one-dimensional convolution neural network; classification; household characteristics; smart energy meters

0 引言

家庭用戶特征，包括用戶的年齡、薪資、房屋狀況、社會關(guān)系等，可以幫助零售商了解不同用戶的生活習慣和用電模式，有助于公用事業(yè)和零售商實施更有效的需求響應(yīng)方案和更個性化的服務(wù)，并就需求響應(yīng)和能源效率計劃的目標做出更可靠的決策。（剩余9656字）