Residential Electricity Behavior Classification Model Based on Sparse Denoising Autoencoder And K-Means

User electricity data contains the characteristics of residential users' electricity consumption behavior. In order to help power companies formulate demand response plans and time of use electricity prices, and better extract electricity consumption behavior characteristics, this paper propose...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2023 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC) S. 506 - 510
Hauptverfasser: Yao, Zhengnan, Wei, Feishen, Huang, Yifan
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 29.12.2023
Schlagworte:
Autoencoder
Dimensionality reduction
Electricity
Electricity consumption behavior classification
Feature extraction
Internet of Things
Load clustering
Noise reduction
Stochastic processes
Time factors
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:User electricity data contains the characteristics of residential users' electricity consumption behavior. In order to help power companies formulate demand response plans and time of use electricity prices, and better extract electricity consumption behavior characteristics, this paper proposes an electricity consumption behavior classification model based on sparse denoising autoencoder (SDAE) feature dimensionality reduction and K-means clustering. Firstly, sparse denoising autoencoder is used to learn features, and K-means clustering is used for classification. Visualize the classification results using the t-distributed stochastic neighbor embedding (t-SNE) method, calculate typical user curves using Gaussian distance weighting, and analyze the characteristics of the electricity consumption curve. The effectiveness of the proposed method was verified by calculating and comparing the clustering indicators of other common dimensionality reduction methods.
DOI:10.1109/ICIRDC62824.2023.00098