Clustering Appliance Operation Modes With Unsupervised Deep Learning Techniques

In smart grids, consumers can be involved in demand response programs to reduce the total power consumption of their households during the peak hours of the day. Unfortunately, nowadays, utility companies are facing important challenges in the implementation of demand response programs because of th...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2023-07, Vol.19 (7), p.8196-8204
Main Authors: Castangia, Marco, Barletta, Nicola, Camarda, Christian, Quer, Stefano, Macii, Enrico, Patti, Edoardo
Format: Article
Language:English
Subjects:
Algorithms
Appliance operation modes
appliance program
autoencoder
Cluster analysis
Clustering
Clustering algorithms
Deep learning
Demand side management
Energy consumption
Energy management
Feature extraction
Household appliances
Households
Machine learning
Neural networks
Power consumption
Representations
Signatures
Smart grid
smart grids
Task analysis
Washing machines
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Summary:In smart grids, consumers can be involved in demand response programs to reduce the total power consumption of their households during the peak hours of the day. Unfortunately, nowadays, utility companies are facing important challenges in the implementation of demand response programs because of their negative impact on the comfort of end-users. In this article, we cluster the different operation modes of household appliances based on the analysis of their power signatures. For this purpose, we implement an autoencoder neural network to create a better data representation of the power signatures. Then, we cluster the different operational programs by using a K -means algorithm fitted to the new data representation. To test our methodology, we study the operation modes of some washing machines and dishwashers whose power signatures were derived from both submeters and nonintrusive load monitoring techniques. Our clustering analysis reveals the existence of multiple working programs showing well-defined features in terms of both average energy consumption and duration. Our results can then be used to improve demand response programs by reducing their impact on the comfort of end-users. Furthermore, end-users can rely on our framework to favor lighter operation modes and reduce their overall energy consumption.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2022.3217495