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 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.

Automated summary

In smart grids, consumers can participate in demand response programs to reduce household power consumption during peak hours. However, utility companies face challenges in implementing these programs due to their negative impact on user comfort. This article uses neural networks and clustering algorithms to analyze power signatures of appliances and identify different operational modes. By studying washing machines and dishwashers, the analysis reveals distinct working programs based on energy consumption and duration, providing insights for improving demand response programs and reducing overall energy usage by promoting lighter operation modes.