The National Technical University of Athens (NTUA), one of the DEDALUS scientific partners, has completed a study on
grouping residential electricity consumers, based on their historical electricity consumption, to create more targeted demand-response programmes.
This grouping will be used in almost every DEDALUS service at the end of the day, making the services more targeted per group.
Specifically, this paper introduces a
machine learning-based framework to optimize demand response programmes. Using data from nearly 5,000 households in London, four clustering algorithms—K-means, K-medoids, Hierarchical Agglomerative Clustering, and DBSCAN—were evaluated to identify groups with similar consumption patterns.
The problem was reframed as a probabilistic classification task, leveraging Explainable AI to improve model interpretability. The optimal number of clusters was found to be seven, although two clusters, comprising around 10% of the data, exhibited high internal dissimilarity and were excluded from further consideration. This framework offers a
scalable solution for utility companies to enhance the targeting and effectiveness of demand response initiatives.
“
Our research aims to tackle a key challenge in energy management: efficiently identifying and classifying household energy consumption patterns to enhance the implementation of Demand Response programs. Optimizing household energy use is increasingly critical, both for promoting environmental sustainability and for enabling utility companies to deliver more targeted and effective DR solutions. This work aligns with the overarching objectives of the DEDALUS project, which seeks to expand residential participation in DR programs across Europe by bringing together key stakeholders and advancing smarter energy management strategies”, said Vasilis Michalakopoulos – one of the paper’s authors.
The paper, in Open Access, is available for download in the
Resources section of the DEDALUS project’s website.
(Photo Credits: iStock)
Project website:
DEDALUS HORIZON
LinkedIn:
DEDALUS
YouTube:
DEDALUS-EU