Researchers from the National Technical University of Athens (NTUA) have developed a cutting-edge machine learning framework to optimize demand response programs for residential electricity consumers. This innovative approach promises to enhance the targeting and effectiveness of utility companies’ efforts to promote sustainable energy use. By clustering households based on their electricity consumption patterns, this framework enables more personalized demand response initiatives that can drive significant environmental and economic benefits. Demand response is a critical strategy for managing energy grids and promoting renewable energy integration.
Releasing the Power of Clustering
A research group at NTUA has moved the ball dramatically forward in residential electricity clustering by using machine learning to design a demand response framework that is more selective and efficient. The researchers used data from almost 5,000 households in London to test four clustering algorithms (K-means, K-medoids, Hierarchical Agglomerative Clustering and DBSCAN) aimed at finding groups of consumers with similar behaviour.
The main innovation comes from casting the problem as a probabilistic classification task, which makes it possible to use Explainable AI (XAI) methods on the models. This method allows utility companies to have a clearer picture on what is behind each cluster of consumers, so they can create demand response programs that reflect the specificities of a target group.
How to get out the most of demand response?
Seven clusters were found to be the best fit; two other clusters, of about 10% of the data and with poor internal dissimilarity, were discarded. The segmentation therefore enables utility companies to direct demand response initiatives at these key customer segments, providing them with the greatest opportunity to engage and ensuring that their efforts have the highest possible chance of success.
This framework allows utility companies to increase the targeting and overall effectiveness of demand response programs in a scalable manner. Vasilis Michalakopoulos, one of the co-authors of the paper said: “Our research aims at addressing a fundamental problem in energy management; how to effectively identify and categorise household load patterns to aid Demand Response strategies implementation.” As the need for environmentally-friendly solutions continues to rise, and as utility companies must develop more sophisticated DR programs in order to remain viable players, the optimization of energy expenditures used at home becomes all the more important.
Compliance with DEDALUS Project Requirements
In a related direction to the DEDALUS project objectives this work opens new horizons for residential demand response in Europe. The DEDALUS project, through identification of the key actors and in promotion of smart energy management objectives, aspires to mobilize consumers and foster the transition to a more sustainable energy perspective.
“This work complements the overall ambitions of the project DEDALUS which aims at boosting residential participation into DR across Europe by bringing together key stakeholders and on purposefully designed energy management strategies,” said Michalakopoulos. The application of this machine learning-oriented platform within DEDALUS services ensures that demand response strategies will be more accurately directed, boosting their performance and hence advancing a sustainable and resilient energy system.
