MAS-DR: An ML-Based Aggregation and Segmentation Framework for Residential Consumption Users to Assist DR Programs

Petros Tzallas (), Alexios Papaioannou (), Asimina Dimara, Napoleon Bezas, Ioannis Moschos, Christos-Nikolaos Anagnostopoulos, Stelios Krinidis, Dimosthenis Ioannidis and Dimitrios Tzovaras
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Petros Tzallas: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Alexios Papaioannou: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Asimina Dimara: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Napoleon Bezas: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Ioannis Moschos: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Christos-Nikolaos Anagnostopoulos: Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, Greece
Stelios Krinidis: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Dimosthenis Ioannidis: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Dimitrios Tzovaras: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece

Sustainability, 2025, vol. 17, issue 4, 1-33

Abstract: The increasing complexity of energy grids, driven by rising demand and unpredictable residential consumption, highlights the need for efficient demand response (DR) strategies and data-driven services. This paper proposes a machine learning-based framework for DR that clusters users based on their consumption patterns and categorizes individual usage into distinct profiles using K-means, Hierarchical Agglomerative Clustering, Spectral Clustering, and DBSCAN. Key features such as statistical, temporal, and behavioral characteristics are extracted, and the novel Household Daily Load (HDL) approach is used to identify residential consumption groups. The framework also includes context analysis to detect daily variations and peak usage periods for individual users. High-impact users, identified by anomalies such as frequent consumption spikes or grid instability risks using IsolationForest and kNN, are flagged. Additionally, a classification service integrates new users into the segmented portfolio. Experiments on real-world datasets demonstrate the framework’s effectiveness in helping energy managers design tailored DR programs.

Keywords: energy consumption clustering; customer segmentation; consumption pattern analysis; feature selection for clustering; demand response (DR) programs (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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