IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i4p1551-d1590529.html
   My bibliography  Save this article

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

Author

Listed:
  • Petros Tzallas

    (Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
    Management Science and Technology Department, Democritus University of Thrace (DUTh), 65404 Kavala, Greece)

  • Alexios Papaioannou

    (Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
    Management Science and Technology Department, Democritus University of Thrace (DUTh), 65404 Kavala, Greece)

  • Asimina Dimara

    (Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
    Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, 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
    Management Science and Technology Department, Democritus University of Thrace (DUTh), 65404 Kavala, 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)

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.

Suggested Citation

  • Petros Tzallas & Alexios Papaioannou & Asimina Dimara & Napoleon Bezas & Ioannis Moschos & Christos-Nikolaos Anagnostopoulos & Stelios Krinidis & Dimosthenis Ioannidis & Dimitrios Tzovaras, 2025. "MAS-DR: An ML-Based Aggregation and Segmentation Framework for Residential Consumption Users to Assist DR Programs," Sustainability, MDPI, vol. 17(4), pages 1-33, February.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:4:p:1551-:d:1590529
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/4/1551/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/4/1551/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yilmaz, S. & Chambers, J. & Patel, M.K., 2019. "Comparison of clustering approaches for domestic electricity load profile characterisation - Implications for demand side management," Energy, Elsevier, vol. 180(C), pages 665-677.
    2. Angelina D. Bintoudi & Napoleon Bezas & Lampros Zyglakis & Georgios Isaioglou & Christos Timplalexis & Paschalis Gkaidatzis & Athanasios Tryferidis & Dimosthenis Ioannidis & Dimitrios Tzovaras, 2021. "Incentive-Based Demand Response Framework for Residential Applications: Design and Real-Life Demonstration," Energies, MDPI, vol. 14(14), pages 1-20, July.
    3. Satre-Meloy, Aven & Diakonova, Marina & Grünewald, Philipp, 2020. "Cluster analysis and prediction of residential peak demand profiles using occupant activity data," Applied Energy, Elsevier, vol. 260(C).
    4. Motlagh, Omid & Berry, Adam & O'Neil, Lachlan, 2019. "Clustering of residential electricity customers using load time series," Applied Energy, Elsevier, vol. 237(C), pages 11-24.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shi, Zhengyu & Wu, Libo & Zhou, Yang, 2023. "Predicting household energy consumption in an aging society," Applied Energy, Elsevier, vol. 352(C).
    2. Valdes, Javier & Masip Macia, Yunesky & Dorner, Wolfgang & Ramirez Camargo, Luis, 2021. "Unsupervised grouping of industrial electricity demand profiles: Synthetic profiles for demand-side management applications," Energy, Elsevier, vol. 215(PA).
    3. Wen, Hanguan & Liu, Xiufeng & Yang, Ming & Lei, Bo & Cheng, Xu & Chen, Zhe, 2023. "An energy demand-side management and net metering decision framework," Energy, Elsevier, vol. 271(C).
    4. Tanoto, Yusak & Haghdadi, Navid & Bruce, Anna & MacGill, Iain, 2020. "Clustering based assessment of cost, security and environmental tradeoffs with possible future electricity generation portfolios," Applied Energy, Elsevier, vol. 270(C).
    5. Mishra, Kakuli & Basu, Srinka & Maulik, Ujjwal, 2022. "Load profile mining using directed weighted graphs with application towards demand response management," Applied Energy, Elsevier, vol. 311(C).
    6. Michalakopoulos, Vasilis & Sarmas, Elissaios & Papias, Ioannis & Skaloumpakas, Panagiotis & Marinakis, Vangelis & Doukas, Haris, 2024. "A machine learning-based framework for clustering residential electricity load profiles to enhance demand response programs," Applied Energy, Elsevier, vol. 361(C).
    7. Jieyi Kang & David Reiner, 2021. "Identifying residential consumption patterns using data-mining techniques: A large-scale study of smart meter data in Chengdu, China," Working Papers EPRG2114, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    8. Ahir, Rajesh K. & Chakraborty, Basab, 2021. "A meta-analytic approach for determining the success factors for energy conservation," Energy, Elsevier, vol. 230(C).
    9. Li, Wenqiang & Gong, Guangcai & Fan, Houhua & Peng, Pei & Chun, Liang & Fang, Xi, 2021. "A clustering-based approach for “cross-scale” load prediction on building level in HVAC systems," Applied Energy, Elsevier, vol. 282(PB).
    10. Ahir, Rajesh K. & Chakraborty, Basab, 2023. "A data-driven analytic approach for investigation of electricity demand variability for energy conservation programs," Energy, Elsevier, vol. 282(C).
    11. Dabirian, Sanam & Miller, Clayton & Adli, Alireza & Eicker, Ursula, 2024. "Gaussian-based plug load profile prediction in non-residential buildings archetype," Applied Energy, Elsevier, vol. 374(C).
    12. Ruhang, Xu, 2020. "Efficient clustering for aggregate loads: An unsupervised pretraining based method," Energy, Elsevier, vol. 210(C).
    13. Wen, Hanguan & Liu, Xiufeng & Yang, Ming & Lei, Bo & Xu, Cheng & Chen, Zhe, 2024. "A novel approach for identifying customer groups for personalized demand-side management services using household socio-demographic data," Energy, Elsevier, vol. 286(C).
    14. Gianluca Trotta & Kirsten Gram-Hanssen & Pernille Lykke Jørgensen, 2020. "Heterogeneity of Electricity Consumption Patterns in Vulnerable Households," Energies, MDPI, vol. 13(18), pages 1-17, September.
    15. Do-Hyeon Ryu & Ryu-Hee Kim & Seung-Hyun Choi & Kwang-Jae Kim & Young Myoung Ko & Young-Jin Kim & Minseok Song & Dong Gu Choi, 2020. "Utilizing Electricity Consumption Data to Assess the Noise Discomfort Caused by Electrical Appliances between Neighbors: A Case Study of a Campus Apartment Building," Sustainability, MDPI, vol. 12(20), pages 1-16, October.
    16. Hyun Cheol Jeong & Jaesung Jung & Byung O Kang, 2020. "Development of Operational Strategies of Energy Storage System Using Classification of Customer Load Profiles under Time-of-Use Tariffs in South Korea," Energies, MDPI, vol. 13(7), pages 1-17, April.
    17. Li, Lanlan & Ming, Huayang & Fu, Weizhong & Shi, Quan & Yu, Shiwei, 2021. "Exploring household natural gas consumption patterns and their influencing factors: An integrated clustering and econometric method," Energy, Elsevier, vol. 224(C).
    18. Yamaguchi, Yohei & Shoda, Yuto & Yoshizawa, Shinya & Imai, Tatsuya & Perwez, Usama & Shimoda, Yoshiyuki & Hayashi, Yasuhiro, 2023. "Feasibility assessment of net zero-energy transformation of building stock using integrated synthetic population, building stock, and power distribution network framework," Applied Energy, Elsevier, vol. 333(C).
    19. Pullinger, Martin & Zapata-Webborn, Ellen & Kilgour, Jonathan & Elam, Simon & Few, Jessica & Goddard, Nigel & Hanmer, Clare & McKenna, Eoghan & Oreszczyn, Tadj & Webb, Lynda, 2024. "Capturing variation in daily energy demand profiles over time with cluster analysis in British homes (September 2019 – August 2022)," Applied Energy, Elsevier, vol. 360(C).
    20. Hua Chen & Shuang Dai & Fanlin Meng, 2023. "Smart Building Thermal Management: A Data-Driven Approach Based on Dynamic and Consensus Clustering," Sustainability, MDPI, vol. 15(21), pages 1-25, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:4:p:1551-:d:1590529. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.