IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v308y2022ics0306261921015853.html
   My bibliography  Save this article

Electricity consumption pattern analysis beyond traditional clustering methods: A novel self-adapting semi-supervised clustering method and application case study

Author

Listed:
  • Zhang, Xiaohai
  • Ramírez-Mendiola, José Luis
  • Li, Mingtao
  • Guo, Liejin

Abstract

The fast-paced informatization of power systems across the world provides an unprecedented amount of data, which greatly facilitates their study and offers in turn the possibility to assist in the transition towards truly smart, low-carbon energy systems. In this context, the use of clustering methods for the study of household Electricity Consumption Behaviour (ECB) proves highly beneficial as it facilitates, among other things, more effective deployment of distributed renewable energy assets, development of differentiated tariff policies and load forecasting. However, the similarity metrics used in traditional clustering methods have difficulties in accurately capturing the time variability of electrical load profiles. In order to address this problem, we developed a novel semi-supervised automatic clustering method based on a self-adapting metric learning process. The proposed method is a bespoke application to the analysis of electricity demand load patterns that combines the recently developed Deep Linear Discriminant Analysis algorithm for supervised learning with the data-adaptive Affinity Propagation clustering algorithm (DLDA + AP), and achieves high-quality automatic clustering with an accuracy that is 75 percentage points higher than traditional methods such as k-means, on average. Based on this bespoke method, a unified load dictionary which captures the mainstream daily electricity consumption patterns of 5566 households in London was produced. Through the analysis of the load dictionary and household daily electricity consumption, it’s possible to build a complete ECB profile for the households in the sample dataset. Furthermore, combining the 206 household properties which were found to be strongly correlated with the ECB, this method provides a practical approach to residential customer segmentation for the electricity market.

Suggested Citation

  • Zhang, Xiaohai & Ramírez-Mendiola, José Luis & Li, Mingtao & Guo, Liejin, 2022. "Electricity consumption pattern analysis beyond traditional clustering methods: A novel self-adapting semi-supervised clustering method and application case study," Applied Energy, Elsevier, vol. 308(C).
  • Handle: RePEc:eee:appene:v:308:y:2022:i:c:s0306261921015853
    DOI: 10.1016/j.apenergy.2021.118335
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921015853
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2021.118335?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    2. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    3. Vázquez-Canteli, José R. & Nagy, Zoltán, 2019. "Reinforcement learning for demand response: A review of algorithms and modeling techniques," Applied Energy, Elsevier, vol. 235(C), pages 1072-1089.
    4. Ramírez-Mendiola, José Luis & Grünewald, Philipp & Eyre, Nick, 2019. "Residential activity pattern modelling through stochastic chains of variable memory length," Applied Energy, Elsevier, vol. 237(C), pages 417-430.
    5. Dawoud, Samir M. & Lin, Xiangning & Okba, Merfat I., 2018. "Hybrid renewable microgrid optimization techniques: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2039-2052.
    6. Salcedo-Sanz, S. & Cornejo-Bueno, L. & Prieto, L. & Paredes, D. & García-Herrera, R., 2018. "Feature selection in machine learning prediction systems for renewable energy applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 728-741.
    7. Markovič, Rene & Gosak, Marko & Grubelnik, Vladimir & Marhl, Marko & Virtič, Peter, 2019. "Data-driven classification of residential energy consumption patterns by means of functional connectivity networks," Applied Energy, Elsevier, vol. 242(C), pages 506-515.
    8. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    9. Perera, A.T.D. & Wickramasinghe, P.U. & Nik, Vahid M. & Scartezzini, Jean-Louis, 2019. "Machine learning methods to assist energy system optimization," Applied Energy, Elsevier, vol. 243(C), pages 191-205.
    10. Félix Iglesias & Wolfgang Kastner, 2013. "Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns," Energies, MDPI, vol. 6(2), pages 1-19, January.
    11. 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).
    12. Behl, Madhur & Smarra, Francesco & Mangharam, Rahul, 2016. "DR-Advisor: A data-driven demand response recommender system," Applied Energy, Elsevier, vol. 170(C), pages 30-46.
    13. Chitalia, Gopal & Pipattanasomporn, Manisa & Garg, Vishal & Rahman, Saifur, 2020. "Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 278(C).
    14. Chicco, Gianfranco, 2012. "Overview and performance assessment of the clustering methods for electrical load pattern grouping," Energy, Elsevier, vol. 42(1), pages 68-80.
    15. Guo, Zhifeng & Zhou, Kaile & Zhang, Chi & Lu, Xinhui & Chen, Wen & Yang, Shanlin, 2018. "Residential electricity consumption behavior: Influencing factors, related theories and intervention strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 399-412.
    16. Rhodes, Joshua D. & Cole, Wesley J. & Upshaw, Charles R. & Edgar, Thomas F. & Webber, Michael E., 2014. "Clustering analysis of residential electricity demand profiles," Applied Energy, Elsevier, vol. 135(C), pages 461-471.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Yi & Ma, Jiahao & Gao, Ning & Wen, Qingsong & Sun, Liang & Guo, Hongye, 2023. "Federated fuzzy k-means for privacy-preserving behavior analysis in smart grids," Applied Energy, Elsevier, vol. 331(C).
    2. Zhou, Chenyu & Shen, Yun & Wu, Haixin & Wang, Jianhong, 2022. "Using fractional discrete Verhulst model to forecast Fujian's electricity consumption in China," Energy, Elsevier, vol. 255(C).
    3. Xie, Yutao & Xiao, Jiang-Wen & Wang, Yan-Wu & Dong, Jiale, 2024. "A new customer selection framework for time-based pricing program," Energy, Elsevier, vol. 290(C).

    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. 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).
    2. 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).
    3. 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).
    4. Rongheng Lin & Budan Wu & Yun Su, 2018. "An Adaptive Weighted Pearson Similarity Measurement Method for Load Curve Clustering," Energies, MDPI, vol. 11(9), pages 1-17, September.
    5. 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.
    6. Russo, Marianna & Bertsch, Valentin, 2020. "A looming revolution: Implications of self-generation for the risk exposure of retailers," Energy Economics, Elsevier, vol. 92(C).
    7. Tang, Rui & Yildiz, Baran & Leong, Philip H.W. & Vassallo, Anthony & Dore, Jonathon, 2019. "Residential battery sizing model using net meter energy data clustering," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    8. Pfenninger, Stefan, 2017. "Dealing with multiple decades of hourly wind and PV time series in energy models: A comparison of methods to reduce time resolution and the planning implications of inter-annual variability," Applied Energy, Elsevier, vol. 197(C), pages 1-13.
    9. Gouveia, João Pedro & Seixas, Júlia & Mestre, Ana, 2017. "Daily electricity consumption profiles from smart meters - Proxies of behavior for space heating and cooling," Energy, Elsevier, vol. 141(C), pages 108-122.
    10. Kathirgamanathan, Anjukan & De Rosa, Mattia & Mangina, Eleni & Finn, Donal P., 2021. "Data-driven predictive control for unlocking building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    11. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    12. Cansino, José M. & Dugo, Víctor & Gálvez-Ruiz, David & Román-Collado, Rocío, 2023. "What drove electricity consumption in the residential sector during the SARS-CoV-2 confinement? A special focus on university students in southern Spain," Energy, Elsevier, vol. 262(PB).
    13. Zhou, Kaile & Yang, Changhui & Shen, Jianxin, 2017. "Discovering residential electricity consumption patterns through smart-meter data mining: A case study from China," Utilities Policy, Elsevier, vol. 44(C), pages 73-84.
    14. Capozzoli, Alfonso & Piscitelli, Marco Savino & Brandi, Silvio & Grassi, Daniele & Chicco, Gianfranco, 2018. "Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings," Energy, Elsevier, vol. 157(C), pages 336-352.
    15. Robbert Claeys & Hakim Azaioud & Rémy Cleenwerck & Jos Knockaert & Jan Desmet, 2020. "A Novel Feature Set for Low-Voltage Consumers, Based on the Temporal Dependence of Consumption and Peak Demands," Energies, MDPI, vol. 14(1), pages 1-24, December.
    16. 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).
    17. Khan, Waqas & Liao, Juo Yu & Walker, Shalika & Zeiler, Wim, 2022. "Impact assessment of varied data granularities from commercial buildings on exploration and learning mechanism," Applied Energy, Elsevier, vol. 319(C).
    18. 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.
    19. Rajabi, Amin & Eskandari, Mohsen & Ghadi, Mojtaba Jabbari & Li, Li & Zhang, Jiangfeng & Siano, Pierluigi, 2020. "A comparative study of clustering techniques for electrical load pattern segmentation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    20. Li, Kehua & Yang, Rebecca Jing & Robinson, Duane & Ma, Jun & Ma, Zhenjun, 2019. "An agglomerative hierarchical clustering-based strategy using Shared Nearest Neighbours and multiple dissimilarity measures to identify typical daily electricity usage profiles of university library b," Energy, Elsevier, vol. 174(C), pages 735-748.

    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:eee:appene:v:308:y:2022:i:c:s0306261921015853. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.