Comparative Analysis of Electricity Consumption Patterns Across Four K-Means Clusters of Small and Medium Enterprises
By: A. Laizans, A. Backurs, L. Jansons and N. Roldugins
References
- Granell, R., Axon, C. J., & Wallom, D. C. H. (2015). Impacts of raw data temporal resolution using selected clustering methods on residential electricity load profiles. IEEE Transactions on Power Systems, 30(6), 3217–3224. https://doi.org/10.1109/TPWRS.2014.2377213
- Tisenkopfs, M., Jansons, L., Geipele, I., Lapuke, S., & Backurs, A. (2025). Optimization of Electricity Consumption-Associated Costs in a Medium-Sized Logistics Company. Energies, 18(12), 3206. https://doi.org/10.3390/en18123206
- Flath, C., Nicolay, D., Conte, T., Van Dinther, C., & Filipova-Neumann, L. (2012). Cluster analysis of smart metering data. Business & Information Systems Engineering, 4(1), 31–39. https://doi.org/10.1007/s12599-011-0201-5
- Kwac, J., Flora, J., & Rajagopal, R. (2014). Household energy consumption segmentation using hourly data. IEEE Transactions on Smart Grid, 5(1), 420–430. https://doi.org/10.1109/TSG.2013.2278477
- Figueiredo, V., Rodrigues, F., Vale, Z., & Gouveia, J. B. (2005). An electric energy consumer characterization framework based on data mining techniques. IEEE Transactions on Power Systems, 20(2), 596–602. https://doi.org/10.1109/TPWRS.2005.846234
- McLoughlin, F., Duffy, A., & Conlon, M. (2015). A clustering approach to domestic electricity load profile characterisation using smart metering data. Applied Energy, 141, 190–199. https://doi.org/10.1016/j.apenergy.2014.12.039
- Räsänen, T., Voukantsis, D., Niska, H., Karatzas, K., & Kolehmainen, M. (2010). Data-based method for creating electricity use load profiles using large amount of customer data. International Journal of Electrical Power & Energy Systems, 32(5), 445–452. https://doi.org/10.1016/j.ijepes.2009.11.013
- Chicco, G., & Mazza, A. (2020). Metaheuristic optimization of power and energy systems: Underlying principles and main issues of the ‘rush to heuristics’. Energies, 13(19), 5097. https://doi.org/10.3390/en13195097
- Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. https://doi.org/10.1016/0377-0427(87)90125-7
- Haben, S., Singleton, C., & Grindrod, P. (2016). Analysis and clustering of residential customers energy behavioral demand using smart meter data. IEEE Transactions on Smart Grid, 7(1), 136–144. https://doi.org/10.1109/TSG.2015.2409786
- Panapakidis, I. P., Papadopoulos, T. A., Christoforidis, G. C., & Papagiannis, G. K. (2014). Pattern recognition algorithms for electricity load curve analysis of buildings. Energy and Buildings, 73, 137–145. https://doi.org/10.1016/j.enbuild.2014.01.002
- Lusis, P., Khalilpour, K. R., Andrew, L., & Liebman, A. (2017). Short-term residential load forecasting: Impact of calendar effects and forecast granularity. Applied Energy, 205, 654–669. https://doi.org/10.1016/j.apenergy.2017.07.114
- Widén, J., & Wäckelgård, E. (2010). A high-resolution stochastic model of domestic activity patterns and electricity demand. Applied Energy, 87(6), 1880–1892. https://doi.org/10.1016/j.apenergy.2009.11.006
- Deakin, M., Bloomfield, H., Greenwood, D., Shchetinin, O., & Strbac, G. (2021). Clustering-based characterisation of smart meter data for GB small businesses and other non-domestic consumers. Energy, 234, 121240. https://doiorg/10.1016/j.energy.2021.121240
- Strbac, G. (2008). Demand side management: Benefits and challenges. Energy Policy, 36(12), 4419–4426. https://doi.org/10.1016/j.enpol.2008.09.030
- Chicco, G., Napoli, R., & Piglione, F. (2006). Comparisons among clustering techniques for electricity customer classification. IEEE Transactions on Power Systems, 21(2), 933–940. https://doi.org/10.1109/TPWRS.2006.873122
- Brodén, D., Henningsson, M., & Söder, L. (2022). Load profile characteristics and electricity demand flexibility in the hospitality sector. Energy and Buildings, 260, 111924. https://doi.org/10.1016/j.enbuild.2022.111924
- Yildiz, B., Bilbao, J. I., & Sproul, A. B. (2017). A review and analysis of regression and machine learning models on commercial building electricity load forecasting. Renewable and Sustainable Energy Reviews, 73, 1104–1122. https://doi.org/10.1016/j.rser.2017.02.023
- Pflugradt, N., & Muntwyler, U. (2016). Synthesizing residential load profiles using behavior simulation. Energy Procedia, 122, 655–660. https://doi.org/10.1016/j.egypro.2017.07.365
- Teeraratkul, T., O’Neill, D., & Lall, S. (2018). Shape-based approach to household electric load curve clustering and prediction. IEEE Transactions on Smart Grid, 9(5), 5196–5206. https://doi.org/10.1109/TSG.2017.2683461
- Zhong, S., & Tam, K. S. (2015). Hierarchical classification of load profiles based on their characteristic attributes in frequency domain. IEEE Transactions on Power Systems, 30(5), 2434–2441. https://doi.org/10.1109/TPWRS.2014.2363487
- Wang, Y., Chen, Q., Sun, M., Kang, C., & Xia, Q. (2018). An ensemble forecasting method for the aggregated load with subprofiles. IEEE Transactions on Smart Grid, 9(4), 3906–3908. https://doi.org/10.1109/TSG.2018.2807985
- McLoughlin, F., Duffy, A., & Conlon, M. (2013). Characterising domestic electricity consumption patterns by dwelling and occupant socio-economic variables: An Irish case study. Energy and Buildings, 48, 240–248. https://doi.org/10.1016/j.enbuild.2012.01.037
- Charrad, M., Ghazzali, N., Boiteau, V., & Niknafs, A. (2014). NbClust: An R package for determining the relevant number of clusters in a data set. Journal of Statistical Software, 61(6), 1–36. https://doi.org/10.18637/jss.v061.i0
- Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236–244. https://doi.org/10.1080/01621459.1963.10500845
- Faruqui, A., & Sergici, S. (2010). Household response to dynamic pricing of electricity: A survey of 15 experiments. Journal of Regulatory Economics, 38(2), 193–225. https://doi.org/10.1007/s11149-010-9127-y
- Hledik, R. (2009). How green is the smart grid? The Electricity Journal, 22(3), 29–41. https://doi.org/10.1016/j.tej.2009.02.016
- Lund, H. (2010). Renewable energy systems: The choice and modeling of 100% renewable solutions. Academic Press.
- Fetz, A., & Filippini, M. (2010). Economies of vertical integration in the Swiss electricity sector. Energy Economics, 32(6), 1325–1330. https://doi.org/10.1016/j.eneco.2010.04.009
- European Commission. (2019). Clean energy for all Europeans package – Electricity market directive (2019/944/EU). Official Journal of the European Union.
- Chen, K., Chen, K., Wang, Q., He, Z., Hu, J., & He, J. (2018). Short-term load forecasting with deep residual networks. IEEE Transactions on Smart Grid, 10(4), 3943–3952. https://doi.org/10.1109/TSG.2018.2844307
- Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., … & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154.
- Amasyali, K., & El-Gohary, N. M. (2018). A review of data-driven building energy consumption prediction studies. Renewable and Sustainable Energy Reviews, 81, 1192–1205. https://doi.org/10.1016/j.rser.2017.04.095
- Haben, S., Arora, S., Giasemidis, G., Voss, M., & Vukadinovic Greetham, D. (2021). Review of low voltage load forecasting: Methods, applications, and recommendations. Applied Energy, 304, 117798. https://doi.org/10.1016/j.apenergy.2021.117798
Language: English
Page range: 32 - 45
Published on: May 27, 2026
Published by: Institute of Physical Energetics
In partnership with: Paradigm Publishing Services
Publication frequency: 6 issues per year
Keywords:
Related subjects:
© 2026 A. Laizans, A. Backurs, L. Jansons, N. Roldugins, published by Institute of Physical Energetics
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.