Clustering of residential electricity customers using load time series

Citations

Cited by:

1. Zhang, Chao & Lasaulce, Samson & Hennebel, Martin & Saludjian, Lucas & Panciatici, Patrick & Poor, H. Vincent, 2021. "Decision-making oriented clustering: Application to pricing and power consumption scheduling," Applied Energy, Elsevier, vol. 297(C).
2. Gbeminiyi John Oyewole & George Alex Thopil, 2025. "Industrial Electricity Pricing and Renewable Energy: A Temporal Analysis of the Effect of Taxes," Energies, MDPI, vol. 18(8), pages 1-25, April.
3. 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).
4. 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).
5. 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).
6. Giuseppe Ciaburro & Gino Iannace, 2021. "Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review," Data, MDPI, vol. 6(6), pages 1-30, May.
7. Hampton, Harrison & Foley, Aoife, 2022. "A review of current analytical methods, modelling tools and development frameworks applicable for future retail electricity market design," Energy, Elsevier, vol. 260(C).
8. 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.
9. Lin, Jin & Dong, Jun & Dou, Xihao & Liu, Yao & Yang, Peiwen & Ma, Tongtao, 2022. "Psychological insights for incentive-based demand response incorporating battery energy storage systems: A two-loop Stackelberg game approach," Energy, Elsevier, vol. 239(PC).
10. Densing, Martin & Wan, Yi, 2022. "Low-dimensional scenario generation method of solar and wind availability for representative days in energy modeling," Applied Energy, Elsevier, vol. 306(PB).
11. Chen, Xiao & Zanocco, Chad & Flora, June & Rajagopal, Ram, 2022. "Constructing dynamic residential energy lifestyles using Latent Dirichlet Allocation," Applied Energy, Elsevier, vol. 318(C).
12. Shan, Rui & Kittner, Noah, 2024. "Allocation of policy resources for energy storage development considering the Inflation Reduction Act," Energy Policy, Elsevier, vol. 184(C).
13. Ahir, Rajesh K. & Chakraborty, Basab, 2021. "A meta-analytic approach for determining the success factors for energy conservation," Energy, Elsevier, vol. 230(C).
14. Heung-gu Son & Yunsun Kim & Sahm Kim, 2020. "Time Series Clustering of Electricity Demand for Industrial Areas on Smart Grid," Energies, MDPI, vol. 13(9), pages 1-14, May.
15. 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).
16. Guo, Zhifeng & O'Hanley, Jesse R. & Gibson, Stuart, 2022. "Predicting residential electricity consumption patterns based on smart meter and household data: A case study from the Republic of Ireland," Utilities Policy, Elsevier, vol. 79(C).
17. Tang, Wenjun & Wang, Hao & Lee, Xian-Long & Yang, Hong-Tzer, 2022. "Machine learning approach to uncovering residential energy consumption patterns based on socioeconomic and smart meter data," Energy, Elsevier, vol. 240(C).
18. Maximilian Hoffmann & Leander Kotzur & Detlef Stolten & Martin Robinius, 2020. "A Review on Time Series Aggregation Methods for Energy System Models," Energies, MDPI, vol. 13(3), pages 1-61, February.
19. Ma, Shuaiyin & Huang, Yuming & Liu, Yang & Liu, Haizhou & Chen, Yanping & Wang, Jin & Xu, Jun, 2023. "Big data-driven correlation analysis based on clustering for energy-intensive manufacturing industries," Applied Energy, Elsevier, vol. 349(C).
20. 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.
21. 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).
22. 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).
23. Das, Laya & Garg, Dinesh & Srinivasan, Babji, 2020. "NeuralCompression: A machine learning approach to compress high frequency measurements in smart grid," Applied Energy, Elsevier, vol. 257(C).
24. Oscar A. Bustos-Brinez & Javier Rosero Garcia, 2025. "Clustering Analysis for Active and Reactive Energy Consumption Data Based on AMI Measurements," Energies, MDPI, vol. 18(1), pages 1-22, January.
25. 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).
26. 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).