IDEAS home Printed from https://ideas.repec.org/r/eee/appene/v194y2017icp333-342.html
   My bibliography  Save this item

k-means based load estimation of domestic smart meter measurements

Citations

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


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. Xiong, Rui & Li, Zhengyang & Yang, Ruixin & Shen, Weixiang & Ma, Suxiao & Sun, Fengchun, 2022. "Fast self-heating battery with anti-aging awareness for freezing climates application," Applied Energy, Elsevier, vol. 324(C).
  3. Alejandro Pena-Bello & Edward Barbour & Marta C. Gonzalez & Selin Yilmaz & Martin K. Patel & David Parra, 2020. "How Does the Electricity Demand Profile Impact the Attractiveness of PV-Coupled Battery Systems Combining Applications?," Energies, MDPI, vol. 13(15), pages 1-19, August.
  4. Chreim, Bashar & Esseghir, Moez & Merghem-Boulahia, Leila, 2022. "LOSISH—LOad Scheduling In Smart Homes based on demand response: Application to smart grids," Applied Energy, Elsevier, vol. 323(C).
  5. Raghuvamsi, Y & Teeparthi, Kiran, 2023. "A review on distribution system state estimation uncertainty issues using deep learning approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
  6. Többen, Johannes & Schröder, Thomas, 2018. "A maximum entropy approach to the estimation of spatially and sectorally disaggregated electricity load curves," Applied Energy, Elsevier, vol. 225(C), pages 797-813.
  7. Zhong, Shengyuan & Wang, Xiaoyuan & Zhao, Jun & Li, Wenjia & Li, Hao & Wang, Yongzhen & Deng, Shuai & Zhu, Jiebei, 2021. "Deep reinforcement learning framework for dynamic pricing demand response of regenerative electric heating," Applied Energy, Elsevier, vol. 288(C).
  8. van Zoest, Vera & El Gohary, Fouad & Ngai, Edith C.H. & Bartusch, Cajsa, 2021. "Demand charges and user flexibility – Exploring differences in electricity consumer types and load patterns within the Swedish commercial sector," Applied Energy, Elsevier, vol. 302(C).
  9. Ruhang, Xu, 2020. "Efficient clustering for aggregate loads: An unsupervised pretraining based method," Energy, Elsevier, vol. 210(C).
  10. Fanidhar Dewangan & Almoataz Y. Abdelaziz & Monalisa Biswal, 2023. "Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review," Energies, MDPI, vol. 16(3), pages 1-55, January.
  11. Kang, J. & Reiner, D., 2021. "Machine Learning on residential electricity consumption: Which households are more responsive to weather?," Cambridge Working Papers in Economics 2142, Faculty of Economics, University of Cambridge.
  12. 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.
  13. Naderi, Shayan & Heslop, Simon & Chen, Dong & Watts, Scott & MacGill, Iain & Pignatta, Gloria & Sproul, Alistair, 2023. "Clustering based analysis of residential duck curve mitigation through solar pre-cooling: A case study of Australian housing stock," Renewable Energy, Elsevier, vol. 216(C).
  14. Thiago Eliandro de Oliveira Gomes & André Ross Borniatti & Vinícius Jacques Garcia & Laura Lisiane Callai dos Santos & Nelson Knak Neto & Rui Anderson Ferrarezi Garcia, 2023. "Clustering Electrical Customers with Source Power and Aggregation Constraints: A Reliability-Based Approach in Power Distribution Systems," Energies, MDPI, vol. 16(5), pages 1-20, March.
  15. Oscar Duarte & Javier E. Duarte & Javier Rosero-Garcia, 2024. "Data Imputation in Electricity Consumption Profiles through Shape Modeling with Autoencoders," Mathematics, MDPI, vol. 12(19), pages 1-19, September.
  16. Wang, Yingli & Duan, Jialong & Zhao, Yuanyuan & Yuan, Haiwen & He, Benlin & Tang, Qunwei, 2018. "Film-type rain energy converters from conductive polymer/PtCo hybrids," Applied Energy, Elsevier, vol. 218(C), pages 317-324.
  17. Abdelfattah Abassi & Mostapha El Jai & Ahmed Arid & Hussain Benazza, 2025. "Modeling and Mitigating Billing Attacks in Scalable Smart Grids with Distributed and Intelligent Systems," SN Operations Research Forum, Springer, vol. 6(1), pages 1-34, March.
  18. Emilio Ghiani & Alessandro Serpi & Virginia Pilloni & Giuliana Sias & Marco Simone & Gianluca Marcialis & Giuliano Armano & Paolo Attilio Pegoraro, 2018. "A Multidisciplinary Approach for the Development of Smart Distribution Networks," Energies, MDPI, vol. 11(10), pages 1-29, September.
  19. Rafik Nafkha & Krzysztof Gajowniczek & Tomasz Ząbkowski, 2018. "Do Customers Choose Proper Tariff? Empirical Analysis Based on Polish Data Using Unsupervised Techniques," Energies, MDPI, vol. 11(3), pages 1-17, February.
  20. Zhou, Huansheng & Zheng, J.H. & Li, Zhigang & Wu, Q.H. & Zhou, X.X., 2019. "Multi-stage contingency-constrained co-planning for electricity-gas systems interconnected with gas-fired units and power-to-gas plants using iterative Benders decomposition," Energy, Elsevier, vol. 180(C), pages 689-701.
  21. Abeysinghe, Sathsara & Wu, Jianzhong & Sooriyabandara, Mahesh & Abeysekera, Muditha & Xu, Tao & Wang, Chengshan, 2018. "Topological properties of medium voltage electricity distribution networks," Applied Energy, Elsevier, vol. 210(C), pages 1101-1112.
  22. Mitra, Somalee & Chakraborty, Basab & Mitra, Pabitra, 2024. "Smart meter data analytics applications for secure, reliable and robust grid system: Survey and future directions," Energy, Elsevier, vol. 289(C).
  23. Jingyuan Zhang & Jusheng Song & Zouyang Fan, 2023. "The Study of Historical Progression in the Distribution of Urban Commercial Space Locations—Example of Paris," Sustainability, MDPI, vol. 15(19), pages 1-23, October.
  24. Guo Xu & Xinliang Teng & Lei Zhang & Jianjun Xu, 2025. "Electricity Data Quality Enhancement Strategy Based on Low-Rank Matrix Recovery," Energies, MDPI, vol. 18(4), pages 1-24, February.
  25. Malin Lachmann & Jaime Maldonado & Wiebke Bergmann & Francesca Jung & Markus Weber & Christof Büskens, 2020. "Self-Learning Data-Based Models as Basis of a Universally Applicable Energy Management System," Energies, MDPI, vol. 13(8), pages 1-42, April.
  26. Elnozahy, Ahmed & Abdel-Salam, Mazen & Abo-Elyousr, Farag K., 2024. "Optimal techno-economic energy coordination of solar PV water pumping irrigation systems," Energy, Elsevier, vol. 288(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.