Open Access
| Issue |
E3S Web Conf.
Volume 727, 2026
International Conference on Electronics, Engineering Physics and Earth Science (EEPES 2026)
|
|
|---|---|---|
| Article Number | 02012 | |
| Number of page(s) | 9 | |
| Section | Renewable Energy and Green Technologies | |
| DOI | https://doi.org/10.1051/e3sconf/202672702012 | |
| Published online | 27 July 2026 | |
- S. Kaur, P. Chowhan, A. Sharma, A Novel Hybrid Deep Learning-Based Framework for Intelligent Anomaly Detection in Smart Meters. IEEE Access 13, 107022 (2025). https://doi.org/10.1109/ACCESS.2025.3581257 [Google Scholar]
- Y. Himeur, K. Ghanem, A. Alsalemi, F. Bensaali, A. Amira, Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives. Appl. Energy 287, 116601 (2021). https://doi.org/10.1016/j.apenergy.2021.116601 [CrossRef] [Google Scholar]
- L. Lei, B. Wu, X. Fang, L. Chen, H. Wu, W. Liu, A dynamic anomaly detection method of building energy consumption based on data mining technology. Energy 263, 125575 (2023). https://doi.org/10.1016/j.energy.2022.125575 [Google Scholar]
- Z. Niu, K. Yu, X. Wu, LSTM-Based VAE-GAN for Time-Series Anomaly Detection. Sensors 20, 3738 (2020). https://doi.org/10.3390/s20133738 [Google Scholar]
- G. E. Okereke, M. C. Bali, C. N. Okwueze, E. C. Ukekwe, S. C. Echezona, C. I. Ugwu, K-means clustering of electricity consumers using time-domain features from smart meter data. J. Electr. Syst. Inf. Technol. 10, 2 (2023). https://doi.org/10.1186/s43067-023-00068-3 [Google Scholar]
- X. Liu, Y. Ding, H. Tang, F. Xiao, A data mining-based framework for the identification of daily electricity usage patterns and anomaly detection in building electricity consumption data. Energy Build. 231, 110601 (2021). https://doi.org/10.1016/j.enbuild.2020.110601 [Google Scholar]
- M. Gulati, P. Arjunan, LEAD1.0: A Large-scale Annotated Dataset for Energy Anomaly Detection in Commercial Buildings, in Proceedings of the 13th ACM International Conference on Future Energy Systems (ACM, Virtual Event, 2022), pp. 485–488 [Google Scholar]
- A. Nukala, S. K. C. Sekhara, Y. K. Peker, M. R. Abid, A Practical Framework for Energy Fault Detection in Smart Buildings, in Proceedings of the 2025 6th International Conference on Artificial Intelligence and Robot Control (IEEE, Savannah, GA, USA, 2025), 194–201 [Google Scholar]
- S. Lloyd, Least squares quantization in PCM. IEEE Trans. Inf. Theory 28, 129 (1982). https://doi.org/10.1109/TIT.1982.1056489 [CrossRef] [Google Scholar]
- P. J. Rousseeuw, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53 (1987). https://doi.org/10.1016/0377-0427(87)90125-7 [CrossRef] [Google Scholar]
- T. Liu, K. M. Ting, Z.-H. Zhou, Isolation-based anomaly detection. ACM Trans. Knowl. Discov. Data 6, 1–39 (2012). https://doi.org/10.1145/2133360.2133363 [Google Scholar]
- M. M. Breunig, H.-P. Kriegel, R. T. Ng, J. Sander, LOF: Identifying density-based local outliers. ACM SIGMOD Rec. 29, 93–104 (2000). https://doi.org/10.1145/342009.335388 [Google Scholar]
- L. Breiman, Random forests. Mach. Learn. 45, 5 (2001) [NASA ADS] [CrossRef] [Google Scholar]
- T. Chen, C. Guestrin, XGBoost: A scalable tree boosting system, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, San Francisco, CA, USA, 2016), 785–794 [Google Scholar]
- G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, T.-Y. Liu, LightGBM: A highly efficient gradient boosting decision tree, in Advances in Neural Information Processing Systems (Curran Associates, Inc., 2017) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.