The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Power Utility Nontechnical Loss Analysis With Extreme Learning Machine Method
Abstract
This paper presents a new approach to nontechnical loss (NTL) analysis for utilities using the modern computational technique extreme learning machine (ELM). Nontechnical losses represent a significant proportion of electricity losses in both developing and developed countries. The ELM-based approach presented here uses customer load-profile information to expose abnormal behavior that is known to be highly correlated with NTL activities. This approach provides a method of data mining for this purpose, and it involves extracting patterns of customer behavior from historical kWh consumption data. The results yield classification classes that are used to reveal whether any significant behavior that emerges is due to irregularities in consumption. In this paper, ELM and online sequential-ELM (OS-ELM) algorithms are both used to achieve an improved classification performance and to increase accuracy of results. A comparison of this approach with other classification techniques, such as the support vector machine (SVM) algorithm, is also undertaken and the ELM performance and accuracy in NTL analysis is shown to be superior.
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Cites
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Cited by
Extreme learning machines: a survey
Huang, G.-B., Wang, D. H., & Lan, Y. (2011) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-011-0019-y · Cited by 1,914
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Wang, Y., Chen, Q., Hong, T., & Kang, C. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2818167 · Cited by 1,369
Self-citationArtificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities
Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.125834 · Cited by 1,258
A Comprehensive Review of Smart Energy Meters in Intelligent Energy Networks
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Soofi, A. A., & Awan, A. (2017) · Journal of Basic & Applied Sciences · doi:10.6000/1927-5129.2017.13.76 · Cited by 316
Metadata
- Authors
- Nizar, A. H., Dong, Z. Y., & Wang, Y.
- Venue
- IEEE Transactions on Power Systems
- Published
- 2008-08
- Volume
- 23
- Issue
- 3
- Pages
- 946-955
- ISSN
- 0885-8950 (ISSN-L)
- OpenAlex
- W2162112159
- Cited by
- 342
- References
- 19
- FWCI
- 8.97
- Type
- journal article
- Language
- en
- Open access
- closed
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- Nizar, A. H., Dong, Z. Y., & Wang, Y. (2008). Power Utility Nontechnical Loss Analysis With Extreme Learning Machine Method. IEEE Transactions on Power Systems. https://doi.org/10.1109/tpwrs.2008.926431
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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