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Load Signature Study—Part I: Basic Concept, Structure, and Methodology
Abstract
Load signature is the unique consumption pattern intrinsic to each individual electrical appliance/piece of equipment. This paper focus on building a universal platform to better understand and explore the nature of electricity consumption patterns using load signatures and advanced technology, such as feature extraction and intelligent computing. Through this knowledge, we can explore and develop innovative applications to achieve better utilization of resources and develop more intelligent ways of operation. This paper depicts the basic concept, features of load signatures, structure and methodology of applying mathematical programming techniques, pattern recognition tools, and committee decision mechanism to perform load disaggregation. New indices are also introduced to aid our understanding of the nature of load signatures and different disaggregation algorithms.
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Citations
Cited by
Is disaggregation the holy grail of energy efficiency? The case of electricity
Carrie Armel, K., Gupta, A., Shrimali, G., & Albert, A. (2013) · Energy Policy · doi:10.1016/j.enpol.2012.08.062 · Cited by 595
Big data analytics in smart grids: a review
Zhang, Y., Huang, T., & Bompard, E. F. (2018) · Energy Informatics · doi:10.1186/s42162-018-0007-5 · Cited by 382
Metadata
- Authors
- Liang, J., Ng, S. K. K., Kendall, G., & Cheng, J. W. M.
- Venue
- IEEE Transactions on Power Delivery
- Published
- 2010-04
- Volume
- 25
- Issue
- 2
- Pages
- 551-560
- ISSN
- 0885-8977 (ISSN-L)
- 1937-4208
- OpenAlex
- W2025423356
- Cited by
- 453
- References
- 0
- FWCI
- 10.82
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Load Signature Study—Part I: Basic Concept, Structure, and Methodology. https://app.cochat.ai/papers/10.1109/tpwrd.2009.2033799
- The paper
- Liang, J., Ng, S. K. K., Kendall, G., & Cheng, J. W. M. (2010). Load Signature Study—Part I: Basic Concept, Structure, and Methodology. IEEE Transactions on Power Delivery. https://doi.org/10.1109/tpwrd.2009.2033799
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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