The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
Open accessAbstract
The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue. To date, substantial works have been conducted on smart meter data analytics. To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics. Following the three stages of analytics, namely, descriptive, predictive, and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management. We also review the techniques and methodologies adopted or developed to address each application. In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security.
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Cites
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Energy Sharing Management for Microgrids With PV Prosumers: A Stackelberg Game Approach
Liu, N., Yu, X., Wang, C., & Wang, J. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2654302 · Cited by 425
A review of electric load classification in smart grid environment
Zhou, K.-L., Yang, S.-L., & Shen, C. (2013) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2013.03.023 · Cited by 254
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Antonopoulos, I., Robu, V., Couraud, B., Kirli, D., Norbu, S., Kiprakis, A., Flynn, D., Elizondo-Gonzalez, S., & Wattam, S. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109899 · Cited by 656
Francisco, A., Mohammadi, N., & Taylor, J. E. (2020) · Journal of Management in Engineering · doi:10.1061/(asce)me.1943-5479.0000741 · Cited by 325
When Machine Learning Meets Privacy in 6G: A Survey
Sun, Y., Liu, J., Wang, J., Cao, Y., & Kato, N. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3011561 · Cited by 211
Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow
Zamzam, A. S., & Baker, K. (2020) · 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) · doi:10.1109/smartgridcomm47815.2020.9303008 · Cited by 206
Metadata
- Authors
- Wang, Y., Chen, Q., Hong, T., & Kang, C.
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2019-05
- Volume
- 10
- Issue
- 3
- Pages
- 3125-3148
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- OpenAlex
- W2786918196
- Cited by
- 1,369
- References
- 3
- FWCI
- 48.92
- Type
- journal article
- Language
- en
- Open access
- green
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- Wang, Y., Chen, Q., Hong, T., & Kang, C. (2019). Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges. IEEE Transactions on Smart Grid. https://doi.org/10.1109/tsg.2018.2818167
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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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