The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.
Big data analytics in smart grids: a review
Open accessAbstract
Data analytics are now playing a more important role in the modern industrial systems. Driven by the development of information and communication technology, an information layer is now added to the conventional electricity transmission and distribution network for data collection, storage and analysis with the help of wide installation of smart meters and sensors. This paper introduces the big data analytics and corresponding applications in smart grids. The characterizations of big data, smart grids as well as huge amount of data collection are firstly discussed as a prelude to illustrating the motivation and potential advantages of implementing advanced data analytics in smart grids. Basic concepts and the procedures of the typical data analytics for general problems are also discussed. The advanced applications of different data analytics in smart grids are addressed as the main part of this paper. By dealing with huge amount of data from electricity network, meteorological information system, geographical information system etc., many benefits can be brought to the existing power system and improve the customer service as well as the social welfare in the era of big data. However, to advance the applications of the big data analytics in real smart grids, many issues such as techniques, awareness, synergies, etc., have to be overcome.
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Cites
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Cited by
Ahmad, T., Madonski, R., Zhang, D., Huang, C., & Mujeeb, A. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112128 · Cited by 711
Li, J., Herdem, M. S., Nathwani, J., & Wen, J. Z. (2023) · Energy and AI · doi:10.1016/j.egyai.2022.100208 · Cited by 409
Microgrid Digital Twins: Concepts, Applications, and Future Trends
Bazmohammadi, N., Madary, A., Vasquez, J. C., Mohammadi, H. B., Khan, B., Wu, Y., & Guerrero, J. M. (2022) · IEEE Access · doi:10.1109/access.2021.3138990 · Cited by 233
Evaluation of Missing Data Imputation Methods for an Enhanced Distributed PV Generation Prediction
Sundararajan, A., & Sarwat, A. I. (2020) · Advances in Intelligent Systems and Computing · doi:10.1007/978-3-030-32520-6_43 · Cited by 22
Metadata
- Authors
- Zhang, Y., Huang, T., & Bompard, E. F.
- Venue
- Energy Informatics
- Published
- 2018-08-13
- Volume
- 1
- Issue
- 1
- ISSN
- 2520-8942 (ISSN-L)
- OpenAlex
- W2887119851
- Cited by
- 382
- References
- 77
- FWCI
- 12.23
- Type
- journal article
- Language
- en
- Open access
- gold
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