The DOI matches its Crossref record; 104 of the 104 references it cites were found in a registry or an index.
Big data analytics in smart grids: state‐of‐the‐art, challenges, opportunities, and future directions
Open accessAbstract
Big data has potential to unlock novel groundbreaking opportunities in power grid that enhances a multitude of technical, social, and economic gains. As power grid technologies evolve in conjunction with measurement and communication technologies, this results in unprecedented amount of heterogeneous big data. In particular, computational complexity, data security, and operational integration of big data into power system planning and operational frameworks are the key challenges to transform the heterogeneous large dataset into actionable outcomes. In this context, suitable big data analytics combined with visualization can lead to better situational awareness and predictive decisions. This paper presents a comprehensive state‐of‐the‐art review of big data analytics and its applications in power grids, and also identifies challenges and opportunities from utility, industry, and research perspectives. The paper analyzes research gaps and presents insights on future research directions to integrate big data analytics into power system planning and operational frameworks. Detailed information for utilities looking to apply big data analytics and insights on how utilities can enhance revenue streams and bring disruptive innovation are discussed. General guidelines for utilities to make the right investment in the adoption of big data analytics by unveiling interdependencies among critical infrastructures and operations are also provided.
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Cites
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Cited by
Applications of artificial intelligence and machine learning in smart cities
Ullah, Z., Al-Turjman, F., Mostarda, L., & Gagliardi, R. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.02.069 · Cited by 756
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
A survey on the development status and application prospects of knowledge graph in smart grids
Wang, J., Wang, X., Ma, C., & Kou, L. (2021) · IET Generation, Transmission & Distribution · doi:10.1049/gtd2.12040 · Cited by 111
Metadata
- Authors
- Bhattarai, B. P., Paudyal, S., Luo, Y., Mohanpurkar, M., Cheung, K., Tonkoski, R., Hovsapian, R., Myers, K. S., Zhang, R., Zhao, P., Manic, M., Zhang, S., & Zhang, X.
- Venue
- IET Smart Grid
- Published
- 2019-03
- Volume
- 2
- Issue
- 2
- Pages
- 141-154
- ISSN
- 2515-2947 (ISSN-L)
- OpenAlex
- W2913259953
- Cited by
- 292
- References
- 104
- FWCI
- 10.57
- Type
- journal article
- Language
- en
- Open access
- gold
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- Bhattarai, B. P., Paudyal, S., Luo, Y., Mohanpurkar, M., Cheung, K., Tonkoski, R., Hovsapian, R., Myers, K. S., Zhang, R., Zhao, P., Manic, M., Zhang, S., & Zhang, X. (2019). Big data analytics in smart grids: state‐of‐the‐art, challenges, opportunities, and future directions. IET Smart Grid. https://doi.org/10.1049/iet-stg.2018.0261
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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).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
