The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Methods and applications for Artificial Intelligence, Big Data, Internet of Things, and Blockchain in smart energy management
Open accessAbstract
Information technologies involving artificial Intelligence, big data, Internet of Things devices and blockchain have been developed and implemented in many engineering fields worldwide. Existing review articles focus on developments and characteristics of individual topics and the associated deployment in the energy sector. These technologies, all based on communication, information, and data analysis, are naturally coherent and integrable. This article reviews the literature and patents in four closely related fields and aims to provide a holistic view of how they are related and their integrability in relation to smart energy management strategies. Artificial intelligence models forecast energy use and load profiles as well as schedule resources to ensure reliable performance and effective utilization of energy resources. Training artificial intelligence models requires immense volumes of data. Utilizing big data systems and data mining enables the discovery of new functions and relationships, which determines the performance of artificial intelligence. Data mining also refines the information; thus, artificial intelligence is trained iteratively with more accurate data. Smart energy management can be further enhanced through advanced digital technologies like Internet of Things and blockchain. An Internet of Things platform containing edge, fog and cloud layers helps connect artificial intelligence to other hardware and software devices and systems. Furthermore, an Internet of Things platform efficiently transmits and stores data, improving access and availability to stakeholders for data mining. Emerging technologies such as blockchain and cryptocurrency facilitate energy trading and can be designed in the cloud layer of an Internet of Things platform to supplement data storage. Providing an efficient and seamless integration of artificial intelligence, big data, and advanced digital technologies will be an important factor in the emerging transition of the energy sector to a lower-carbon system.
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Cites
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IoB: Internet-of-batteries for electric Vehicles–Architectures, opportunities, and challenges
Li, H., Bin Kaleem, M., Liu, Z., Wu, Y., Liu, W., & Huang, Z. (2023) · Green Energy and Intelligent Transportation · doi:10.1016/j.geits.2023.100128 · Cited by 95
Battery State of Health Estimate Strategies: From Data Analysis to End-Cloud Collaborative Framework
Yang, K., Zhang, L., Zhang, Z., Yu, H., Wang, W., Ouyang, M., Zhang, C., Sun, Q., Yan, X., Yang, S., et al. (2023) · Batteries · doi:10.3390/batteries9070351 · Cited by 58
Educational Collaboration : Teachers and Artificial Intelligence
Mujiono, M. (2023) · Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran · doi:10.33394/jk.v9i2.7801 · Cited by 12
Metadata
- Authors
- Li, J., Herdem, M. S., Nathwani, J., & Wen, J. Z.
- Venue
- Energy and AI
- Publisher
- Elsevier BV
- Published
- 2023-01
- Volume
- 11
- Pages
- 100208
- ISSN
- 2666-5468 (ISSN-L)
- OpenAlex
- W4300982014
- Cited by
- 409
- References
- 50
- FWCI
- 23.62
- Type
- journal article
- Language
- en
- Open access
- gold
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- Li, J., Herdem, M. S., Nathwani, J., & Wen, J. Z. (2023). Methods and applications for Artificial Intelligence, Big Data, Internet of Things, and Blockchain in smart energy management. Energy and AI. https://doi.org/10.1016/j.egyai.2022.100208
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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.
