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Using Blockchain and Artificial Intelligence in Energy Management as a Tool to Achieve Energy Efficiency

Improving energy management has received a lot of attention due to environmental issues, energy crises, and growing energy prices in today’s world. Various digital technologies have been developed to enhance energy management to cover these challenges. This article investigates Blockchain and Artificial Intelligence, which have recently attracted increasing attention. The study applies the software of VOSviewer for providing bibliometric analysis. The data (pull of the scientific documents) was generated from the Scopus and Web of Science. There have been done a critical analysis of the literature for evaluating the research in the proposed area. This paper focuses on possible use cases of blockchain and artificial intelligence technologies and considers aspects of technology used to achieve energy efficiency. The findings showed that the technologies are widely applied in energy management. Besides, it is lack of documents that contain the terms ‘energy efficiency and ‘blockchain’. The results of analysis confirm that artificial intelligence has been rapidly integrating with energy management, helping to develop more efficient and secure energy generation techniques.
- Sumy State University Ukraine
- Sumy State University Ukraine
- Silesian University of Technology Poland
- Politechnika Śląska w Gliwicach Poland
- Silesian University of Technology Poland
blockchain, energy management, Economic history and conditions, TA177.4-185, Information technology, HC10-1085, artificial intelligence, T58.5-58.64, Economics as a science, Engineering economy, HB71-74, energy efficiency
blockchain, energy management, Economic history and conditions, TA177.4-185, Information technology, HC10-1085, artificial intelligence, T58.5-58.64, Economics as a science, Engineering economy, HB71-74, energy efficiency
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).18 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
