Combined power generation and electricity storage device using deep learning and internet of things technologies
Energy Reports, ISSN: 2352-4847, Vol: 8, Page: 5016-5025
2022
- 11Citations
- 49Captures
- 2Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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Findings from University of Bolton in the Area of Energy Described (Combined Power Generation and Electricity Storage Device Using Deep Learning and Internet of Things Technologies)
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Review Description
In microgrids, residential customers play a significant part in the operation. An alternative to client administration should be to utilize smart houses to deal with demand and implement demand responsiveness measures. A power generation and electricity storage device (PGESD) for next-generation technologies is proposed in this article. The current research provides an intelligent home load control system that promotes reaction to demand thinking about this circumstance. The technology is adapted to scenarios where users can charge fluctuating electric power and transmit microgeneration devices. The suggested system utilizes deep learning technology and a fuzzy logic model for better computation and lesser complexity. The choice process involves monitoring environmental information, power production, and battery storage. This article proposes a next-generation power generation and electricity storage device (PGESD). To create Smart Buildings and Microgrids, the proposed system employs technologies and techniques that have become increasingly important. With a precision and accuracy ratio of 89% and 92%, respectively, the proposed PGESD method yields precise numerical results.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S2352484722005510; http://dx.doi.org/10.1016/j.egyr.2022.02.304; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85128187062&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S2352484722005510; https://dx.doi.org/10.1016/j.egyr.2022.02.304
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