Combined power generation and electricity storage device using deep learning and internet of things technologies. (November 2022)

• Record Type: Journal Article
• Authors: Iwendi, Celestine; Wang, Gai-Ge
• Is Part Of: Energy reports. Volume 8(2022)
• Page Start: 5016
• Page End: 5025
• Publication Date: 2022-11
• Subjects: Energy storage -- Machine learning -- Internet of things -- Fuzzy logic -- Electricity storage device -- Power generation; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.02.304
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: 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.