Edge Deep Learning for Smart Energy Applications




Journal Title

Journal ISSN



Volume Title


CRC Press


Book chapter

Peer reviewed



The Internet of Energy (IoE) paradigm is an advancing area of research concerning the fusion of smart technology and energy efficiency [1], combing data collection, processing, and visualization. Smart energy monitoring witnesses technological advancements such as smart metering and IoE networking, allowing the expansion of smart energy networks in a smart house. In this research, we aim to understand energy behavior through big data collection and classification and improve energy efficiency using behavioral economics, deep learning-based recommender systems, and intuitive data visualizations. In specific, a specialized case study is reported on the ODROID XU4 platform [3], and a setup developed at De Montfort University (DMU) at the Energy Lab and AI Lab, it is aimed to build a novel appliance level dataset with contextual ambient environmental data. As a novel advancement in the field, the ODROID performs edge deep learning computations on the collected data, to clean it, summarize it, anonymize it, and classification, it transmits it to a cloud server for further deep processing and storage. Concluding, the proposed work provides aids in exploiting energy-efficiency technologies for improving energy efficiency via an innovative, automated energy efficiency deep learning engine.



Edge computing, Energy efficiency, artificial intelligence, deep learning, internet of things, internet of energy


Alsalemi, A., Amira, A., Malekmohamadi,, H., Diao, K., Bensaali, F. (2022) Edge Deep Learning for Smart Energy Applications. In: Verma, A., Verma, P., Farhaoui, Y., Lv, Z. (Eds.) Emerging Real-World Applications of Internet of Things, Boca Raton: CRC Press.


Research Institute

Institute of Artificial Intelligence (IAI)