Edge Deep Learning for Smart Energy Applications
| dc.cclicence | CC-BY-NC | en |
| dc.contributor.author | Alsalemi, Abdullah | |
| dc.contributor.author | Amira, Abbes | |
| dc.contributor.author | Malekmohamadi, Hossein | |
| dc.contributor.author | Diao, Kegong | |
| dc.contributor.author | Bensaali, Faycal | |
| dc.date.acceptance | 2021-12 | |
| dc.date.accessioned | 2022-02-03T09:15:56Z | |
| dc.date.available | 2022-02-03T09:15:56Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | 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. | en |
| dc.funder | No external funder | en |
| dc.identifier.citation | 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. | en |
| dc.identifier.uri | https://hdl.handle.net/2086/21664 | |
| dc.language.iso | en | en |
| dc.peerreviewed | Yes | en |
| dc.publisher | CRC Press | en |
| dc.researchinstitute | Institute of Artificial Intelligence (IAI) | en |
| dc.subject | Edge computing | en |
| dc.subject | Energy efficiency | en |
| dc.subject | artificial intelligence | en |
| dc.subject | deep learning | en |
| dc.subject | internet of things | en |
| dc.subject | internet of energy | en |
| dc.title | Edge Deep Learning for Smart Energy Applications | en |
| dc.type | Book chapter | en |