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Machine learning-based prediction model for battery levels in IoT devices using meteorological variables
dc.contributor.author | Zurita Macias, Juan Emilio | |
dc.contributor.author | Trilles, Sergio | |
dc.date.accessioned | 2024-03-13T10:37:35Z | |
dc.date.available | 2024-03-13T10:37:35Z | |
dc.date.issued | 2024-04-01 | |
dc.identifier.citation | Juan Emilio Zurita Macias, Sergio Trilles, Machine learning-based prediction model for battery levels in IoT devices using meteorological variables, Internet of Things, Volume 25, 2024, 101109, ISSN 2542-6605, https://doi.org/10.1016/j.iot.2024.101109. | ca_CA |
dc.identifier.issn | 2542-6605 | |
dc.identifier.uri | http://hdl.handle.net/10234/206153 | |
dc.description.abstract | Efficient energy management is vital for the sustainability of IoT devices employing solar harvesting systems, particularly to circumvent battery depletion during periods of diminished solar incidence. Embracing the structured methodology of CRISP-DM, this study introduces machine learning (ML) models that utilise meteorological data to predict battery charge levels in solar-powered IoT devices. These models enable proactive adjustments to the devices’ data sampling frequencies, ensuring effective energy utilisation. The proposed ML models were evaluated using authentic battery charge data and weather forecast records. The empirical results of this study corroborate the predictive prowess of the models, with an average accuracy reaching as high as 94.09% in specific test cases. This substantiates the potential of the developed methodology to significantly enhance the energy autonomy of IoT devices through predictive analytics. | ca_CA |
dc.description.sponsorShip | Funding for open access charge: CRUE-Universitat Jaume I | |
dc.format.extent | 16 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Elsevier | ca_CA |
dc.relation.isPartOf | Internet of Things, Volume 25, 2024. | ca_CA |
dc.rights | © 2024 Published by Elsevier B.V. | ca_CA |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | ca_CA |
dc.subject | Internet of Things | ca_CA |
dc.subject | Machine learning | ca_CA |
dc.subject | Battery level prediction | ca_CA |
dc.subject | Solar energy harvesting | ca_CA |
dc.title | Machine learning-based prediction model for battery levels in IoT devices using meteorological variables | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | 10.1016/j.iot.2024.101109 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | https://www.sciencedirect.com/science/article/pii/S2542660524000519 | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
project.funder.name | Ministerio de Ciencia e Innovación de España | ca_CA |
project.funder.name | Agencia Estatal de Investigación | ca_CA |
oaire.awardNumber | PID2022-141813OB-I00 | ca_CA |
oaire.awardNumber | MCIN/AEI/10.13039/501100011033 | ca_CA |
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