Energy consumption estimation using machine learning with data from smart meters in a residential complex building in Iraq

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dc.contributor.authorAl-Shawwaf, Noor Malik Safaa
dc.contributor.authorIbrahim, Abdullahi Abdu
dc.contributor.authorAl-Sabti, Saif Mohamed Baraa
dc.date.accessioned2024-02-24T07:46:32Z
dc.date.available2024-02-24T07:46:32Z
dc.date.issued2023en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractDemand for energy production, energy conservation techniques, and now a significant international problem due to the exponential expansion in population and their total dependency on the use of electrical and electronic equipment. The construction industry is a significant user of energy, hence. This research paper aims to determine the energy usage of a residential complex building in Iraq by using machine learning strategies to the data collected by smart meters in that building. The critical focus is producing accurate projections of future loads in the residential complex over two or three years to surpass any potential artificial intelligence barriers. Data gathering, pre-processing, algorithm selection, model training, model assessment, and energy consumption estimation are the steps involved in the technique. The dataset has been corrected, and the three algorithms used are linear regression, decision trees, random forests, gradient boosting, bagging regressor, extra trees regressor,SVR, lasso, ridge, elastic net, K-nearest neighbours and neural network. The protection of users' privacy and the safety of their data are among the ethical issues. An accurate calculation of the energy consumed is required to manage energy and reduce costs effectively. The findings of this study can be used to address the problem of inefficient management of energy resources in residential structures in Iraq and other countries with comparable conditions.en_US
dc.identifier.citationAl-Shawwaf, N. M. S., Ibrahim, A. A., Al-Sabti, S. M. B. (2023). Energy consumption estimation using machine learning with data from smart meters in a residential complex building in Iraq. ISAS 2023 - 7th International Symposium on Innovative Approaches in Smart Technologies, Proceedings. 10.1109/ISAS60782.2023.10391736en_US
dc.identifier.isbn9798350383065
dc.identifier.scopus2-s2.0-85184802447
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4618
dc.indekslendigikaynakScopus
dc.institutionauthorAl-Shawwaf, Noor Malik Safaa
dc.institutionauthorIbrahim, Abdullahi Abdu
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofISAS 2023 - 7th International Symposium on Innovative Approaches in Smart Technologies, Proceedings
dc.relation.isversionof10.1109/ISAS60782.2023.10391736en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDataseten_US
dc.subjectEnergy Consumptionen_US
dc.subjectIraqen_US
dc.subjectMachine Learningen_US
dc.subjectResidential complexen_US
dc.subjectSmart Metersen_US
dc.titleEnergy consumption estimation using machine learning with data from smart meters in a residential complex building in Iraq
dc.typeConference Object

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