Smart home systems and energy efficiency in daily lives

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dc.contributor.authorAksoy, Ramazan
dc.contributor.authorKaraoglu, Sena
dc.contributor.authorYuzak, Ergin
dc.contributor.authorÇiÇek, Gulay
dc.date.accessioned2026-01-31T15:04:29Z
dc.date.available2026-01-31T15:04:29Z
dc.date.issued2025
dc.departmentİstanbul Beykent Üniversitesi
dc.description.abstractThis chapter examines the differences between the energy efficiency and environmental impacts of traditional houses and smart home systems in daily life. Compared to classical arrangements in traditional homes, the advantages of automation and energy management provided by using smart home systems are discussed. This chapter evaluates and compares the effects of both concepts on daily life, focusing on criteria such as comfort, security and energy saving between a traditional house and a house furnished with a smart system. The chapter also addresses the environmental sustainability of smart home systems. The potential of smart home systems to provide energy efficiency has the potential to reduce the environmental impact of this technology. The chapter addresses how smart home systems can be optimized, especially in terms of energy saving, to understand their environmental impact and discusses how this technology can contribute to a sustainable lifestyle. In this context, it evaluates the environmental sustainability and user convenience of future home designs by evaluating the environmental impacts of smart home systems compared to traditional homes. This study also aims to evaluate the performance of commonly used classification algorithms such as Neural Network, Support Vector Machines Rbf Kernel, Logistic Regression, Decision Trees, K Nearest Neighbor Algorithm, Support Vector Machines and Random Forest on smart home systems and traditional home energy consumption dataset. The effectiveness of each algorithm in energy consumption classification is analyzed using various performance metrics, and the results allow inferences to be made to support important decisions on the energy efficiency of smart home systems. This study provides valuable insights into identifying optimal classification algorithms for future smart home systems design to support energy saving and sustainability goals. © Springer Nature Switzerland AG 2025. All rights reserved.
dc.identifier.doi10.1007/978-3-031-78038-7_1
dc.identifier.endpage32
dc.identifier.isbn9783031780387
dc.identifier.isbn9783031780370
dc.identifier.scopus2-s2.0-105004969613
dc.identifier.scopusqualityN/A
dc.identifier.startpage1
dc.identifier.urihttps://doi.org/10.1007/978-3-031-78038-7_1
dc.identifier.urihttps://hdl.handle.net/20.500.12662/10566
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Nature
dc.relation.publicationcategoryKitap Bölümü - Uluslararası
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20260128
dc.subjectCoefficient of performance
dc.subjectEnvironmental management systems
dc.subjectSmart homes
dc.subjectClassification algorithm
dc.subjectDaily lives
dc.subjectEnergy
dc.subjectEnergy savings
dc.subjectEnergy-consumption
dc.subjectEnergy-savings
dc.subjectEnvironmental sustainability
dc.subjectSmart-home system
dc.subjectSupport vectors machine
dc.subjectTraditional house
dc.subjectEnergy saving
dc.titleSmart home systems and energy efficiency in daily lives
dc.typeBook Chapter

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