Assessment of Energy Efficiency in Smart Home Wireless Power Systems

Thesis Abstract

The rapid advancement of wireless power transfer (WPT) technologies has significantly influenced the development of smart home energy systems, presenting both opportunities for enhanced convenience and challenges related to energy efficiency. Despite the growing adoption of wireless power systems in residential environments, there remains limited empirical data on their operational efficiencies, especially considering diverse environmental, technical, and behavioral factors. This study aims to comprehensively assess the energy efficiency of wireless power systems deployed within smart homes, with specific objectives to identify key factors influencing efficiency, evaluate the impact of different design configurations, and develop optimization guidelines for energy-efficient operation. Employing a mixed-methods research design, the study combines quantitative and qualitative approaches to achieve a robust understanding of the subject matter. The quantitative component involves a cross-sectional survey of 150 smart home occupants across metropolitan areas, selected through stratified random sampling to ensure representation across different demographic groups. Data collection instruments include structured questionnaires designed to capture operational, technical, and behavioral data, along with in-situ measurements of wireless power transfer efficiency using calibrated power analyzers over a period of three months. The qualitative aspect involves semi-structured interviews with 20 system installers and engineers to gather insights into design practices, technical challenges, and maintenance issues affecting system efficiency. Data analysis utilizes multiple regression analysis to identify significant predictors of energy efficiency, path analysis to explore causal relationships among variables, and ANOVA to compare efficiencies across different system configurations. Thematic analysis is employed to interpret qualitative data, identifying recurring themes related to system design, user behavior, and maintenance practices. The study frames its theoretical underpinning on the Technology Acceptance Model and the Diffusion of Innovation Theory, which inform understanding of how user attitudes and adoption patterns influence overall system performance. Expected findings from this research include quantifiable relationships between technical parameters such as transfer distance, alignment precision, and coil design with energy efficiency outcomes. The analysis is anticipated to reveal that optimized coil alignment and adaptive power control significantly enhance efficiency, with behavioral factors such as user engagement and maintenance routines also playing crucial roles. Furthermore, the study expects to demonstrate substantial variability in efficiency levels between different system configurations, highlighting areas for standardization and best practice development. This research contributes new empirical evidence to the relatively under-explored area of real-world wireless power system performance in residential settings, moving beyond theoretical models to practical insights. It extends existing knowledge by identifying specific technical and behavioral determinants of energy efficiency, proposing a comprehensive efficiency assessment framework suitable for industry and academic application. The findings are intended to inform system designers, manufacturers, and policymakers by providing actionable guidelines for optimizing energy consumption without compromising functional performance. The study concludes that strategic improvements in wireless power system design, coupled with user education and routine maintenance, can substantially elevate energy efficiency levels in smart homes. Recommendations include adopting adaptive power management algorithms, promoting user awareness programs, and establishing industry standards for system configuration. It is further suggested that future research explore longitudinal assessments of system performance and the integration of real-time monitoring technologies to facilitate continuous efficiency optimization. Overall, this study aims to bridge the gap between technological potential and practical implementation, fostering sustainable and energy-efficient smart home environments.