Smart Sensor Networks for Real-Time Energy Optimization in Green Buildings

Thesis Abstract

The increasing global emphasis on sustainable development underscores the urgent need for energy-efficient solutions within the built environment, particularly green buildings, which aim to minimize environmental impact while maintaining occupant comfort. Despite advances in sustainable architecture, significant energy wastage persists due to suboptimal operational management, often attributable to inadequate real-time monitoring systems. This study seeks to develop and evaluate a smart sensor network framework designed to optimize energy consumption in green buildings through real-time data acquisition and adaptive control strategies. The primary aim is to enhance energy efficiency by integrating advanced sensor technologies with intelligent algorithms capable of dynamic decision-making. Specific objectives include designing a sensor deployment model tailored to green building environments, developing algorithms for real-time data analysis and energy load adjustment, and empirically assessing the impact of the implemented system on energy consumption patterns over a twelve-month period. Adopting a mixed-methods research design, this study combines quantitative experimental approaches with qualitative system usability assessments. The population comprises operational green buildings within a metropolitan area engaged in sustainable initiatives, with a target sample of five buildings selected through stratified random sampling to ensure representation of diverse architectural typologies and occupancy patterns. Data collection instruments include wireless sensor arrays for temperature, humidity, occupancy, light levels, and energy consumption metrics, alongside semi-structured interviews with facility managers and occupants to gather contextual insights. The quantitative data will be analyzed using regression analysis to identify correlations between sensor-driven interventions and reductions in energy use, while qualitative data will undergo thematic analysis to evaluate system usability and occupant response. The anticipated findings suggest a statistically significant reduction in energy consumption—projected at 15-20%—attributable to the adaptive control provided by the sensor network. The study expects to demonstrate that the integrated sensor framework facilitates more precise environmental regulation, contributes to occupant comfort, and enhances operational efficiency in green buildings. It aims to establish a model that can be replicated or adapted across similar contexts, advancing understanding of ICT-driven energy management. From a theoretical perspective, the research integrates the Technology Acceptance Model to explore user acceptance and the Ecological Modernization Theory to contextualize the technological shift toward sustainable practice. The contribution to knowledge lies in advancing a practical, scalable model for real-time energy optimization that leverages sensor network technology within sustainable building management frameworks. It fills notable gaps in empirical evidence regarding the deployment effectiveness of integrated sensor systems in diverse green building settings and provides a comprehensive analytical methodology for evaluating such interventions. The main conclusion underscores the potential of intelligent sensor networks to substantially improve energy efficiency and occupant comfort when embedded within green building operations. Based on the findings, it is recommended that building management adopt similar sensor-driven systems with tailored algorithms, invest in ongoing sensor maintenance, and incorporate occupant feedback mechanisms to maximize the systems' benefits. Additionally, future research should explore long-term impacts, integration with renewable energy sources, and automation capabilities to further advance sustainable building practices.