<b>ENERGY CONSERVATION THROUGH INTERNET-OF-THING FRAMEWORK</b>
Creators: name: Wu, Da Chun
Publisher: name: Purdue University Graduate School
Year: 2024
Subjects: classificationCode: 401799
subject: Mechanical engineering not elsewhere classified

description: Improving the energy efficiency of buildings and manufacturing plants involves a continuous cycle of real-time monitoring, analysis, decision-making, action, and assessment, which are essential components of a smart manufacturing approach. Achieving this requires a comprehensive platform that integrates data storage and sharing; incorporates models to interpret sensor data and algorithms to analyze it; provides actionable options with projected benefits and trade-offs; executes selected actions and evaluates their outcomes; and retains knowledge for ongoing enhancement. However, many commercially available solutions are designed for large-scale institutions, making them expensive and requiring significant customization by specialized professionals, which limits accessibility for smaller companies and building owners. This research aims to address these limitations by developing an IoT-based platform that integrates all essential functions while remaining affordable and user-friendly for small and medium-sized businesses and individual building owners. The platform supports the seamless integration of sensors, software, hardware models, decision-making algorithms, actuators, and a structured knowledge repository, with data communication and sharing managed via the internet through cloud services to ensure accessibility and flexibility. The platform was applied in real-world settings to verify its performance and usability, focusing on three core implementations to establish an advanced energy management framework. The first implementation involved ventilation optimization using IoT sensors to monitor parameters such as temperature, differential pressure, and airflow, combined with neural networks to predict system behavior under varying conditions. A genetic algorithm was used to identify optimal operational settings for make-up air units, ensuring energy-efficient ventilation while maintaining indoor air quality and temperature standards. This approach resulted in a 20% reduction in annual ventilation energy consumption and a 60% decrease in power demand on weekends. The second implementation focused on optimizing compressed air system pressure settings, addressing the high energy intensity and inefficiencies caused by leaks, heat, and pressure drops. IoT-enabled sensors captured real-time data on pressure, flow, and power consumption, which were analyzed using machine learning models, achieving a 7% energy saving for every 1 bar reduction in pressure. The final implementation addressed the detection of unwanted air demand using unsupervised k-means classification to distinguish between normal operating hours and non-operating periods. Unexpected air usage patterns during non-operating hours were identified and analyzed through histogram and heatmap techniques, enabling corrective measures that saved approximately 393 kWh weekly, equivalent to 10% of the compressor’s weekly energy consumption. The innovation of this study lies in the integration of model-based intelligence within an IoT system, enhancing real-time energy management capabilities and enabling continuous learning and improvements in operational efficiency. This work demonstrates how advanced IoT frameworks can bridge the gap between energy efficiency and practicality for smaller enterprises, fostering sustainable and cost-effective operations.