Report
IoT-Based Comfort Control and Fault Diagnostics System for Energy-Efficient Homes
19 Apr 2024
Abstract
This project studies an Internet of Things (IoT)-based comfort control and fault diagnostics system (referred as iComfort in this report) for energy-efficient homes. The system delivers an occupant-comfort-oriented thermal environment adaptive to fault scenarios and achieves HVAC energy savings in a cost-effective and straightforward way. This smart iComfort home system consists of the following key features. 1) Cost-effectiveness and scalability of the entire hardware and software system: The system includes low-cost temperature, humidity, and airflow sensors, and a Raspberry Pi-based local hub that interfaces with the cloud and IoT-enabled devices. The cost is low, not only for sensors, but also the costs associated with sensor installation, system setup and commissioning, data communication and storage, and data analytics (e.g., the development of automated fault detection and diagnosis (AFDD), as well as adaptive control strategies that are both computationally efficient and practical to implement). 2) Energy performance and user satisfaction: The system delivers user satisfaction and energy savings. This includes a) ease of use, b) optimal occupant thermal comfort, and c) accurate system feedback (e.g., low false alarm of AFDD strategies). 3) Favorable demonstrated prototype performance: The prototype tested at the Pacific Northwest National Laboratory (PNNL) Lab Homes demonstrates the accuracy of fault detections and diagnoses and shows thermal comfort improvement and energy savings through adaptive and optimal HVAC operations.
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Details
- Title
- IoT-Based Comfort Control and Fault Diagnostics System for Energy-Efficient Homes
- Creators
- Zheng O'Neill - Texas A & M Univ., College Station, TX (United States)] (ORCID:0000000288397174Tao Yang - Texas A & M Univ., College Station, TX (United States)Jin Wen - Drexel UniversityRichard Kimball - Drexel Univ., Philadelphia, PA (United States)Hai Xiao - Clemson UniversityNingxuan Wen - Clemson UniversityDanny Taasevigen - Pacific Northwest National LaboratoryTexas A & M Univ., College Station, TX (United States). Texas A & M Engineering Experiment Station
- Publisher
- United States
- Resource Type
- Report
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Identifiers
- 991021960804804721