About
To contribute to this goal, this project mains to develop and demonstrate a scalable and low-cost Internet of Things (IoT) based Comfort Control and Fault Diagnostics System for Energy Efficient Homes. It is a cost-effective and easy-to-use smart home system that delivers an occupant-comfort-oriented thermal environment, with at least 10% HVAC energy savings compared to the baseline control framework.
Overall Objectives
- Develop and evaluate a low-cost wireless distributed sensor system that includes low-cost temperature, humidity, and airflow sensors, and a Raspberry Pi-based local hub that interfaces;
- Develop and validate automated fault detection and diagnostics (AFDD) strategies for heating, ventilation and air conditioning (HVAC) systems utilizing the distributed and low-cost sensor system;
- Develop and validate a smart home energy management system to optimize the building operation for balanced energy efficiency and occupant comfort by integrating the distributed sensor system, IoT enabled devices, AFDD, and adaptive model-based controls;
- Develop the performance metrics and targets, and test procedures for the developed technology (including data collection requirements), and validate the developed technology (hardware and software) by testing a prototype to evaluate the technology’s performance relative to baseline performance metrics and targets;
- Develop strategies to seamlessly transfer the developed technologies to Building America partners and stakeholders.
Recent News
- 2019 Aug. Our DOE Building America Project was officially launched!
Principal Investigators
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Zheng O’Neill, Texas A&M University Dr. O’Neill is an Associate Professor at J. Mike Walker ’66 Department of Mechanical Engineering at Texas A&M University at College Station. She is a registered Professional Engineer and has more than 20 years of experience in building technology covering integrated building energy and control systems design, modeling and optimization, building commissioning, and low- energy/net-zero energy buildings. She has led or been involved with proposals leading to external research awards totaling over $15 million since 2014, and has over 100 journal and conference papers published. |
Jin Wen, Drexel University Dr. Wen is a Professor in the Department of Civil, Architectural, and Environmental Engineering at Drexel University. She has more than 20 years of experience in building control, operation, AFDD, indoor air quality, and occupant behavior simulation areas and has published more than 40 journal papers in these areas. She has served as an energy consultant for Philadelphia Housing Authority for three years and has provided policy and technical advice for their low and high rise residential buildings. |
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Hai Xiao, Clemson University Dr. Xiao is the S.L. Bell Distinguished Professor of Electrical and Computer Engineering at Clemson University. He is an internationally recognized expert in sensors, measurement and instrumentation, advanced manufacturing, and biomedical imaging. He has many years of successful experience in design, fabrication, and testing novel sensors for various applications, including the recent invention of the all-digital wireless sensing technology that will be further researched in this project to enable the IoT based comfort control and fault detection and diagnostics system for residential houses. |
Danny Taasevigen, Pacific Northwest National Laboratory Mr. Taasevigen is a Mechanical Engineer and Technical Team Lead within the Buildings and Connected Systems Group at PNNL. His experience is in reducing energy consumption in new and existing buildings through the research and application of advanced control strategies. He is also involved in AFDD research projects for small commercial buildings served by packaged air conditioners and heat pumps. He has extensive experience in developing and executing lab testing and developing performance metrics to validate test results compared to baseline operations. |
Publications
- Yang, Tao, Zheng O’Neill, Arkasama Bandyopadhyay, Jin Wen, and Austin Rogers. “Comparisons of Rule-Based Fault Detection and Diagnostic Methods for Residential Vapor Compression Cycle Systems.” ASHRAE Transactions 128 (2022).
- Yang, Tao, YangYang Fu, Zheng O’Neill, Rich Kimball, and Jin Wen. “An Adaptive Model-Predictive Control Informed Rule-based Control for Residential Cooling Operations under Extreme Weather Events.” ASHRAE Transactions 129 (2023).
- Chen, Zhelun, Zheng O’Neill, Jin Wen, Ojas Pradhan, Tao Yang, Xing Lu, Guanjing Lin et al. “A review of data-driven fault detection and diagnostics for building HVAC systems.” Applied Energy 339 (2023): 121030.
- Yang, Tao, Arkasama Bandyopadhyay, Zheng O’Neill, Jin Wen, and Bing Dong. “From occupants to occupants: A review of the occupant information understanding for building HVAC occupant-centric control.” In Building simulation, vol. 15, no. 6, pp. 913-932. Beijing: Tsinghua University Press, 2022.
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