Abstract
Demand-driven HVAC (heating, ventilation, and air conditioning) operation is essential in occupant-oriented smart buildings, where the levels of heating, cooling, and ventilation are intelligently regulated to avoid energy waste. Despite the great potential of building energy efficiency, one of the remaining technical challenges is how to accurately estimate building occupancy information in real time. In this paper, this design challenge is addressed. An advanced audio-processing technique is adopted that minimizes the impacts of environmental sounds on the recorded voice sounds of humans. Adopted mathematical modeling and signal processing procedures are elaborated in this work. Experimental studies show that our proposed audio processing with background sound cancellation algorithm improves the estimation accuracy of room occupancy quantity by approximately 11–12%, which results in an averaged ventilation energy reduction of 3.54% compared to the case of not applying background sound cancellation. The proposed audio-processing technique is promising to achieve non-intrusive, cost-effective, robust, and accurate solutions for building occupancy estimation.
Highlights
According to U.S Energy Information Administration (EIA) statistics, more than 39% of carbon dioxide and 70% of electricity in the United States are consumed by buildings
Experimental studies show that our proposed audio processing with background sound cancellation algorithm improves the estimation accuracy of room occupancy quantity by approximately 11–12%, which results in an averaged ventilation energy reduction of 3.54% compared to the case of not applying background sound cancellation
Simulation results hourly ventilation electricity for Withthe theadoption adoptionofofvarious variousinformation informationtechnologies technologiesininnext-generation next-generationsmart smartbuildings, buildings, demand-driven building operation is very attractive for reducing energy consumption in buildings
Summary
According to U.S Energy Information Administration (EIA) statistics, more than 39% of carbon dioxide and 70% of electricity in the United States are consumed by buildings. These requirements in [10] include user-transparency, high accuracy, low failure rate, easy maintenance, low complexity, good privacy protection, and low price It is expected in [10] that the development of future occupancy recognition and counting sensors will lead to drastic improvements in the way that HVAC systems operate in buildings. It is necessary to further improve audio-processing algorithms to suppress outdoor noises and to maintain indoor human sounds as the main acoustic signal for occupancy extraction This is the focus of this research. To deal with this challenge of background sound interference, a background sound-cancellation algorithm is studied and adopted in this work to enhance the impacts of human speech during acoustic-driven occupancy estimation. 11–12% in 10 typical noise environments, which results in a reduction of 3.54% in ventilation energy in a case study of building energy simulation
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
