Monday, June 26, 2017: 8:00 AM-9:30 AM
Research Summit
Chair:
Vikrant Aute, Ph.D., University of Maryland
Accurate predictive occupancy data is crucial when using occupancy as one of the data points in energy modeling for buildings and for use by building operators to optimize day-to-day and hour-to-hour operations. This session provides information on why occupancy is important in predicting energy loads and different methods of determining occupancy which can then be used to develop more accurate modeling formats.
1 Sizing Methodology for Domestic How Water Systems Based on Accurate Occupant Behavior (LB-17-C019)
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Jean Rouleau, Université Laval
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Louis Gosselin, Ph.D., P.E., Université Laval
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Alfonso Ramallo-Gonzalez, University of Bath
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As households are being required to be more energy efficient over the years, the energy consumption for producing domestic hot water (DHW) is receiving increasing attention. Hence, the design of hot water systems is becoming more important for a holistic approach to energy conservation. Current sizing for these systems is often based on estimations that obey empirical rules. An inaccurate evaluation of the hot water demand could lead to a poor hot water system design that is either undersized or oversized. This either means an insufficient amount of hot water available to occupants or an overpriced system that never gets to be used at its optimal operational point. Therefore, it is crucial to properly evaluate the hot water demand when designing hot water systems for dwellings. This paper discusses a stochastic tool constructed to generate hot water demand profiles for residential buildings using a 10-minute resolution.
2 Modelling Residential Building Stock Heating Load Demand, Integration of Occupancy Models (LB-17-C020)
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Giuseppina Buttitta, University College of Dublin (UCD)
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William Turner, University College of Dublin (UCD)
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Donal Finn, University College of Dublin (UCD)
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Oliver Neu, University College of Dublin (UCD)
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In the residential housing sector, a strong correlation exists between occupant behavior and space heating energy use. In particular, the occupancy scenario (e.g., daytime absence, morning presence, etc.) has a significant influence on residential heating load profiles, as well as on cumulative heating energy consumption. The share of households characterized by different occupancy scenarios is a crucial assumption in order to accurately model the residential building stock heating demand. The choice of the most suitable occupancy model is a trade-off between complexity, accuracy and computational effort, as well as model integration at large scale. This paper analyzes the combined influence of different occupancy assumptions and different occupancy models on the housing heating loads for a UK building stock sample.
3 Development and Comparison of Four Different Occupancy Counting and Estimation Solutions (LB-17-C021)
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Junjing Yang, Ph.D., National University of Singapore
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Alexandros Pantazaras, National University of Singapore
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Arun Kumar Chandran, Ph.D., National University of Singapore
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Siew Eang Lee, Ph.D., National University of Singapore
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Mat Santamouris, Ph.D., UNSW
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Kwok Wai Tham, Ph.D., National University of Singapore
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Lawrence Wong, National University of Singapore
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Occupancy information is important to building facility managers in terms of predictive control, safety, as well as the indoor environment quality. Previous works have addressed different occupancy counting and estimation solutions in different buildings or spaces. In this paper, we build up a test bed using the existing University lecture theatres to develop and compare four different occupancy counting methodologies. The paper addresses the occupancy counting challenge in educational building deployment scenario with large groups of people entering and leaving. Experiments have been conducted for three months with every 5 minutes data reporting interval. The results will be compared with the ground truth of real time pictures.
4 A Low-Cost Bi-Directional People Counter for Building Control (LB-17-C022)
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Peng Yin, Ph.D., University of Louisiana at Lafayette
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Xiaoguang Xiao, University of Louisiana at Lafayette
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Accurate occupancy information is crucial to the demand response HVAC control. However, traditional passive infrared (PIR) occupancy sensors can only provide binary results of occupant presence without detecting the number of people in a room. In this paper, a low-cost bi-directional people counter was developed to determine the occupant number in a single entry room by detecting people entering or leaving the room. The developed people counter was designed to be installed in the doorway with a capability of recording the time and the number of people entering, leaving, and staying in the room separately. A field evaluation of the developed people counter was conducted in a single entry student lounge in a period of two weeks.
