4.7 Article

Case study of an advanced integrated comfort control algorithm with cooling, ventilation, and humidification systems based on occupancy status

Journal

BUILDING AND ENVIRONMENT
Volume 133, Issue -, Pages 246-264

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.buildenv.2017.12.010

Keywords

Building energy; Integrated comfort control; Multinomial logistic regression; Occupancy detection algorithm; PIR sensors

Funding

  1. Korean Institute of Energy Technology Evaluation and Planning (KETEP)
  2. Ministry of Trade, Industry, and Energy (MOTIE) of the Republic of Korea [20142020103190]
  3. National Research Foundation of Korea (NRF) - Korea government [2015R1A2A2A01005761]
  4. Architecture & Urban Development Research Program - Ministry of Land, Infrastructure and Transport of Korean government [17AUDP-B079104-04, 17AUDP-B099686-03]

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In this study, we propose a new control algorithm based on occupancy status for indoor environmental devices to maintain thermal comfort while saving energy of residential buildings. The algorithm consists of two parts: an environmental data driven model for the occupancy status detection and an integrated comfort algorithm for operating the indoor devices. To detect the occupancy status, a multinomial logistic regression model is developed based on the data obtained from passive infrared (PIR) sensors, a door sensor, CO2 concentrations, and lighting electricity consumption. The occupancy status is categorized into three classes: away, active, and inactive (sleep). The new control algorithm integrates and operates air-conditioning, ventilation, and humidification systems by considering the outdoor environment, and ensuring indoor thermal comfort and energy savings at each occupancy status. The control algorithm is evaluated at different outdoor conditions with a testbed that consists of a variable refrigerant flow (VRF) air conditioning system, humidifier, and ventilation system. The performance of the proposed control algorithm is experimentally compared with that of a conventional individual control method in terms of the thermal comfort and energy consumption. The results show that the advanced integrated comfort control system improves the thermal comfort and decreases the energy consumption. Specifically, Case 1 (active, hot, and mild humidity) exhibits a 6.3% energy reduction and 64% improvement in the thermal comfort. Case 2 (active, hot, and low humidity) shows a 21% energy reduction and 72% improvement in the thermal comfort. Case 3 (inactive, comfort indoors) displays a 7% energy increase and 31% improvement in the thermal comfort. These results confirm that the proposed advanced integrated comfort control algorithm could be useful for designing better space environmental control to ensure thermal comfort and energy conservation.

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