주거 건물의 거주자 재실기반 바닥난방 제어방안

Abstract

Occupancy-based heating control is effective in reducing heating energy by preventing unnecessary heating during unoccupied period. Various technologies on detecting human occupancy have been developed using complicated machine learning algorithm and stochastic methodologies. This study aims at deriving low-cost and simple algorithm of occupancy inference that can be implemented to residential buildings. It also aims at finding the optimum start/stop time of floor heating system based on the occupant's standard schedule. The core concept of the algorithm is to combine the occupancy probabilities based on indoor CO2 concentration and motion sensor data. The probability was estimated by applying different levels of decrement ratio depending on CO2 concentration change rate and aggregated motion sensor signals. The developed algorithm was validated by comparing the inference results with the occupancy schedule in a real residential building. The results showed that the inference algorithm can achieve the accuracy of 75~99%, which would be successfully implemented to the control of residential heating systems. Control schedules for floor heating system determined were using the occupant's standard schedule. Designbuilder software was used to analyze the optimal start/stop time of heating control that satisfies thermal comfort of residents. The results showed that if residents shut off their heating 6 hours before going out and start heating 4 hours before return, they could save 14.2% on heating energy while satisfying comfort.