Optimal heating start and stop control based on the inferred occupancy schedule in a household with radiant floor heating system

Abstract

Occupancy-based heating control is important for not only achieving thermal comfort but also reducing the heating energy consumption of residential buildings. In this study, an occupancy inference method was developed for the optimal start and stop control of radiant floor heating systems. The basic concept of the inference is to combine the occupancy probabilities based on the indoor CO2 concentration and passive infrared (PIR) signals. The probability is estimated by applying different probability reduction ratios depending on the derivative of the CO2 concentration and aggregated PIR signals. In a validation experiment, the results showed that the developed algorithm can predict the occupancy with an accuracy of 83.5%–98.9%. The inferred occupancy data were used to determine the optimal heating start and stop control to improve energy saving performance and thermal comfort. Parametric simulations were conducted with jEPlus to find the optimal start and stop times. This optimal start and stop control was found to reduce the heating energy consumption by up to 3.1% and thermal discomfort times from 62.5 h to 8.3 h.