An adaptive indoor temperature control approach simultaneously improving thermal comfort and task performance

Air conditioning and mechanical ventilation systems help maintain the thermal comfort (TC) of occupants in a workplace. Indoor temperature determination that only aims to improve TC causes difficulty in fulfilling the occupant's task performance (TP). It is crucial to find an appropriate indoor temperature range that simultaneously accommodates occupants' TC and TP. In this study, we propose an adaptive temperature control approach that simultaneously improves the occupant's TC and TP. The relationship among the dissatisfaction rate, predictive mean vote, and indoor temperature was established and quantified. An investigation in a human-robot collaboration scenario was conducted by using the proposed approach. The preferable temperature range under various combinations of TC and TP is recommended. The proposed algorithm successfully achieves a significant energy-saving of 9.98% within the desired temperature range. By reducing the preferred upper limit of the temperature range by 3 °C compared to current standards, the algorithm further reduces energy consumption. To validate the effectiveness of the proposed model, we developed empirical formulas that establish the relationship between thermal comfort, task performance, and temperature, enabling the identification of optimal indoor temperature settings. Additionally, our model effectively captures the complex trade-offs and complementary nature between thermal comfort and task performance with respect to indoor temperature. Overall, this approach offers a solution for setting indoor temperatures in the workplace.