• Novel optimal adaptive controllers dynamically adjust the radiators’ performance. • The model is applied and analyzed in a newly built commercial building in Sweden. • Solar dominates in warmth while occupancy gain and ventilation in cold months. • The temperature deviation and comfort consistency are improved by 72% and 54%. • Adaptive controllers reduce radiators’ heat use and CO 2 emissions by 13% and 9%. The present study introduces and thoroughly investigates a novel smart heating, ventilation, and air conditioning system with thermal storage in a newly built commercial building in Uppsala, Sweden. The system combines 25 double U-tube borehole thermal energy storage, district heating, and intelligent control strategies to effectively manage heating and cooling demands for offices and restaurants. A novel optimal adaptive control framework dynamically adjusts the radiator supply temperature by accounting for solar radiation, ventilation flow rate, occupancy gains, and outdoor temperature. These modifications are optimized using the particle swarm method to enhance thermal comfort and energy efficiency. The proposed framework is compared with the existing control system based solely on outdoor temperature from techno-economic, environmental, and comfort aspects. According to the results, the outdoor temperature history and wind velocity have minimal effects on heating demand deviations, while solar radiation, occupancy gains, and ventilation performance play significant roles. The results further indicfate that solar radiation is the most influential factor in warmer months, whereas occupancy and ventilation gain are more important in colder months. Results demonstrate substantial enhancements in thermal comfort, with the weighted temperature deviation index reduced by 72.7% and the comfort consistency ratio increased by 54.4%. The designed adaptive controller reduces the annual heating supplied to radiators and the payback period by 13.2% and 9.0%, respectively, and decreases CO 2 emissions and the index by 9.4% and 2.6%, respectively. After 20 years, the adaptive controller outperforms the basic model in terms of profit, increasing it by 20.4% to 190,260 USD, proving its economic superiority in the long run. In transitional months like April (14.9 MWh, 56.3% of the total) and May (15.9 MWh, 69.9%), when efficient solar gains reduce heating demands, the suggested adaptive controller also has substantial monthly energy savings.
Behzadi et al. (Mon,) studied this question.