Operation mode-informed multi-objective optimization of thermally activated building energy systems with integrated renewable and storage technologies

Thermally activated building systems (TABSs) with integrated hybrid geothermal-solar energy generation and storage technologies offer enhanced energy performance and support flexible operation of buildings. Limited studies have investigated the integration of multi-energy complementary systems into TABS-based buildings for effective multi-energy coordination, and few investigations have jointly optimized decision variables of both TABS and the associated energy systems. This study aims to perform a multi-objective design optimization of the TABS with ground source heat pump-air source heat pump-photovoltaic-battery (GSHP-ASHP-PV-battery) systems based on the identification of daily heating and cooling operation modes. The non-dominated sorting genetic algorithm-II (NSGA-II) with the technique for order preference by similarity to an ideal solution (TOPSIS) was applied to identify the optimal case by maximizing thermal-environmental-economic performance. The binary logistic regression (BLR) analysis with non-hierarchical stepwise selection was used to determine the best-performing model to estimate the occurrence probability of system operation modes based on indoor and outdoor environmental parameters. The assessment results showed that the developed regression models at the best classification probability exhibited optimal performance in identifying heating and cooling activations. Moreover, compared to the baseline case, the optimal design achieved a 29.2% reduction in the unmet hour ratio and a 7.6% decrease in the imported carbon dioxide emissions, with an increase in the total annual cost by 4.4%. The optimal case also demonstrated positive impacts on grid stability, building energy autonomy, and operational flexibility. The proposed approach offers valuable guidance for optimizing system design to promote low-carbon development and sustainability.