Enhancing building energy optimization efficiency: A performance analysis of simplification approaches

Building energy performance analysis often relies on detailed, computationally intensive modeling. Although efforts have been made to simplify energy modeling, applying these simplified models in simulation-based multi-objective optimization remains underexplored. This study evaluates the effectiveness of widely adopted simplification techniques for a mixed-use commercial building within a parallel NSGA-II optimization framework. Techniques such as thermal zone abstraction, material property approximation, geometric simplification, and idealization of Heating, Ventilation, and Air Conditioning (HVAC) systems were tested through progressively simplified scenarios. The findings demonstrate that simplified models can substantially reduce computational time compared to detailed models, especially when using parallel computing. For the detailed model, parallel optimization reduced computational time to 10.65 h, a 51 % improvement over the 21.65 h required for a single-threaded NSGA-II. Among the simplified models, the shoebox was the fastest, completing in 0.39 h but underestimating mean annual natural gas and electricity consumption by 11 % and 21 %, respectively, compared to the validated detailed model. The R-value model, though the most accurate with deviations under 2 %, achieved only a 3 % reduction in computational time. Based on space usage, the three-zone model balanced accuracy and efficiency by reducing the computational time by 40 %, with 7 % and 14 % underestimations for natural gas and electricity consumption, respectively. Additionally, a survey of energy modelers found that 42 % of participants achieved over 40 % time savings through geometric and HVAC simplifications, whereas material simplifications yielded only 5–10 % time savings for most respondents. These findings highlight the inherent trade-off between computational efficiency and predictive accuracy in building energy model simplifications. • Accuracy and speed of simplified models are assessed in an optimization process. • A parallel computing simulation-based optimization framework is presented. • Results highlight the impact of space usage in merging zone simplification. • prNSGA-II improved detailed model speed by 51 % over a single-threaded NSGA-II. • Shoebox model achieved 96 % time savings but had the highest energy deviation.