Exploring Mega-Trend Diffusion Algorithms for Synthetizing Data Associated with Occupant-Building Interaction in IVEs

The utilization of immersive virtual environments (IVEs) has emerged as a pivotal tool in enhancing observation of occupant-building interaction (OBI) in non-existing and pre-operational buildings (e.g., buildings under-designed, renovated, and retrofitted). The data derived from IVEs are critical in developing Building Predictive Models (BPMs) that prioritize occupant comfort and optimize building performance. Nevertheless, a persistent challenge is the collection of sufficiently large sample sizes from IVEs, often resulting in data sets inadequate for creating accurate and dependable BPMs. To address the gap, the generation of synthetic data is one promising solution. Mega-trend diffusion (MTD) is particularly adept at managing the nuances of small, mixed-type, and imbalanced data sets aligning with the natures of the IVE data sets. This study explores MTD-based algorithms such as baseline MTD, baseline MTD with class probability function, and k-Nearest Neighbors MTD (kNNMTD), all of which are adept at addressing the inherent data challenges. Various small data sets associated with OBI in IVEs were used to test these algorithms. The fidelity of the synthetic data sets is assessed using the Pairwise Correlation Difference (PCD) and accuracy of Artificial Neural Networks (ANNs) trained on the synthetic data sets with several modeling structures. A variety of findings indicated strength and limitations of the algorithms, where some areas need further investigation. At this stage, the evaluation based on this study found that the kNNMTD produced synthetic data sets that were closest to the experimental data set (i.e., the smallest PCD), contributing to the most accurate ANN models.