Open Source Integration for Sustainable Buildings: Validating a Low-Cost Computational Framework in a Subtropical Academic Environment

This study proposes a scalable cyber–physical system (CPS) framework utilizing a hierarchical five-layer architecture to enhance indoor environmental quality and energy efficiency. The methodology integrates a Random Forest-based predictive model trained on a 22-month longitudinal dataset (2024–2025) to separate climatic effects from occupancy-driven loads. This study prioritized the development of a high-precision and cost-effective monitoring architecture to address the persistent challenge of sustaining thermal comfort in subtropical academic laboratories. The proposed system achieved a validation mean absolute percentage error (MAPE) of 2.50%, indicating strong predictive reliability. Hardware expenditures were below USD 400, substantially reducing barriers to broader adoption. Field deployment confirmed an operational EUI of 188.6 kWh/m2·year, which is 28.5% lower than prevailing regional benchmarks, while consistently meeting stringent indoor air quality (IAQ) requirements. Additionally, simulation modules calibrated with the validated dataset indicated a further 15–20% reduction potential through the application of active control strategies. Collectively, these findings establish a transferable empirical reference for climate-responsive operational practice.