Artificial intelligence (AI) technology has rapidly advanced and transformed the nature of scientific inquiry. The recent release of the large language model Chat Generative Pre-Trained Transformer (ChatGPT) has attracted significant attention from the public and various industries. This study applied ChatGPT to autonomous building system operations, specifically coupling it with an EnergyPlus reference office building simulation model. The operational objective was to minimize the energy use of the building systems, including four air-handling units, two chillers, a cooling tower, and two pumps, while ensuring that indoor CO₂ concentrations remain below 1000 ppm. The performance of ChatGPT in an autonomous operation was compared with control results based on a deep Q-network (DQN), which is a reinforcement learning method. The ChatGPT and DQN lowered the total energy use by 16.8% and 24.1%, respectively, compared with the baseline operation, while maintaining an indoor CO₂ concentration below 1000 ppm. Notably, compared with the DQN, ChatGPT-based control does not require a learning process to develop intelligence for building control. In real-world applications, the high generalization capabilities of the ChatGPT-based control, resulting from its extensive training on vast and diverse data, could potentially make it more effective.

Indoor CO₂ concentration is considered a metric of indoor air quality that affects the health of occupants. In this study, a hybrid model was developed for forecasting the varying indoor CO₂ concentration levels in a residential apartment unit in the presence of occupants by controlling the ventilation rates of a heat recovery ventilator. In this model, the mass balance equation for a single zone as a white-box model was combined with a Bayesian neural network (BNN) as a black box model. During the learning process of the hybrid model, the BNN estimated an aggregated unknown ventilation rate and transferred the estimation to the mass-balance equation. A parametric study was conducted by changing the prediction horizons of the hybrid model from 5 to 15 min, and the forecasting performance of the hybrid model was compared with the stand-alone mass balance equation. The hybrid model showed better forecasting performance than that of the mass balance equation on the experimental dataset for a living room and bedroom. The average MBE and CVRMSE of the hybrid model for the prediction horizon of 15 min were 0.65% and 5.23%, respectively, whereas those of the mass balance equation were 0.99% and 9.30%, respectively.

The building sector is responsible for more than one-third of total global energy consumption; hence, increasingly efficient energy use in this sector will contribute to achieving carbon neutrality. Most existing building-energy-benchmarking methods evaluate building energy performance based on total energy use intensity; however, energy usage in buildings varies with the seasons, and as such, this approach renders the evaluation of cooling and heating energy difficult. In this study, an information gain-based temporal segmentation (IGTS) method was used to identify the seasonal transition times based on patterns of hourly weather and corresponding building energy use. Twelve commercial buildings were considered for the study and four seasons were identified using IGTS; base-load, cooling energy, and heating energy data were gathered. For the 12 buildings, the estimated and measured heating and cooling energy during the summer and winter periods showed a linear relationship (R² = 0.976), and the average of those differences was 9.07 kWh/m². In addition, differences in the benchmarking results based on these energies were marginal. The results indicated that the IGTS approach can be effectively used for determining the actual heating and cooling energy consumption in buildings, as well as for energy benchmarking. This can, in turn, improve building energy use, with positive implications for achieving carbon neutrality.