The current practice with building energy simulation software tools requires the manual entry of a large list of detailed inputs pertaining to the building characteristics, geographical region, schedule of operation, end users, occupancy, control aspects, and more. While these software tools allow the evaluation of the energy consumption of a building with various combinations of building parameters, with the manual information entry and considering the large number of parameters related to building design and operation, global optimization is extremely challenging. In the present paper, a novel approach is developed for the global optimization of building energy models (BEMs) using Python EnergyPlus. A Python-based script is developed to automate the data entry into the building energy modeling tool (EnergyPlus) and numerous possible designs that cover the desired ranges of multiple variables are simulated. The resulting datasets are then used to establish a surrogate BEM using an artificial neural network (ANN) which is optimized through two different approaches, including Bayesian optimization and a genetic algorithm. To demonstrate the proposed approach, a case study is performed for a building on the campus of the Florida Institute of Technology, located in Melbourne, FL, USA. Eight parameters are selected and 200 variations of them are supplied to EnergyPlus, and the produced results from the simulations are used to train an ANN-based surrogate model. The surrogate model achieved a maximum of 90% R2 through hyperparameter tuning. The two optimization approaches, including the genetic algorithm and the Bayesian method, were applied to the surrogate model, and the optimal designs achieved annual energy consumptions of 11.3 MWh and 12.7 MWh, respectively. It was shown that the approach presented bridges between the physics-based building energy models and the strong optimization tools available in Python, which can allow the achievement of global optimization in a computationally efficient fashion.

The present paper investigates the performance of a thermoelectric (TE)-based radiant ceiling panel with an additional layer of phase change material (PCM) for building cooling application through numerical and experimental analyses. The design of the ceiling panel consisted of an aluminum sheet with TE modules installed on the back to maintain a relatively low ceiling temperature that provided cooling through radiation and convection. A three-dimensional model was developed in COMSOL Multiphysics, and the system’s performance in several different configurations was assessed. The effect of the number of TE modules, as well as incorporating different amounts of PCM under transient conditions, was investigated for two modes of operation: startup and shutdown. It was shown that for a 609.6 mm × 609.6 mm ceiling panel, the use of four TE modules reduced the average surface temperature down to the comfort range in less than 5 min while producing a relatively uniform temperature distribution across the ceiling panel. It was also shown that the addition of a 2 mm thick PCM layer to the back of the ceiling panel enhanced the system’s performance by elongating the time that it took for the ceiling panel’s temperature to exceed the comfort range when the system shut down, which in turn reduced the number of on/off cycling of the system. The numerical results demonstrated a good agreement with the experimental data. The results from this study can be used for the optimal design of a TE-based radiant ceiling cooling system as a promising technology for smart buildings.