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Bayesian calibration as proposed by Kennedy and O’Hagan [22] has been increasingly applied to building energy models due to its ability to account for the discrepancy between observed values and model predictions. However, its application has been limited to calibration using monthly aggregated data because it is computationally inefficient when the dataset is large. This study focuses on improvements to the current implementation of Bayesian calibration to building energy simulation. This is achieved by: (1) using information theory to select a representative subset of the entire dataset for the calibration, and (2) using a more effective Markov chain Monte Carlo (MCMC) algorithm, the No-U-Turn Sampler (NUTS), which is an extension of Hamiltonian Monte Carlo (HMC) to explore the posterior distribution. The calibrated model was assessed by evaluating both accuracy and convergence. Application of the proposed method is demonstrated using two cases studies: (1) a TRNSYS model of a water-cooled chiller in a mixed-use building in Singapore, and (2) an EnergyPlus model of the cooling system of an office building in Pennsylvania, U.S.A. In both case studies, convergence was achieved for all parameters of the posterior distribution, with Gelman–Rubin statistics R ˆ within 1 ± 0.1. The coefficient of variation of the root mean squared error (CVRMSE) and normalized mean biased error (NMBE) were also within the thresholds set by ASHRAE Guideline 14 [1] . [ABSTRACT FROM AUTHOR]

Detection of occupant presence has been used extensively in built environments for applications such as demand-controlled ventilation and security. However, the ability to discern the actual number of people in a room is beyond the scope of most current sensing techniques. To address this issue, a complex environmental sensor network is deployed in the Robert L. Preger Intelligent Workplace (IW) at Carnegie Mellon University. The results indicate that there are significant correlations between measured environmental conditions and occupancy status. It is shown that an average of 83% accuracy on the occupancy number detection was achieved by Gaussian Mixture Model based Hidden Markov Models during testing periods. To illustrate the consequent energy impact based on the occupant behaviour detection (i.e. number and duration of occupancy) in the space, an EnergyPlus model of the IW with an assumed standard variable air volume (VAV) system is created. Simulations are conducted to compare the energy consumption consequences between a prescribed occupancy schedule according to the ASHRAE 90.1 base case with the predicted occupancy behaviour. The results show that energy saving of 18.5% can be achieved in the IW while maintaining indoor thermal comfort. [ABSTRACT FROM PUBLISHER]