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Building energy modeling and simulation is an effective approach to evaluate building performance and energy system operations to achieve higher building energy efficiency. The high-order building models can offer exceptional simulation capacity and accuracy, however, its high level of complexity does not allow it to directly work with the optimization algorithms and methods that require a complete differential-algebraic-equations-based mathematical description of the physical model. In order to fill in the gap, the study presents a systematic approach to develop and calibrate the reduced-order building models. A notable feature of the approach is its coupling with high-order building simulations in order to pre-process the input information and support the calibration of the reduced model. A case study on a representative office building shows that the developed reduced-order model can present acceptable simulation accuracy compared with high-order simulations and significantly reduce the modeling complexity. 

Thermal comfort and energy efficiency are always the two most significant objectives in HVAC operations. However, for conventional HVAC systems, the pursuit of high energy efficiency may be at the expense of satisfactory thermal comfort. Therefore, even if centralized HVAC systems nowadays have higher energy efficiency than before in office buildings, most of them cannot adapt the dynamic occupant behaviors or individual thermal comfort. In order to realize high energy efficiency while still maintain satisfactory thermal environment for occupants indoors, the integrated hybrid HVAC system has been developed for years such as task-ambient conditioning system. Moreover, the occupant-based HVAC control system such as human- in-the-loop has also been investigated so that the system can be adaptive based on occupant behaviors. However, most of research related to personalized air-conditioning system only focuses on field-study with limited scale (i.e. only one office room), this paper has proposed a co- simulation model in energyplus to simulate the hybrid cooling system with synthetic thermal comfort distributions based on global comfort database I&II. An optimization framework on cooling set-point is proposed with the objective of energy performance and the constraints of thermal comfort distribution developed by unsupervised Gaussian mixture model (GMM) clustering and kernel density estimation (KDE). The co-simulation results have illustrated that with the proposed optimization algorithm and the hybrid cooling system, HVAC demand power has decreased 5.3% on average with at least 90% of occupants feeling satisfied.