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Occupancy is an important factor driving building performance. Static and homogeneous occupant schedules, commonly used in building performance simulation, contribute to issues such as performance gaps between simulated and measured energy use in buildings. Stochastic occupancy models have been recently developed and applied to better represent spatial and temporal diversity of occupants in buildings. However, there is very limited evaluation of the usability and accuracy of these models. This study used measured occupancy data from a real office building to evaluate the performance of an agent-based occupancy simulation model: the Occupancy Simulator. The occupancy patterns of various occupant types were first derived from the measured occupant schedule data using statistical analysis. Then the performance of the simulation model was evaluated and verified based on (1) whether the distribution of observed occupancy behavior patterns follows the theoretical ones included in the Occupancy Simulator, and (2) whether the simulator can reproduce a variety of occupancy patterns accurately. Results demonstrated the feasibility of applying the Occupancy Simulator to simulate a range of occupancy presence and movement behaviors for regular types of occupants in office buildings, and to generate stochastic occupant schedules at the room and individual occupant levels for building performance simulation. For future work, model validation is recommended, which includes collecting and using detailed interval occupancy data of all spaces in an office building to validate the simulated occupant schedules from the Occupancy Simulator.

High-density cities usually surfer deterioration of the urban built environment due to slow or stagnant air movement. This deterioration could be mitigated by improved urban natural ventilation through good urban planning and building design. However, indoor natural ventilation requirements in current building standards only define absolute indoor air speed without considering outdoor conditions. To fill this gap, this paper developed generic urban-scale coupled indoor and outdoor CFD models and proposes a new integrated index (CIOI index) to evaluate indoor natural ventilation potential, which couples both indoor and outdoor wind environments. CIOI v regression models are developed by applying machine learning algorithms based on 3,840 CFD simulation results. The Gradient Boosting (GB) model shows an improved performance in terms of higher correlation ( R 2 =0.8) and lower errors (MAPE=16%) compared with the multivariate linear regression model. Building designers and urban planners can use this CIOI prediction model to quickly check, in the early design stage, the influence of their design variations on indoor ventilation potential. [ABSTRACT FROM AUTHOR]

Purpose – This study aims to develop a web-based tool – LEED Energy Performance Online Submission Tool (LEPOST) to reduce the submission cost of the leadership in energy and environmental design (LEED) application process and facilitate green building design. Lifecycle cost reduction is a major driver for designing green buildings. LEED rating system has been well recognised and widely used in the green building industry. However, certification cost incurred in time and money is often a deterrent for some projects. Design/methodology/approach – LEPOST automatically maps EnergyPlus and eQUEST energy simulation results to the LEED energy performance requirement submission templates using an extensible markup language (XML) data structure. It incorporates the Energy Star Target Finder online engine and current utility data to calculate points required to assess LEED Energy and Atmosphere Prerequisite 2 and Credit 1 automatically. Findings – A comparative case study is conducted using an office building project. The study results show that the tool can reduce the amount of time for the LEED energy performance evaluation and submission process from more than 6 hours to 2 minutes. The total number of manual data entries is reduced from 442 to 20. Research limitations/implications – Future work includes the update to support LEED V4, the development of a parametric design function that can help design teams perform design alternatives to evaluate energy performance with minimum effort, and the integration with the LEED Online system. Practical implications – The use of the tool by the building industry may decrease the cost of LEED certification for building owners, developers and design teams by simplifying the submission process. Originality/value – The overall development framework of LEPOST contributes to the knowledge of the data interoperability in the building sector by demonstrating a viable solution to extract and map digital model information for achieving code and standard compliance purposes. [ABSTRACT FROM AUTHOR]

This study proposes a design–build–operate energy information modelling (DBO-EIM) infrastructure to allow users to deploy the design-stage building energy model for model predictive control (MPC) system in the building operation. A newly constructed office building is studied as a test bed. An EnergyPlus model-based predictive control (EPMPC) system is designed and simulated in the Matlab/Simulink environment within the DBO-EIM infrastructure. EPMPC aims at minimizing heating, ventilation, and air conditioning energy consumption while maintaining occupant thermal comfort. Compared to the baseline rule-based control system, EPMPC demonstrates a 28.9% energy reduction in one-week simulation in the heating season and 2.7% energy reduction in one-week simulation in the cooling season. The comfort constraint is met during more than 90% of the simulated hours. The study demonstrates one significant contribution of the DBO-EIM infrastructure that a design-stage EnergyPlus model can be integrated in an MPC system and the preliminary simulation results are satisfactory. [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]

This article focuses on the change in architecture and architectural engineering. There are both negative and positive drivers for curricular change in architecture and architectural engineering departments to embrace more fully systems integration for building performance. On the negative side, failures in indoor air quality, spatial flexibility, acoustics and building integrity represent increasing challenges for designers, leading to costly litigation and remediation, and undermining the reputation of professionals. Heightened performance goals, including environmental sustainability goals, as well as emerging materials, components and system innovations require a greater level of expertise and collaboration between architect, engineers, building scientists and other disciplines.

Presents an overview of the usage of performance-based simulation tools for building design and evaluation in Singapore. Details of the building design process; Discussion on design decision and building performance; Localization of performance simulation tools.

Focuses on a concept from the Total Building Performance (TBP) team that constitutes an integrated and holistic framework for conceptualizing, specifying, designing, analyzing and commissioning a building project. Background on a Develop and Build procurement contract for the National Library Board building awarded to the team; Details of principal performance mandates in the TBP model; Implementation of the concept in selective projects significant in Singapore.

Provides information on the impact of any act of construction on the habitability of the built environment for humans and other species.

• Reinforcement learning-based method to using a whole building energy model for HVAC optimal control. • Implementation and deployment of the method in an existing novel heating system (Mullion system) of an office building. • Building energy modeling for the Mullion system and sub-hourly multi-objective model calibration using the Bayesian method and genetic algorithm optimization. • 16.7% heating demand reduction in the real-life deployment with more than 95% probability compared to the old rule-based control. • Discussions on the practicability of the control method. Whole building energy model (BEM) is a physics-based modeling method for building energy simulation. It has been widely used in the building industry for code compliance, building design optimization, retrofit analysis, and other uses. Recent research also indicates its strong potential for the control of heating, ventilation and air-conditioning (HVAC) systems. However, its high-order nature and slow computational speed limit its practical application in real-time HVAC optimal control. Therefore, this study proposes a practical control framework (named BEM-DRL) that is based on deep reinforcement learning. The framework is implemented and assessed in a novel radiant heating system in an existing office building as a case study. The complete implementation process is presented in this study, including: building energy modeling for the novel heating system, multi-objective BEM calibration using the Bayesian method and the Genetic Algorithm, deep reinforcement learning training and simulation results evaluation, and control deployment. By analyzing the real-life control deployment data, it is found that BEM-DRL achieves 16.7% heating demand reduction with more than 95% probability compared to the old rule-based control. However, the framework still faces the practical challenges including building energy modeling of novel HVAC systems and multi-objective model calibration. Systematic study is also needed for the design of deep reinforcement learning training to provide a guideline for practitioners. [ABSTRACT FROM AUTHOR]