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• A novel agent-based model (ABM) was developed to explore occupant behaviors in the context of built environment. • Relevant data was collected using customized sensor nodes and paper-based survey in a test bed building. • A validation study was conducted to test the occupant behavior model with the methods of visualization and calculation of quantification metrics for performance evaluation. • The proposed research framework has the potential to improve both building energy efficiency and occupant comfort during the life cycle of a building. Occupant behaviors are one of the dominant factors that influence building energy use. Traditional building energy modeling programs use typical occupant schedules that often do not reflect actual situations. Robust occupant behavior modeling that seamlessly integrates with building energy models will not only improve simulation performance, but also provide a deeper understanding of occupant behaviors in buildings. This paper presents a development and validation approach to a novel occupant behavior model in commercial buildings. A robust agent-based modeling (ABM) tool, namely Performance Moderator Functions server (PMFserv), is used as the basis of the occupant behavior model. The ABM considers various occupant perceptions and interactions with window, door, and window-blinds based on the environmental conditions. An elaborate agent-based model that represents an office space in an existing building is developed. This is followed by a validation study of the ABM through the use of embedded sensors that capture the indoor ambient conditions and a survey to record actual occupant behaviors. By comparing the recorded behavior data with ABM output, this paper discusses the proposed ABM's prediction ability, limitations, and extensibility. Finally, the paper concludes with the potential of integrating the occupant behavior model with building energy simulation programs. [ABSTRACT FROM AUTHOR]

This paper introduces a Human and Building Interaction Toolkit (HABIT) for simulating the thermally adaptive behaviors and comfort of office occupants alongside building energy consumption. The toolkit uses the Building Controls Virtual Test Bed (BCVTB) to co-simulate a field-tested, agent-based behavior model with an EnergyPlus medium office model. The usefulness of the toolkit is demonstrated through a series of zone and building-level case study simulations that examine the wisdom of pairing local heating and cooling options with strategic thermostat set point offsets, judging from the energy, Indoor Environmental Quality (IEQ), and cost perspectives. Results generally suggest that trading efficient local heating/cooling options for whole space conditioning has both energy and comfort benefits, saving up to 28% of monthly HVAC energy while improving the acceptability of thermal conditions in a Philadelphia climate. Nevertheless, cost analysis shows that the fuel source of conserved energy must be considered – particularly in the case of personal heater use, which adds to electric plug loads and associated utility and CO 2 emissions cost penalties. Moreover, costs from even small changes in simulated occupant productivity tend to overwhelm energy costs, suggesting the need to improve the accuracy and precision of available productivity models across multiple seasons and climates. [ABSTRACT FROM AUTHOR]

The economic and environmental benefits of building retrofits have been acknowledged. However, there is a series of barriers that threaten to impede implementing successful retrofit projects such as: lack of funding, lack of interoperability, and unstructured decision making. This paper aims to address these barriers by providing the framework of the Virtual Retrofit Model (VRM), an affordable computational platform that supports streamlined decision making for building retrofit projects. An occupant survey was implemented to identify the primary requirements and perceptions from different types of stakeholders of the buildings. The responses were analyzed to identify the most important criteria of the future retrofit projects to focus on if it were to be renovated in the future. A case study approach was used to describe the outcomes from a year-long demonstration project that has been conducted at an aging commercial building. The research activities focused on integrating theories and technologies of Building Information Modeling (BIM), energy simulation, agent-based modeling, multi-criteria decision support system, and software application that can be employed and adopted in building retrofit projects. The software prototype is designed to connect buildings to a smart grid environment where building energy data should be shared for intelligent decision making. [ABSTRACT FROM AUTHOR]

Buildings are responsible for a large share of the energy demand and greenhouse gas (GHG) emissions in Europe and Switzerland. Bottom-up building stock models (BSMs) can be used to assess policy measures and strategies based on a quantitative assessment of energy demand and GHG emissions in the building stock over time. Recent developments in BSM-related research have focused on modeling the status quo of the stock and comparatively little focus has been given to improving the modeling methods in terms of stock dynamics. This paper presents a BSM based on an agent-based modeling approach (ABBSM) that models stock development in terms of new construction, retrofit and replacement by modeling individual decisions on the building level. The model was implemented for the residential building stock of Switzerland and results show that it can effectively reproduce the past development of the stock from 2000 to 2017 based on the changes in policy, energy prices, and costs. ABBSM improves on current modeling practice by accounting for heterogeneity in the building stock and its effect on uptake of retrofit and renewable heating systems and by incorporating both regulatory or financial policy measures as well as other driving and restricting factors (costs, energy prices). [ABSTRACT FROM AUTHOR]