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Occupant behavior (OB) is one of the significant sources of uncertainty in building performance simulation. While OB modeling has received increased attention in the past decade, research on the degree of granularity or level of detail (LoD) required for representing occupants is still in the nascent stages. This paper analyzes the modeling and applicability of three LoDs to represent occupants in building performance assessment. A medium-sized prototype office building located in Chicago, Illinois is used as the simulation case study. Ten occupant-centric attributes are adopted to develop the LoDs for OB representation. We first demonstrate the different modeling approaches required for simulating the three fidelity levels. Later, we illustrate the suitability of the developed LoDs in supporting six building performance use cases across different lifecycle stages. This study intends to provide guidance for the building simulation community on appropriate OB representation to support various use cases.

Abstract. The buildings sector in Indonesia is dominated by homes—the number of which hasbeen growing rapidly to meet population-driven demand. However, most buildings built over thepast four to five decades lack adequate natural ventilation and other passive cooling measuressuited to tropical climates and rely at least partly on air-conditioning. This study focuses on aparametric analysis of passive and low-energy “whole-building” cooling solutions for thetropical climate of Indonesia, evaluating the benefits of 12 passive or low-energy coolingsolutions: solar-reflective “cool” roofs, cool walls, insulated roofs, insulated walls, low thermal-emissivity (“low-E”) windows, solar-control window films, ceiling fans with temperaturesetback, ceiling fans without temperature setback, exterior window shades, interior windowshades, natural ventilation based on schedule and outside air temperature, and natural ventilationbased only on outside air temperature. Results indicate that a ceiling fan with thermostat setback(raising the cooling setpoint to 28.1 °C from 25 °C while maintaining constant thermal sensation)provided both the highest nationwide electricity savings and the highest net present value forhouseholds, followed by insulated walls, insulated roofs, and cool walls.

The Oakland EcoBlock is a whole-systems urban sustainability project that aims to prototype a pathway to affordable urban decarbonization by means of block-scale retrofitting that can reduce more than 50% of home energy usage. Considering the behavioral dimension of the energy retrofit process is key to understanding how household decision-making can enhance the adoption and performance of building efficiency measures – particularly in the EcoBlock, where low-to-moderate income constraints may limit accessibility to energy efficient upgrades. This study uses two survey instruments to explore key drivers of energy efficient technology adoption and operational behavior amongst EcoBlock residents.

The complexity of occupant behavior is one of the major contributors to uncertainty in building performance simulation. Agent-based modeling (ABM), a computational simulation technique, has gained attention in the occupant modeling field due to its capability and flexibility to capture the heterogeneity and dynamics of human behavior and the emergent effects. While multiple efforts in the past decade have demonstrated the usefulness of the ABM approach for simulating occupants and their impacts on building performance, several crucial matters in the ABM research still remain unexplored. This paper presents ten questions that highlight the most important issues regarding ABM research and applications for occupant behavior in the context of building performance simulation. The questions and answers aim to provide insights into current and future ABM research, and more importantly to inspire new significant questions from young researchers in the field. This research is part of the IEA EBC Annex 79 project, occupant-centric building design and operation.

Occupant behaviour simulation frameworks can employ synthetic populations to characterize occupancy and behavioural patterns in buildings based on observed  demographic data at a certain geographical location. For buildings, very few synthetic occupant populations have been generated. This paper uses a Bayesian Networks (BN) structural learning approach to synthesize populations of occupants in a multi-family housing case study. Two additional cases of office occupants and senior housing residents are considered as a cross-case comparison. We draw upon the extended version of drivers-needs-actions-systems (DNAS) framework to guide the selection of variables and data imputation. Our results show that the BN approach is powerful in learning the structure of data sets. The synthetic data sets successfully match the joint distributions of the underlying combined data sets. Experiments on the multi-family housing particularly show better  performance than the office and senior housing cases.

Since the introduction of the occupant behavior Drivers-Needs-Actions-Systems (DNAS) framework in 2013, researchers have used the framework or further developed it based on their case studies, which include efforts to collect new data on occupant behaviors. The effort is often costly for the relatively few new data points added. Problems emerge when the already collected data do not meet the modelers' interoperability requirements. Previous studies addressed this issue by developing more sophisticated ontologies that enable integration with other datasets and synthetic data methodologies that would meet unique research applications. This paper presents an extension of the DNAS framework for the representation of synthetic occupant data to support various applications and use cases across the building life cycle. An agent-based modeling application is one of our motivations that requires more elaborate characteristics of an occupant-agent or a group-of-agent. The extension, built upon a review of the literature, introduces new elements to the framework that fall into five categories, including socio-economic, geographical location, activities, subjective values, and individual and collective adaptive actions. On-going research includes identifying occupant datasets and developing data fusion methods to generate synthetic occupants, as well as to demonstrate its applications in agent-based modeling coupled with building performance simulation.