• Energy Research

It is widely accepted that the prediction of building energy performance is strongly related to the occupancy parameters. Currently, existing buildings and laboratories are the main sources for collecting occupancy related data. However, using such data for predicting the energy consumption of future buildings can create a considerable amount of uncertainties. Recent studies show that Immersive Virtual Environments (IVEs) have the potential to generate design and context sensitive occupant-related data. However, extended observations (longitudinal data covering relevant spatial and temporal events) which are necessary for developing quantitative predictive models are impractical using conventional IVEs. To that end, the authors propose a Spatial-Temporal Event-Driven (STED) modeling approach to enable IVEs for longitudinal studies. Using a single occupant office as case study, two sets of occupancy and lighting data, from IVEs and a comparable physical environment (in-situ), were collected. The occupancy/lighting data was organized in form of state transitions at six events (i.e., arrival in the morning, leaving for and returning from a short leave, leaving for and returning from a long leave, and leaving at the end of a day). It was hypothesized that the probabilities of the occupancy/lighting state transitions in a given event across the two experimental environments (i.e. IVE vs. in-situ) are not statistically different. Results revealed similar patterns at four of the six events (α = 0.05), except at the short leave events. Thereby, STED modeling enabled the potential viability of IVEs for extended observations and generating data to support predictive models. Clearly, more basic research is needed to make data collection using IVEs more effective including a better understanding of virtual cue design and participant's physiological and psychological conditions at the time of experiments.

AbstractBuilding energy simulation plays an increasingly important role in building design and operation. This paper presents an open computing infrastructure, Virtual Information Fabric Infrastruc...

Abstract Building performance models (BPMs) such as building energy simulation models have been widely used in building design. Conventional BPMs may not be able to effectively address human- building interactions in new buildings still under design. The lack of such capability often contributes to the existence of building performance gaps, i.e., differences between predicted performance during design and actual performance of buildings. To improve the prediction accuracy of conventional BPMs, a computational framework is developed. It combines an existing BPM with context-aware design-specific data involving human-building interactions in new designs, using a machine learning approach. Immersive virtual environment (IVE) is used to capture data describing design-specific human-building interactions; and an artificial neural network (ANN) is used to combine data obtained from an existing BPM and an IVE to produce an augmented BPM. Additionally, the framework has the capability to rank influence of factors impacting human-building interactions using a feature ranking technique, which can help the design of future IVE experiments for better data collection. The framework is tested using an application of a single occupancy office. An IVE of the office is created to simulate key artificial light use events during design. The Hunt model is selected as an existing BPM. The actual use of artificial lighting in the office is observed for one month using sensors to validate the effectiveness of the framework. The results of the application have shown the potential of the framework in improving the prediction accuracy of the Hunt model evaluated against data obtained from the actual office. The results verify the important role of context-aware design-specific data in improving the prediction of human-building interactions during design. In addition, the feature ranking technique is effective in identifying influencing factors impacting human-building interactions. Limitations of this study and future work are also discussed.

Abstract Occupant energy behavior is a major factor affecting the energy performance of buildings, but its impact is difficult to predict during design. Although a significant amount of research has been done based on empirical and lab experiments, the performance gap of buildings, i.e., the design energy performance vs. the actual energy performance of buildings, still exists. Immersive virtual environments (IVEs) offer a unique opportunity and alternative for studying occupant energy behavior because of its potential to provide realistic virtual experiences to participants and elicit their behavioral responses. The objective of this study is to perform a comprehensive literature review to understand the potential and challenges of IVE applications to occupant energy behavior studies. The review covers research in both occupant energy behavior and IVE applications. By matching IVE capabilities with factors of the Drivers-Needs-Actions-Systems (DNAs) framework and the needs of occupant energy behavior studies, the authors found that IVE applications vary depending on IVE's technical maturity to handle DNAs factors, which can be classified into three categories; and that current IVE applications are centered on validating IVEs for occupant behavior studies and understanding behaviors in IVEs. Future research is needed to improve strategies for data generation, behavior and sensation modeling, prediction, and validation, as well as the creation of virtual experiences with multiple sensory inputs and social presence.

The overarching goal of this paper is to study if certain contextual factors out of the variables that a researcher is interested in has causal influence on human-building interaction for a building under design. To limit the scope of the study, we concentrate on causal analysis of light switch behavior (i.e., an occupant switching on or off a light) in office buildings. Due to the lack of high-fidelity in data obtained from empirical and lab experiments, we relied on Stated Choice Experiments (SCEs) using Immersive Virtual Environments (IVEs) to collect data on human-light switch interaction. IVEs have shown to have the capability to render realistic virtual experiences to subjects and evoke responses from them for data collection. As far as we know, existing literature taking into account the causal effects of different contextual factors on light switch behavior is limited. The study enhances the knowledge on the various factors influencing the use of office lighting systems by occupants’ and provides quantitative evidence and reference for building designers to design comfort environments for occupants with low energy footprint.

Abstract Geothermal energy is widely considered as an ideal alternative energy for building operations. Despite the fact that ground source heat pumps (GSHPs) have been studied for decades, including economic analyses such as life cycle costing, their technical and economic applicability in hot and humid climate such as FL, USA is yet to be determined. In the past, many life cycle cost analyses on GSHPs were conducted using a deterministic method to derive point estimates. In many cases, data were assumed, but data uncertainties were not accounted for. In this study, a comparison of the deterministic and the probabilistic method was performed in order to understand the impact of data uncertainties on results of analyses. The probabilistic life cycle cost analysis was based on Monte Carlo simulation. A GSHP application case in Pensacola, FL was selected for the comparison. Some data were collected through site visits or interviews, while others were gathered from the literature or published data. Probability distribution functions used by Monte Carlo simulation were derived based on historical data or assumptions. Results of the case study from both deterministic and probabilistic methods confirmed that the GSHP option was economically more favorable than a conventional single zone split system using heat pumps, but with a long payback time if incentives were not considered. The probabilistic method was found to deliver a more reliable conclusion with more critical information than the deterministic method. However, further studies were needed to verify those initial observations obtained from this case study.

This paper contributes an inclusive review of scientific studies in the field of sustainable human building ecosystems (SHBEs). Reducing energy consumption by making buildings more energy efficient has been touted as an easily attainable approach to promoting carbon-neutral energy societies. Yet, despite significant progress in research and technology development, for new buildings, as energy codes are getting more stringent, more and more technologies, e.g., LED lighting, VRF systems, smart plugs, occupancy-based controls, are used. Nevertheless, the adoption of energy efficient measures in buildings is still limited in the larger context of the developing countries and middle income/low-income population. The objective of Sustainable Human Building Ecosystem Research Coordination Network (SHBE-RCN) is to expand synergistic investigative podium in order to subdue barriers in engineering, architectural design, social and economic perspectives that hinder wider application, adoption and subsequent performance of sustainable building solutions by recognizing the essential role of human behaviors within building-scale ecosystems. Expected long-term outcomes of SHBE-RCN are collaborative ideas for transformative technologies, designs and methods of adoption for future design, construction and operation of sustainable buildings.

Abstract Traditional life cycle assessment (LCA) methods are used to conduct building environmental impact assessment (EIA) with little consideration of influential factors that vary in time and of variation in occupancy behaviors. Because the life cycle of a building is quite long, such details have significant influence on the accuracy of evaluation results. To fill in this gap and extend the LCA system, this paper developed a dynamic assessment framework based on LCA principles after reviewing the research progress of DLCA (dynamic LCA). The new framework identified four dynamic building properties (i.e., technological progress, variation in occupancy behavior, dynamic characteristic factors, and dynamic weighting factors) and considered them in corresponding assessment steps to realize real-time EIA. In addition, residential occupancy profiles were described at personal level, family level, and social level; and three potential quantification methods were introduced to explore the relationship between occupancy profiles and household energy consumption. The DLCA framework expands the connotation of the LCA system from a dynamic perspective, making it possible to present time-varying EIs of buildings over their long life cycle and guide occupancy behavior in time. This framework has the potential to be base for developing a useful tool for conducting forecast evaluation and promoting sustainability.