Building performance discrepancies between building design and operation are one of the causes that lead many new designs fail to achieve their goals and objectives. A main factor contributing to the discrepancy is occupant behaviors. Occupants responding to a new design are influenced by several factors. Existing building performance models (BPMs) ignore or partially address those factors (called contextual factors) while developing BPMs. To potentially reduce the discrepancies and improve the prediction accuracy of BPMs, this paper proposes a computational framework for learning mixture models by using Generative Adversarial Networks (GANs) that appropriately combining existing BPMs with knowledge on occupant behaviors to contextual factors in new designs. Immersive virtual environments (IVEs) experiments are used to acquire data on such behaviors. Performance targets are used to guide appropriate combination of existing BPMs with knowledge on occupant behaviors. The resulting model obtained is called an augmented BPM. Two different experiments related to occupants lighting behaviors are shown as case study. The results reveal that augmented BPMs significantly outperformed existing BPMs with respect to achieving specified performance targets. The case study confirmed the potential of the computational framework for improving prediction accuracy of BPMs during design.
more »
« less
Applying Generative Adversarial Network to Combine Occupant Responses to Design Contexts in Immersive Virtual Reality with Existing Building Performance Models
Building performance models (BPMs) are often used to estimate, analyze, and understand the performance of future or non-existing buildings during designs. However, performance gaps between prediction from BPMs and actual building still exist. Obviously, occupant behaviors are one of the major factors which cause the performance gaps because of several reasons, including (1) they are dynamic, (2) they are driven by many contextual factors, and (3) they are difficult to be captured by traditional experiments. This paper discusses a framework of applying generative adversarial networks (GANs) as an alternative approach to combine existing BPMs with occupant responses to design specific and context sensitive factors obtained from immersive virtual environment (IVE) toward designed buildings (target buildings) in order to reduce performance gaps between prediction during designs and actual buildings.
more »
« less
- Award ID(s):
- 1640818
- NSF-PAR ID:
- 10194354
- Date Published:
- Journal Name:
- ASCE Innovative Construction Project Management and Construction Industrialization (ICCREM 2019)
- Page Range / eLocation ID:
- 25 to 34
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Identification and quantitative understanding of factors that influence occupant energy behavior and thermal state during the design phase are critical in supporting effective energy-efficient design. To achieve this, immersive virtual environments (IVEs) have recently shown potential as a tool to simulate occupant energy behaviors and collect context-dependent behavior data for buildings under design. On the other hand, prior models of occupant energy behaviors and thermal states used correlation-based approaches, which failed to capture the underlying causal interactions between the influencing factors and hence were unable to uncover the true causing factors. Therefore, in this study, the authors investigate the applicability of causal inference for identifying the causing factors of occupant/participant energy behavioral intentions and their thermal states in IVE condition and compare those results with the baseline in-situ condition. The energy behavioral intentions here are a proximal antecedent of actual energy behaviors. A set of experiments involving 72 human subjects were performed through the use of a head-mounted device (HMD) in a climate chamber. The subjects were exposed to three different step temperatures (cool, neutral, warm) under an IVE and a baseline in-situ condition. Participants' individual factors, behavioral factors, skin temperatures, virtual experience factors, thermal states (sensation, acceptability, comfort), and energy behavioral intentions were collected during the experiments. Structural causal models were learnt from data using the elicitation method in conjunction with the PC-Stable algorithm. The findings show that the causal inference framework is a potentially effective method for identifying causing factors of thermal states and energy behavioral intentions as well as quantifying their causal effects. In addition, the study shows that in IVE experiments, the participants' virtual experience factors such as their immersion, presence, and cybersickness were not the causing factors of thermal states and energy behavioral intentions. Furthermore, the study suggests that participants' behavioral factors such as their attitudes toward energy conservation and perceived behavioral control to conserve energy were the causing factors of their energy behavioral intentions. Also, the indoor temperature was a causing factor of general thermal sensation and overall skin temperature. The paper also discusses other findings, including discrepancies, limitations of the study, and recommendations for future studies.more » « less
-
A model of a building’s thermal dynamics is needed for prediction-based control. The task of identifying a thermal dynamic model is made challenging by the presence of large unmeasured disturbances, especially the heat gain due to the occupants. In fact, identification of this “occupant-induced load” is also valuable for predictive control—especially in commercial buildings. We propose a method to identify both a model (of resistance-capacitance network type) and the unmeasured disturbances from measured input-output data. The method is based on the insight that the main contributor to the unmeasured disturbance, the occupant-induced load, is piecewise constant, especially in commercial buildings. This can be used to construct an augmented dynamic model so that disturbance estimation is converted to a state estimation problem. An outer-loop optimization identifies the best-fit parameter values. The effectiveness of the method is evaluated using data from a simulation model (under both open and closed-loop operations) and a real building.more » « less
-
Abstract. A novel structural system is being investigated collaboratively – by an international team including three U.S. universities, two Japanese universities and two major experimental research labs – as a means to protect essential facilities, such as hospitals, where damage to the building and its contents and occupant injuries must be prevented and where continuity of operation is imperative during large earthquakes. The new system employs practical structural components, including (1) flexible steel moment frames, (2) stiff steel elastic spines and (3) force-limiting connections (FLC) that connect the frames to the spines, to economically control building response and prevent damaging levels of displacement and acceleration. The moment frames serve as the economical primary element of the system to resist a significant proportion of the lateral load, dissipate energy through controlled nonlinear response and provide persistent positive lateral stiffness. The spines distribute response evenly over the height of the building and prevent story mechanisms, and the FLCs reduce higher-mode effects and provide supplemental energy dissipation. The Frame- Spine-FLC System development is focusing on new construction, but it also has potential for use in seismic retrofit of deficient existing buildings. This paper provides an overview of the ongoing research project, including selected FLC cyclic test results and a description of the full-scale shake-table testing of a building with the Frame-Spine-FLC System, which represents a hospital facility and includes realistic nonstructural components and medical equipment.more » « less
-
null (Ed.)The integration of Internet of Things (IoT)-enabled sensors and building energy management systems (BEMS) into smart buildings offers a platform for real-time monitoring of myriad factors that shape indoor air quality. This study explores the application of building energy and smart thermostat data to evaluate indoor ultrafine particle dynamics (UFP, diameter ≤ 100 nm). A new framework is developed whereby a cloud-based BEMS and smart thermostats are integrated with real time UFP sensing and a material balance model to characterize UFP source and loss processes. The data-driven framework was evaluated through a field campaign conducted in an occupied net-zero energy building—the Purdue Retrofit Net-zero: Energy, Water, and Waste (ReNEWW) House. Indoor UFP source events were identified through time-resolved electrical kitchen appliance energy use profiles derived from BEMS data. This enabled determination of kitchen appliance-resolved UFP source rates and time-averaged concentrations and size distributions. BEMS and smart thermostat data were used to identify the operational mode and runtime profiles of the air handling unit and energy recovery ventilator, from which UFP source and loss rates were estimated for each mode. The framework demonstrates that equipment-level energy use data can be used to understand how occupant activities and building systems affect indoor air quality.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1061/9780784482308.003
