arXiv subjects

Stefano Schiavon

Publications and source records attributed to Stefano Schiavon.

12 recordsLinked to original sources

arXiv2025

From 'What-is' to 'What-if' in Human-Factor Analysis: A Post-Occupancy Evaluation Case

Human-factor analysis typically employs correlation analysis and significance testing to identify relationships between variables. However, these descriptive ('what-is') methods, while effective for identifying associations, are often insufficient for answering causal ('what-if') questions. Their application in such contexts often overlooks confounding and colliding variables, potentially leading to bias and suboptimal or incorrect decisions. We advocate for explicitly distinguishing descriptive from interventional questions in human-factor analysis, and applying causal inference frameworks specifically to these problems to prevent methodological mismatches. This approach disentangles complex variable relationships and enables counterfactual reasoning. Using post-occupancy evaluation (POE) data from the Center for the Built Environment's (CBE) Occupant Survey as a demonstration case, we show how causal discovery reveals intervention hierarchies and directional relationships that traditional associational analysis misses. The systematic distinction between causally associated and independent variables, combined with intervention prioritization capabilities, offers broad applicability to complex human-centric systems, for example, in building science or ergonomics, where understanding intervention effects is critical for optimization and decision-making.

stat.AP↗

arXiv2024

Integrating Symbolic Neural Networks with Building Physics: A Study and Proposal

Symbolic neural networks, such as Kolmogorov-Arnold Networks (KAN), offer a promising approach for integrating prior knowledge with data-driven methods, making them valuable for addressing inverse problems in scientific and engineering domains. This study explores the application of KAN in building physics, focusing on predictive modeling, knowledge discovery, and continuous learning. Through four case studies, we demonstrate KAN's ability to rediscover fundamental equations, approximate complex formulas, and capture time-dependent dynamics in heat transfer. While there are challenges in extrapolation and interpretability, we highlight KAN's potential to combine advanced modeling methods for knowledge augmentation, which benefits energy efficiency, system optimization, and sustainability assessments beyond the personal knowledge constraints of the modelers. Additionally, we propose a model selection decision tree to guide practitioners in appropriate applications for building physics.

cs.LG↗

arXiv2023

Using causal inference to avoid fallouts in data-driven parametric analysis: a case study in the architecture, engineering, and construction industry

The decision-making process in real-world implementations has been affected by a growing reliance on data-driven models. We investigated the synergetic pattern between the data-driven methods, empirical domain knowledge, and first-principles simulations. We showed the potential risk of biased results when using data-driven models without causal analysis. Using a case study assessing the implication of several design solutions on the energy consumption of a building, we proved the necessity of causal analysis during the data-driven modeling process. We concluded that: (a) Data-driven models' accuracy assessment or domain knowledge screening may not rule out biased and spurious results; (b) Data-driven models' feature selection should involve careful consideration of causal relationships, especially colliders; (c) Causal analysis results can be used as an aid to first-principles simulation design and parameter checking to avoid cognitive biases. We proved the benefits of causal analysis when applied to data-driven models in building engineering.

cs.CE↗

arXiv2023

Cozie Apple: An iOS mobile and smartwatch application for environmental quality satisfaction and physiological data collection

Collecting feedback from people in indoor and outdoor environments is traditionally challenging and complex in a reliable, longitudinal, and non-intrusive way. This paper introduces Cozie Apple, an open-source mobile and smartwatch application for iOS devices. This platform allows people to complete a watch-based micro-survey and provide real-time feedback about environmental conditions via their Apple Watch. It leverages the inbuilt sensors of a smartwatch to collect physiological (e.g., heart rate, activity) and environmental (sound level) data. This paper outlines data collected from 48 research participants who used the platform to report perceptions of urban-scale environmental comfort (noise and thermal) and contextual factors such as who they were with and what activity they were doing. The results of 2,400 micro-surveys across various urban settings are illustrated in this paper showing the variability of noise-related distractions, thermal comfort, and associated context. The results show people experience at least a little noise distraction 58% of the time, with people talking being the most common reason (46%). This effort is novel due to its focus on spatial and temporal scalability and collection of noise, distraction, and associated contextual information. These data set the stage for larger deployments, deeper analysis, and more helpful prediction models toward better understanding the occupants' needs and perceptions. These innovations could result in real-time control signals to building systems or nudges for people to change their behavior.

cs.HC↗

arXiv2023

Toward Platform-based Building Design

The electronic design industry has undergone a significant transformation, transitioning from traditional hand-drawn designs to modern automated design processes. While Computer-Aided Design (CAD) tools emerged alongside the electronic industry, the current building design process has little to no automation. There is a need for a unified platform to address the complexity of building design and provide a more systematic approach. Platform-based design (PBD), originally developed in the electronic industry, enables efficient design processes by promoting the reuse of hardware and software systems. It also facilitates design space exploration while optimizing performance. This paper proposes a modular approach that divides the building into various disciplines and introduces a design flow using the PBD framework to streamline the design process. We also present a case study that demonstrates the use of the PBD framework in the Heating, Ventilation, and Air Conditioning (HVAC) systems design.

cs.OH↗

arXiv2023

From Electronic Design Automation to Building Design Automation: Challenges and Opportunities

Design automation, which involves the use of software tools and technologies to streamline the design process, has been widely adopted in the electronics industry, resulting in significant advancements in product development and manufacturing. However, building design, which involves the creation of complex structures and systems, has traditionally lagged behind in leveraging design automation technologies. Despite extensive research on design automation in the building industry, its application in the current design of buildings is limited. This paper aims to (1) compare the design processes between electronics and building design, (2) highlight similarities and differences in their approaches, and (3) examine challenges and opportunities associated with bringing the concept of design automation from electronics to building design.

cs.OH↗

arXiv2022

CBE Clima Tool: a free and open-source web application for climate analysis tailored to sustainable building design

Buildings that are designed specifically to respond to the local climate can be more comfortable, energy-efficient, and with a lower environmental impact. However, there are many social, cultural, and economic obstacles that might prevent the wide adoption of designing climate-adapted buildings. One of the said obstacles can be removed by enabling practitioners to easily access and analyse local climate data. The CBE Clima Tool (Clima) is a free and open-source web application that offers easy access to publicly available weather files (in EPW format) specifically created for building energy simulation and design. It provides a series of interactive visualization of the variables therein contained and several derived ones. It is aimed at students, educators, and practitioners in the architecture and engineering fields. Since its launch has been consistently recording over 3000 monthly unique users from over 70 countries worldwide, both in professional and educational settings.

cs.HC↗

arXiv2022

Machine Learning for Smart and Energy-Efficient Buildings

Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the U.S., and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that the energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Recently, Machine Learning has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction of several machine learning paradigms and the components and functioning of each smart building system we cover. Finally, we discuss challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research at the intersection of smart buildings and machine learning.

cs.LG↗

arXiv2022

Cohort comfort models -- Using occupants' similarity to predict personal thermal preference with less data

We introduce Cohort Comfort Models, a new framework for predicting how new occupants would perceive their thermal environment. Cohort Comfort Models leverage historical data collected from a sample population, who have some underlying preference similarity, to predict thermal preference responses of new occupants. Our framework is capable of exploiting available background information such as physical characteristics and one-time on-boarding surveys (satisfaction with life scale, highly sensitive person scale, the Big Five personality traits) from the new occupant as well as physiological and environmental sensor measurements paired with thermal preference responses. We implemented our framework in two publicly available datasets containing longitudinal data from 55 people, comprising more than 6,000 individual thermal comfort surveys. We observed that, a Cohort Comfort Model that uses background information provided very little change in thermal preference prediction performance but uses none historical data. On the other hand, for half and one third of each dataset occupant population, using Cohort Comfort Models, with less historical data from target occupants, Cohort Comfort Models increased their thermal preference prediction by 8~\% and 5~\% on average, and up to 36~\% and 46~\% for some occupants, when compared to general-purpose models trained on the whole population of occupants. The framework is presented in a data and site agnostic manner, with its different components easily tailored to the data availability of the occupants and the buildings. Cohort Comfort Models can be an important step towards personalization without the need of developing a personalized model for each new occupant.

cs.LG↗

arXiv2021

A window view quality assessment framework

The views that windows provide from inside a building affect human health and well-being. Although window view is an important element of architecture, there is no established framework to guide its design. The literature is widely dispersed across different disciplinary fields, and there is a need to coalesce this information into a framework that can be applied into the building design. Based on the literature, we present a framework for what constitutes 'view quality.' At the basis of our framework, we propose three primary variables: View Content (the assessment of visual features seen in the window view), View Access (the measure of how much of the view can be seen through the window from the occupant's position), and View Clarity (the assessment of how clear the view content appears in the window view when seen by an occupant). Each variable was thematically derived from different sources including daylighting standards, green certification systems, and scientific research studies. We describe the most important characteristics of each variable, and from our review of the literature, we propose a conceptual index that can evaluate the quality of a window view. While discussing the index, we summarize design recommendations for integrating these three variables into the building process and identify knowledge gaps for future research.

cs.CY↗

arXiv2021

Automated decontamination of workspaces using UVC coupled with occupancy detection

Periodic disinfection of workspaces can reduce SARS-CoV-2 transmission. In many buildings periodic disinfection is performed manually; this has several disadvantages: it is expensive, limited in the number of times it can be done over a day, and poses an increased risk to the workers performing the task. To solve these problems, we developed an automated decontamination system that uses ultraviolet C (UVC) radiation for disinfection, coupled with occupancy detection for its safe operation. UVC irradiation is a well-established technology for the deactivation of a wide range of pathogens. Our proposed system can deactivate pathogens both on surfaces and in the air. The coupling with occupancy detection ensures that occupants are never directly exposed to UVC lights and their potential harmful effects. To help the wider community, we have shared our complete work as an open-source repository, to be used under GPL v3.

cs.RO↗

arXiv2020

Balancing thermal comfort datasets: We GAN, but should we?

Thermal comfort assessment for the built environment has become more available to analysts and researchers due to the proliferation of sensors and subjective feedback methods. These data can be used for modeling comfort behavior to support design and operations towards energy efficiency and well-being. By nature, occupant subjective feedback is imbalanced as indoor conditions are designed for comfort, and responses indicating otherwise are less common. This situation creates a scenario for the machine learning workflow where class balancing as a pre-processing step might be valuable for developing predictive thermal comfort classification models with high-performance. This paper investigates the various thermal comfort dataset class balancing techniques from the literature and proposes a modified conditional Generative Adversarial Network (GAN), $\texttt{comfortGAN}$, to address this imbalance scenario. These approaches are applied to three publicly available datasets, ranging from 30 and 67 participants to a global collection of thermal comfort datasets, with 1,474; 2,067; and 66,397 data points, respectively. This work finds that a classification model trained on a balanced dataset, comprised of real and generated samples from $\texttt{comfortGAN}$, has higher performance (increase between 4% and 17% in classification accuracy) than other augmentation methods tested. However, when classes representing discomfort are merged and reduced to three, better imbalanced performance is expected, and the additional increase in performance by $\texttt{comfortGAN}$ shrinks to 1-2%. These results illustrate that class balancing for thermal comfort modeling is beneficial using advanced techniques such as GANs, but its value is diminished in certain scenarios. A discussion is provided to assist potential users in determining which scenarios this process is useful and which method works best.

cs.LG↗