Climate Responsive Design and the Milam Residence

Energy conservation and efficiency is an essential area of focus in contemporary building design. The perception that the designers of buildings during the Modernist period of architecture ignored these principles is a false one. The present study, an examination of Paul Rudolph’s Milam Residence, a masterpiece of American residential architecture, is part of a larger project endeavoring to create a knowledge base of the environmental performance of iconic modernist homes. A critical examination of the Milam House allows insight into specific design characteristics that impact energy efficiency and conservation. Located in Ponte Vedra Beach, Florida, the Milam Residence was constructed in 1962. It was the last of a series of Florida residences designed by Rudolph, Chairman of the Department of Architecture at Yale University (1958–1965). The structure’s form is strongly related to its location on a subtropical beachfront. This paper presents a detailed analysis of the building’s solar responsiveness. Specifically, we examine design strategies such as orientation and sunscreening and their effect on daylighting, shading, and heat gain. The analysis is based on parametric energy modeling studies using Autodesk’s Ecotect, an environmental analysis tool that allows simulation of building performance. While the initial target of the program was early design, the program allows the input of complex geometries and detailed programming of zones, materials, schedules, etc. The program\u27s excellent analyses of desired parameters are augmented by visualizations that make it especially valuable in communicating results. Our findings suggest that the building, as built and situated on the site, does take advantage of daylighting and solar shading and does so in both expected and unexpected ways

Integrated Workflow Development for Data-Driven Neighborhood-Scale Building Performance Simulation

As urbanization intensifies, cities are key contributors to energy consumption and carbon emissions, accounting for a significant portion of global energy use and CO2 emissions. This paper introduces a systematic approach to support the development of urban projects with minimized operational carbon footprints through the integration of data-driven building performance simulation (BPS) tools in early-stage design. Emphasizing the necessity for a collaborative effort among designers, policymakers, and other stakeholders, we discuss the evolution of BPS toward incorporating data-driven tools for energy need reduction and informed decision-making. Despite the proliferation of modeling methods and data-related challenges, we present a theoretical workflow, supported by interactions with design firms in the US and European Union (EU) through interviews. This structured approach, demonstrating adaptability and scalability across urban contexts, foregrounds the potential for future data-driven integration in design practices. Grounded in theoretical concepts and preliminary real-world insights, our work emphasizes the transformation of standard activities toward data-driven processes, showcasing the crucial role of practical experience in advancing sustainable, low-carbon urban development

How Work From Home Has Affected the Occupant’s Well-Being in the Residential Built Environment: An International Survey Amid the Covid-19 Pandemic

This paper presents the results from an international survey that investigated the impacts of the built environment on occupant well-being during the corona virus disease 2019 (COVID-19) pandemic when most professionals were forced to work from home (WFH). The survey was comprised of 81 questions focusing on the respondent’s profiles, residences, home indoor environmental quality, health, and home working experiences. A total of 1460 responses were collected from 35 countries, and 1137 of them were considered complete for the analysis. The results suggest that home spatial layout has a significant impact on occupant well-being during WFH since home-life distractions and noises due to the lack of a personal workspace are likely to prevent productive work. Lack of scenic views, inadequate daylighting, and poor acoustics were also reported to be detrimental to occupant productivity and the general WFH experience. It is also revealed from this survey that temperature, relative humidity, and indoor air quality generally have higher satisfaction ratios compared with the indoor lighting and acoustic conditions, and the home layout. Hence, home design for lighting, acoustics, and layout should also receive greater attention in the future

Net Zero Energy Homes: An Evaluation of Two Homes in the Northeastern United States

This paper will discuss two Net Zero Energy homes in the United States. The aim is to discuss the differences and similarities in the construction type, energy use, active and renewable systems of the two homes. While each of the homes is designed to achieve net zero site energy use, the design and systems are very different. Furthermore, the measure that is used to qualify a home as net zero energy does not account for the full scope of work on each home. It is suggested that a new set of metrics be developed to allow for a more robust understanding of net zero energy buildings, one that integrates passive design strategies, occupant health and comfort, and durability. The objective is to facilitate a broader understanding of efficient and sustainable residential design. This understanding is critical to bringing Net Zero Energy Buildings to the public.</jats:p

Impact Assessment in the Process of Propagating Climate Change Uncertainties into Building Energy Use

Buildings are subject to significant stresses due to climate change and design strategies for climate resilient buildings are rife with uncertainties which could make interpreting energy use distributions difficult and questionable. This study intends to enhance a robust and credible estimate of the uncertainties and interpretations of building energy performance under climate change. A four-step climate uncertainty propagation approach which propagates downscaled future weather file uncertainties into building energy use is examined. The four-step approach integrates dynamic building simulation, fitting a distribution to average annual weather variables, regression model (between average annual weather variables and energy use) and random sampling. The impact of fitting different distributions to the weather variable (such as Normal, Beta, Weibull, etc.) and regression models (Multiple Linear and Principal Component Regression) of the uncertainty propagation method on cooling and heating energy use distribution for a sample reference office building is evaluated. Results show selecting a full principal component regression model following a best-fit distribution for each principal component of the weather variables can reduce the variation of the output energy distribution compared to simulated data. The results offer a way of understanding compound building energy use distributions and parsing the uncertain nature of climate projections.</jats:p

Impact Assessment in the Process of Propagating Climate Change Uncertainties into Building Energy Use

Buildings are subject to significant stresses due to climate change and design strategies for climate resilient buildings are rife with uncertainties which could make interpreting energy use distributions difficult and questionable. This study intends to enhance a robust and credible estimate of the uncertainties and interpretations of building energy performance under climate change. A four-step climate uncertainty propagation approach which propagates downscaled future weather file uncertainties into building energy use is examined. The four-step approach integrates dynamic building simulation, fitting a distribution to average annual weather variables, regression model (between average annual weather variables and energy use) and random sampling. The impact of fitting different distributions to the weather variable (such as Normal, Beta, Weibull, etc.) and regression models (Multiple Linear and Principal Component Regression) of the uncertainty propagation method on cooling and heating energy use distribution for a sample reference office building is evaluated. Results show selecting a full principal component regression model following a best-fit distribution for each principal component of the weather variables can reduce the variation of the output energy distribution compared to simulated data. The results offer a way of understanding compound building energy use distributions and parsing the uncertain nature of climate projections

THE RELATIONSHIP BETWEEN COMFORT PERCEPTIONS AND ACADEMIC PERFORMANCE IN UNIVERSITY CLASSROOM BUILDINGS

This paper presents preliminary data on a series of building comfort experiments conducted in the field. We performed physical in-situ measurements and solicited responses from 409 (184 female; 225 male) university students in six different classrooms at the University of Massachusetts-Amherst during three seasons (fall, winter and spring). Our questions focused on student perception of comfort in varied environmental (temperature and humidity, and air speed) conditions. We collected records of student academic performance in the classes, correlating their comfort perceptions to their test scores. Statistical analysis of classroom environmental variables, thermal satisfaction, and student scores suggest that by enhancing thermal comfort, we can improve academic performance. </jats:p