Design for sustainable behaviour

The global impact of designed goods and the role designer’s play in accelerating rapid, conspicuous consumption has long been recognized within the profession. As such, considerable effort has been directed towards reducing or mitigating negative environmental impacts caused by mass-manufacture and disposal through so called ‘end of pipe’ solutions. Less attention, however, has been placed on reducing the impact of use despite tacit acknowledgement among the design community that sustainable designs cannot reach their full potential without targeting user behaviour. Through increased focus on behaviour, and the implementation of suitably informative or persuasive strategies, designers can purposefully alter the way users interact with products to leverage more sustainable use patterns. This chapter provides design practitioners with an introduction to Design for Sustainable Behaviour (DfSB). This is an emergent field of design practice which seeks to understand user behaviour in order to drive the development of products which encourage more sustainable use. Integrating inspirational case study examples drawn from their own and others’ practice, the authors chart the origins of DfSB and describe its theories, strategies and design processes. Tools to aid strategy selection are introduced and key ethical considerations reflected on in relation to specific design phases. The authors offer practical advice on designing, installing and evaluating design interventions based on experience and conclude with a discussion of the current limitations and potential future developments in DfSB

Management and assessment of performance risks for bioclimatic buildings

Given high energy demands of buildings, developing countries need to be sensitive to the critical role of building energy efficiency in the fight against climate change. Especially in tropical countries where the thermal flow is strong and the lack of electricity distribution networks is a sad reality. The consolidation of this energy efficiency requires the preservation of nature through a harmony between the building and its environment on one hand and an effective evaluation of energy performance on the other hand. Faced with these challenges, the bioclimatic concept is one of the best alternatives to weave this harmony between the building and its environment. Furthermore a meaningful energy performance assessment of buildings based on the knowledge of capitalization with the experience feedback processes can be used to structure the different phases of implementation of the buildings. Firstly, this article presents the general concept of bioclimatic buildings with emphasis on thermal notions that influence thermal comfort inside a building. Secondly, the effort focuses on identifying non-qualities and factors of discomfort whose resolution helps to improve the energy and environmental performance of buildings. This approach supported by land surveys to interview the building actors and users to collect data favourable or not favourable to energy-performance. These data are then processed for the generation of graphical representations used by methods developed on the basis of knowledge and strategies of bioclimatic concepts. After the capitalized knowledge from experience feedback processes allows us to offer corrective solutions and share best practices to address the identified performance problems

Automotive climate control based on thermal state estimation

Abstract available: p.ii

Engineering data compendium. Human perception and performance. User's guide

The concept underlying the Engineering Data Compendium was the product of a research and development program (Integrated Perceptual Information for Designers project) aimed at facilitating the application of basic research findings in human performance to the design and military crew systems. The principal objective was to develop a workable strategy for: (1) identifying and distilling information of potential value to system design from the existing research literature, and (2) presenting this technical information in a way that would aid its accessibility, interpretability, and applicability by systems designers. The present four volumes of the Engineering Data Compendium represent the first implementation of this strategy. This is the first volume, the User's Guide, containing a description of the program and instructions for its use

An ensemble model for predictive energy performance:Closing the gap between actual and predicted energy use in residential buildings

The design stage of a building plays a pivotal role in influencing its life cycle and overall performance. Accurate predictions of a building's performance are crucial for informed decision-making, particularly in terms of energy performance, given the escalating global awareness of climate change and the imperative to enhance energy efficiency in buildings. However, a well-documented energy performance gap persists between actual and predicted energy consumption, primarily attributed to the unpredictable nature of occupant behavior.Existing methodologies for predicting and simulating occupant behavior in buildings frequently neglect or exclusively concentrate on particular behaviors, resulting in uncertainties in energy performance predictions. Machine learning approaches have exhibited increased accuracy in predicting occupant energy behavior, yet the majority of extant studies focus on specific behavior types rather than investigating the interactions among all contributing factors. This dissertation delves into the building energy performance gap, with a particular emphasis on the influence of occupants on energy performance. A comprehensive literature review scrutinizes machine learning models employed for predicting occupants' behavior in buildings and assesses their performance. The review uncovers knowledge gaps, as most studies are case-specific and lack a consolidated database to examine diverse behaviors across various building types.An ensemble model integrating occupant behavior parameters is devised to enhance the accuracy of energy performance predictions in residential buildings. Multiple algorithms are examined, with the selection of algorithms contingent upon evaluation metrics. The ensemble model is validated through a case study that compares actual energy consumption with the predictions of the ensemble model and an EnergyPlus simulation that takes occupant behavior factors into account.The findings demonstrate that the ensemble model provides considerably more accurate predictions of actual energy consumption compared to the EnergyPlus simulation. This dissertation also addresses the research limitations, including the reusability of the model and the requirement for additional datasets to bolster confidence in the model's applicability across diverse building types and occupant behavior patterns.In summary, this dissertation presents an ensemble model that endeavors to bridge the gap between actual and predicted energy usage in residential buildings by incorporating occupant behavior parameters, leading to more precise energy performance predictions and promoting superior energy management strategies

Building Occupants' Comfort Levels Identified with POE and Visualized by BIM

Creating and maintaining a comfortable indoor environment is crucial for energy-efficient building operation. However, there is often a disparity between defined comfort conditions and occupants' perceived comfort. To address this, collecting occupants' feedback and evaluating building performance through post-occupancy evaluation (POE) surveys are essential. Building Information Modeling (BIM) can enhance the visualization of survey results by providing a digital representation of the building. This study aimed to utilize POE surveys, Dynamo, and Revit add-ins to identify, visualize, and communicate factors contributing to discomfort for building occupants. A POE survey was conducted with 51 respondents, assessing aspects such as temperature comfort, indoor air quality, visual comfort, acoustic comfort, and space adequacy. Results indicated that occupants were most dissatisfied with indoor air quality in summer and most satisfied with space adequacy. Dynamo, a visual programming tool, was employed to create a script that imported the survey results into each room, colorizing them based on the survey aspects. Additionally, Revit add-ins were developed using Microsoft Visual Studio and the C# programming language to import and present data from Excel files within the Revit model. This facilitated the visualization of sensor data in the same BIM environment. By conducting the POE survey, comfort levels were identified, and Dynamo scripts colorized the rooms in Revit to represent the comfort levels. The Revit add-ins further enhanced BIM's role as a unified and digital database, allowing the import and reading of sensor data. In summary, this research aimed to use POE surveys, Dynamo, and Revit add-ins to identify, visualize, and communicate factors contributing to discomfort for building occupants. The combination of these tools provided valuable insights into comfort levels and facilitated efficient visualization and communication of survey results within the digital building model

Human experience in the natural and built environment : implications for research policy and practice

22nd IAPS conference. Edited book of abstracts. 427 pp. University of Strathclyde, Sheffield and West of Scotland Publication. ISBN: 978-0-94-764988-3

INTEGRATED MODELING AND MONITORING FOR A HEALTHY AND SUSTAINABLE BUILDING ENVIRONMENT

The transmission of airborne diseases indoors is a significant challenge to public health. Buildings are hotspots for viral transmission, which can result in adverse effects on human health and quality of life, especially considering that individuals spend approximately 87% of their time indoors. The emergence of the COVID-19 pandemic has highlighted the importance of considering health aspects during the development of sustainable built environments. Consequently, maintaining a healthy, sustainable, and comfortable built environment represents a major challenge for facilities management teams. However, research on the infection risks associated with emerging pandemics is still in its infancy, and the effectiveness of intervention strategies remains uncertain. Furthermore, the complex interplay between health, energy consumption, and human comfort remains poorly understood, impeding the development of comprehensive control strategies that encompass all three critical dimensions of building sustainability. In addition, existing technologies have limitations to conduct real-time monitoring, while current communication methods between occupants and facilities management teams suffer from a lack of effectiveness, user-friendliness, and informativeness. These deficiencies hinder their ability to address the pressing needs of occupants during pandemics. To address these challenges, this dissertation proposes a convergent framework that integrates modeling, simulation, and monitoring methodologies for the development and maintenance of a sustainable built environment. Airborne transmission risks were first modeled and estimated under different epidemic scenarios, allowing for the evaluation of various intervention strategies. Facility data was then used to develop methods for modeling and simulating the dimensions of energy consumption and thermal comfort, allowing for the identification of tradeoff relationships among health, energy, and comfort, and quantitatively analyzing the impact of indoor environments through HVAC control strategies on the three major dimensions. Finally, an integrated platform was developed to enable the real-time assessment of health, energy, and comfort, including monitoring, visualization, and conversational communication functionalities. The developed framework thus encompasses modeling, simulation, monitoring, and communication capabilities and can be widely adopted by facility management teams, providing insights and guidance to governments and policymakers based on their specific needs. The applicability of the framework extends beyond specific pandemics and can be used to address a broader range of infectious diseases