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This work would not have been possible without the generous contributions of energy upgrade project data from numerous individuals and organizations. We would like to acknowledge the contributions made by the following:

Purpose The importance of sustainability in architecture currently necessitates the integration of innovative teaching strategies on the subject into architecture programs. This study aims to introduce and examine peer learning pedagogy by peer tutoring to educate architecture students in sustainable design. Design/methodology/approach Based on class assignments proposed in two different architecture sustainability-focused courses in the second and fourth years of the Bachelor of Science in architecture program, a total of 103 students assessed the proposed peer learning experience and its impact on their sustainability mindsets and education. Subjective surveys for evaluating the peer learning experience were designed and delivered at different stages of the course sequences. A total of 502 survey responses were obtained in the study. Findings The qualitative and quantitative data analysis confirms that the proposed peer learning by peer tutoring increased students’ knowledge, motivation and commitment to sustainable design. In addition, participants became more confident in applying sustainable design skills and their academic grades improved more than 25% compared to previous courses using traditional teaching methods. Originality/value Traditional architecture education has long been criticized for their pedagogical methodologies based primarily on passive learning. Recently, these programs have begun to prepare students to become active learners and communicators in collaborative and multidisciplinary environments. A mixed-method approach of combining pre-/post-experience surveys and analysis of final grades was used to determine the level of success and the quantifiable behavior change delivered by students involved in this peer learning experience.

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An innovative building envelope was introduced for daylight permeability in an anidolic manner through the opaque parts of exterior façades and roofs. A prefabricated translucent concrete panel (TCP) with embedded optical fibers (OFs) was coupled with a layer outfitted with compound parabolic concentrators (CPCs). Such TCPs have been predominantly used for aesthetic purposes. Moreover, OFs and CPCs have been used in many industries, particularly for telecommunications and the concentration of solar energy, respectively. The goal of this study was to introduce a novel building-envelope construction solution that can transmit sunlight to the interior of a building. Because of the nature of the traditional building materials blocking the passage of natural light, artificial lighting was constantly required, even during daytime, which consumed a great deal of energy in the form of artificial electrical light. This proposed building envelope is a viable solution to alleviate this inefficiency. Experimental results show the effectiveness and limitations of the proposed solution discussed in this paper.

Physics-based simulation of energy use in buildings is widely used in building design and performance rating, controls design and operations. However, various challenges exist in the modeling process. Model parameters such as people count and air infiltration rate are usually highly uncertain, yet they have significant impacts on the simulation accuracy. With the increasing availability and affordability of sensors and meters in buildings, a large amount of measured data has been collected including indoor environmental parameters, such as room air dry-bulb temperature, humidity ratio, and CO2 concentration levels. Fusing these sensor data with traditional energy modeling poses new opportunities to improve simulation accuracy. This study develops a set of physics-based inverse algorithms which can solve the highly uncertain and hard-to-measure building parameters such as zone-level people count and air infiltration rate. A simulation-based case study is conducted to verify the inverse algorithms implemented in EnergyPlus covering various sensor measurement scenarios and different modeling use cases. The developed inverse models can solve the zone people count and air infiltration at sub-hourly resolution using the measured zone air temperature, humidity and/or CO2 concentration given other easy-to-measure model parameters are known.