This paper explores the impact of integrating Design Thinking and Systems Thinking frameworks and methodologies into a participatory learning environment to enhance emotional intelligence (EI) among fourth-year engineering students at the University of Victoria. This study was undertaken to address the noted gap in the social competencies of engineering graduates frequently noted in both literature and by engineering practitioners. A course, "Infrastructure Design with Indigenous Communities", was developed, drawing on theories of identity formation and best practices from educational psychology. A mixed methods approach was used to analyze the data. This included pre-and post-semester EQ-i 2.0 EI inventories, and thematic analysis of qualitative self-reflective writings to triangulate the data. The sample included 17 fourth-year civil engineering students. Statistical analysis of pre-and post-semester EQ-i 2.0 EI inventories from the 17 students indicated an average overall EI increase of 5.4 points with a critical t-value of 3.105 and a p-value of 0.0034, rejecting the null hypothesis that the course did not affect the students' EI. Qualitative data and thematic analysis were used to triangulate the findings to support the hypothesis that the course had a direct impact on the students' EI. This study is highly relevant to engineering education and practice. It highlights and addresses the need for engineers to possess not only technical competencies, but also emotional competencies to address complex, interconnected challenges at the intersection of technology and society. This is especially crucial when working in cross-cultural contexts with Indigenous communities. This study addresses a gap of relevance to the contemporary training of engineering students. This study suggests that the careful integration of Design Thinking and Systems Thinking into engineering curricula increases many trait parameters of EI, including empathy. This may be beneficial to engineering academia and practices as it produces engineers with more developed social competencies who may be better equipped to consider the social nuances and impacts of engineering designs.

The Advanced Building Construction Collaborative is a market facilitation hub that brings together a diverse network of incumbent and emergent buildings sector actors -across construction, manufacturing, real estate, development, and related areas.It works to accelerate the uptake, scaling, and mainstream adoption of advanced building construction (ABC) -technologies and other innovations for new construction and building retrofits that combine energy-efficient building decarbonization with streamlined, scalable, industrialized construction methods -while supporting and leveraging modernization of the US construction industry.Its mission is to drive ABC in service of decarbonizing the US buildings sector before 2050 while improving affordability, resilience, and equity.

Dataset for the "Heat pumps for all? Distributions of the costs and benefits of residential air-source heat pumps in the United States" paper

Older homes represent approximately 70% of the residential building stock in the United States and often have significant air leakage, inadequate insulation, and inefficient windows.There is an opportunity to improve their energy performance at the time of other planned work such as residing, which occurs on over two million houses annually, or during window replacement.To evaluate the technical and economic potential of exterior insulation and window upgrades to the older portions of the U.S. housing stock at the time of other planned work on the house, this analysis used the ResStock TM tool to evaluate the energy savings, carbon emissions impacts, energy bill impacts, and capital cost of 15 retrofit cases.The retrofit cases included two exterior insulation upgrades and two window upgrades, both individually and in pairwise combinations and both with and without other work planned on the house (i.e., re-siding, window replacement).These retrofit cases were modeled on a large sample of houses representative of the over 48 million single-family detached houses in the contiguous U.S. built before 1990.The four upgrade components included in the modeling were:1. 1" exterior continuous insulation 2. 2" exterior continuous insulation 3. Exterior low-E storm windows Triple-pane windowsThe key takeaways from the analysis include:• In Cold and Mixed-Humid climate single-family detached houses built before 1970, adding insulation at time of re-siding is cost-effective for about 15 million homes.• In single-family detached houses built before 1970 in warmer climates, adding insulation at the time of re-siding is cost-effective in about 4 million homes.• In single-family detached houses in the Cold and Mixed-Humid climates built before 1990, triple-pane windows are cost-effective in about 12 million homes.However, as an alternative to replacing existing windows with code-minimum windows, replacing existing windows with triple-pane windows is cost-effective in only about 4 million homes, predominantly in New England and the Upper Midwest.• When expected increases in home resale value are taken into account, the number of homes in the Cold and Mixed-Humid climates built before 1990 showing cost-effective triple-pane window upgrades rises to about 14 million when no work is planned and 8 million as an alternative to replacing existing windows with code-minimum windows.These conclusions can help inform retrofit recommendations and market transformation efforts in the re-siding and window replacement markets.vii

The conceptualization of “engineering design”, as outlined by the Canadian Engineering Accreditation Board (CEAB), has shifted over the years, however, the gap in engineering education remains a prevailing deficiency in engineering education and practice lies in the exclusion of non-technical competencies (such as empathy, communication, innovation, and creativity) that are impeding engineers from effectively addressing complex issues. These frameworks offer a robust methodology for tackling complex, dynamic, and interconnected challenges—referred to as “wicked problems”. In addition, this paper proposes a fourth-year engineering design course that explicitly incorporates these approaches, addressing the identified gap in social competencies within engineering education. By integrating these approaches into the foundation of engineering design education, there may be an avenue to equip engineers with the skills needed to empathize with stakeholders, understand contextual landscapes and generate meaningful solutions that contribute positively to society.

* New in Version 2.1 * All residential measure savings shapes data (Latest_Res_Shapes.zip and residential measures in Latest_BM_Shapes.zip) were updated to correct post-processing errors present in version 2. The raw baseline-case data that are used in Scout to estimate sector-level baseline hourly loads (file tsv_load) are now included in this data resource (see files Latest_Res_Baselines.zip and Latest_Com_Baselines.zip). Additional residential measure run documentation is available (here for all except water heating efficiency plus flexibility (EE+DF) measure and here for the water heating EE+DF measure). A guide to reading and/or preparing savings shapes CSVs is available in the Scout documentation, p. 36. The documentation also summarizes the net system load conditions that measures with flexibility (DF) characteristics respond to (Table 1, p. 37). * New in Version 2 * All hourly savings shapes CSV files that support the original analysis have been updated to reflect the following improvements: Generate residential data using ResStock v2.5.0 and commercial data using DOE Commercial Prototypes generated with OpenStudio v3.3.0. Residential and commercial measures with flexibility (DF) features respond to updated grid conditions (net peak/low load periods) that are consistent with projections from the EIA 2022 Annual Energy Outlook (AEO) “Low renewables cost” side case. Residential baseline loads and load savings are now distinguished by three building types (single family, multi family, and mobile homes). Updated savings shape CSVs are organized into three ZIP files that may be separately downloaded depending on user interests: Latest_BM_Shapes.zip includes only the subset of savings shape CSVs needed to execute the Scout Benchmark Scenarios. Latest_Res_Shapes.zip includes all residential savings shape CSVs. Latest_Com_Shapes.zip includes all commercial savings shape CSVs. Baseline load shapes in Scout have also been updated based on the same versions of ResStock and the DOE Commercial Prototypes, and peak/take period impact calculations have been updated to reflect the 2022 AEO system conditions. These updated data are contained in Scout v0.8 (see ./supporting_data/tsv_data).Summary of Original Data Files These data underpin an analysis of the near- and long-term technical potential bulk power grid resource offered by best available U.S. building efficiency and flexibility measures. Using multiple openly-available modeling frameworks supported by the U.S. Department of Energy, including Scout, ResStock, and the Commercial Building Prototype Models, we pair bottom-up simulations of measures' building-level impacts with regional representations of the building stock and its projected electricity use to estimate the impacts of multiple building efficiency and flexibility scenarios on hourly regional system loads across the contiguous U.S. in 2030 and 2050. We find that demand-side management via building efficiency and flexibility could avoid up to nearly ⅓ of annual fossil-fired generation and ½ of fossil-fired capacity additions after 2020. Results are reported at both the national and regional scales and are disaggregated by building type and end use, facilitating a quantitative understanding of the role that buildings as a whole and specific building technologies or operational approaches can play in the future evolution of the U.S. electricity system. The four ZIP files that make up this data record are interpreted as follows: Measure_Data.zip: Includes the Scout energy conservation measure (ECM) JSON definitions that were used to generate the main baseline and efficient/flexible scenario results ("Baseline_Measures" and "Efficiency_Flexibility_Measures", respectively), as well as side cases that assess the sensitivity of results to higher levels of variable renewable penetration ("High_RE_Sensitivity_Analysis") and a high degree of building load electrification ("High_Electrification_Measures"). Each measure set includes supporting 8760 load savings shapes in the sub-folder "Savings_Shapes". Additional details about defining and interpreting Scout measures with time-sensitive analysis features are available here. Results_Data.zip: Includes the main and side case results data. Baseline-case outcomes, which are consistent with the EIA 2019 Annual Energy Outlook, are stored in "Baseline_Loads". Efficient/flexible scenario results are stored in "Efficiency_Flexibility_Measure_Impacts_Individual" and "Efficiency_Flexibility_Measure_Impacts_Portfolio," respectively, where the former includes results for individual measures in our analysis without considering any interactions across measures, and the latter includes results for aggregations of energy efficiency (EE), demand flexibility (DF), and efficiency and flexibility (EE+DF) portfolios that do consider interactions across measures in each portfolio. Results for the high electrification side case are stored in the "High_Electrification" sub-folder in the EE+DF case only. Results for the high renewable sensitivity analysis are stored in "High_RE_Sensitivity_Analysis", and residential and commercial 8760 savings shape outcomes for each of the EE, DF, and EE+DF measure portfolios and five of the 2019 EIA Electricity Market Module (EMM) regions (p.6) of focus are stored in "Sector_Level_8760s". Source_Code.zip: Includes the source code needed to translate the measure inputs provided in "Measures_Data.zip" into the outputs provided in "Results_Data.zip". The core set of files required to execute the main analysis results is stored in "Base_Code_Package", while variants to certain files in the core package needed to execute the high renewable sensitivity and high electrification side cases are stored in "Code_Variants". In general, the process of running an analysis is as described in the Scout Quick Start Guide; however, the file "ecm_prep_batch.py" should be substituted for "ecm_prep.py" and the file "run_batch.py" should be substituted for "run.py". These batch files execute multiple versions of "ecm_prep.py" and "run.py" that are tailored to generate individual measure and whole portfolio results for annual, net peak summer and winter, and net off-peak summer and winter metrics (individual measures: "ecm_prep.json," "ecm_prep_spa," "ecm_prep_wpa," "ecm_prep_sta," "ecm_prep_wta"; whole portfolio: "ecm_results.json," "ecm_results_spa.json," "ecm_results_wpa.json," and "ecm_results_sta.json," and "ecm_results_wta.json"). Results for the side cases are generated by replacing the versions of the "ecm_prep" and "run" files included in the "Base_Code_Package" folder with those in the "Code_Variants" folder. Sector-level 8760 shapes are generated using the "--sect_shapes" command line option as described here. See Scout's Local Execution Tutorials for more details on how to develop Scout inputs and outputs. Supporting_Data.zip: Includes supplemental data files provided by EIA that describe key inputs and outputs to the Electricity Market Module in the AEO 2019 run of the National Energy Modeling System ("EIA EMM Data (AEO 2019)"), as well as raw EnergyPlus outputs that were used to develop the baseline Scout hourly load shape file found in "./Source_Code/Base_Code_Package/supporting_data/tsv_data/tsv_load.json".

The residential building sector accounts for a substantial portion of total energy consumption in the United States and offers a significant opportunity for energy reduction and decarbonization through improvements in energy efficiency. Heating and air conditioning are the primary contributors to residential energy usage and electricity system peak demand. However, due to the diversity of the housing stock and the complexity of factors affecting heating and cooling demand, identifying the relative contributions to heating and cooling loads poses challenges. To address this, we applied the ResStock analysis tool to simulate 550,000 building energy models, providing statistical representation of residential buildings in the contiguous United States. We introduced outputs that quantified the heating and cooling influence of different components of a home, such as air leakage, envelope components (ceilings, walls, windows, foundations), internal heat gains from people, lighting, plug loads, and duct losses and gains. Leveraging the granularity of ResStock, we present a dataset to enable deeper understanding of the contributors to heating and cooling loads as a function of housing characteristics such as location, envelope efficiency, and building type. This work aims to support prioritization of research and development and informed decision-making for residential building decarbonization.

building, occupants, and location. Energysavings and incremental costs are calculated relative to a minimum upgrade reference scenario, which accounts for efficiency upgrades that would occur in the absence of a retrofit because of equipment wear-out and replacement with current minimum standards.

U.S. multifamily buildings house 35 million households, consuming 4 quads of source energy and spending $48 billion on utility bills every year. Almost all of these households live in urban areas, where cities are taking the lead on setting aggressive energy goals. Cities currently do not have the data and tools necessary to identify and target opportunities to save energy in their building stock. Building on the open-source, OpenStudio-based ResStock platform, this project will extend the publicly-available ResStock modeling capabilities to the multifamily sector, enabling Radiant Labs, and others, to partner with cities to market and strategically deploy cost-effective, energy efficiency (EE) upgrades directly to high-priority households. Radiant Labs has been successful in using ResStock's single-family capabilities to provide value to the City of Boulder, Colorado. However, lack of multifamily capabilities in ResStock is a roadblock for Radiant Labs working with New York City, San Francisco, Washington, D.C., and other cities that have expressed significant interest in working with them. In addition, other cities, companies, and utilities can use these open-source capabilities to grow their EE portfolios, thereby multiplying the impact of this project. This work also enables national-scale analysis of EE potential in multifamily buildings, which is of strong interest to a variety of stakeholders, including the U.S. Department of Energy (DOE) Office of Energy Policy and Systems Analysis (EPSA), DOE Weatherization Assistance Program (WAP), U.S. Department of Housing and Urban Development (HUD), and the Bonneville Power Administration (BPA).

Energy efficiency (EE) has long been recognized as a source of value to the electricity grid. Especially with increasing penetration of variable renewable generation, demand response (DR) can also provide system value and support the evolving needs of the grid. Yet there has been little study to date of interactions between EE and DR that may complicate their grid impacts. In this study we perform bottom-up modelling of the interactive effects between EE and DR in buildings for three representative regions of the United States electricity grid. Leveraging new simulation tools that enable detailed modelling of the building stock, we synthesize system-level demand profiles for several scenarios representing different portfolios of EE measures. In each scenario, we couple the underlying building models with a database of DR-enabling technologies to estimate building-level DR capabilities and compute a system-level supply curve for DR. We assess the resulting EE and DR interactive effects based on an existing conceptual framework. The results show a complex relationship between EE and DR, with interactive effects whose size and direction can vary widely depending on the grid system, type of DR, and the framework level being considered. Most often, the overall effect is competition between EE and DR, but significant complementarity can also occur, especially when the EE portfolio includes controls measures. Our results suggest that EE and DR programs developed without considering interactive effects may erode the benefits of both resources, whereas a more integrated approach may yield increased benefits.

Does engineering design education in North America prepare students to address the major issues of our time? In today's political and social climate, engineers are part of multi-disciplinary teams tasked with finding solutions to complex issues like poverty, climate change, the housing affordability crisis, resource depletion, and water shortages. By definition, these problems are "wicked". If engineers are to play a role in addressing issues that exist at the intersection of technology and society, they must have a deep understanding of both technical competencies and of human factors. They must have the ability to empathize. In consideration of today's social, political, and environmental challenges, it has never been more important to instill social competencies into engineering education and practice, particularly around engineering design. This paper analyzes the previous literature on empathy in engineering education in North America and synthesizes the data to present the conceptualization that engineers have of empathy in education and practice.