This research investigates a method of urban building energy simulation (UBES) by integrating Building Information Modeling (BIM), building simulation, and algorithm-based prediction to forecast the impact of surrounding conditions. In the urban context, building energy performances are determined not only by the individual building design but also by the building's surrounding context. Many energy performances are sensitive to outdoor and surrounding building conditions, such as neighbouring building volumes, heights, and spaces between buildings. However, such surrounding conditions were overlooked because they can exponentially increase the complexity of urban modeling and simulation. In that regard, the research sought to investigate a novel framework to take advantage of accurate performance simulations and algorithm-based fast predictions. This paper presents our UBES method implemented from three research phases: (i) building a parametric urban model in BIM to provide simulation inputs, (ii) creating a parametric simulation interface to produce training and validation data, and (iii) creating a prediction interface using a Support Vector Machine (SVR) algorithm. Lastly, the paper elaborates on the findings from the prediction results.

The research investigates the design and development of a serious game to teach green building design and energy literacy in rural middle schools in the United States. The paper presents a pilot study, education mini-game development integrated with parametric BIM and energy simulations. The game scenario was built on the developed science curriculum modules in our funded research, teaching building energy technologies such as daylighting, artificial lighting, window configurations, building materials, solar panels, etc. The mini-game, Illumi’s World, presents a baseline science lab and a media library of typical public schools in the United States. The players have the opportunity to improve energy literacy in several ways: manipulating the building configurations and the energy options, reviewing energy costs and emission level changes, and monitoring the performance from the game dashboards. This paper presents background theory, curriculum design, the mini-game development framework, methods and tools for energy simulation and BIM visualization, and the findings and challenges.

Systems thinking (ST) is a foundational cognitive skillset to advance sustainability education but has not been well examined for learners prior to higher education. This case study research in rural middle schools in the Midwestern U.S. examines systems thinking outcomes of a place-based energy literacy unit focused on energy-efficient building design. The unit employs the science of energy-efficient, green buildings to illuminate the ways in which energy flows between natural and built environments. The unit emphasized electrical, light, and thermal energy systems and the ways these systems interact to create functional and energy-efficient buildings. This study focuses on three case study classrooms where students across schools (n = 89 students) created systems models as part of pre- and post-unit tests (n = 162 models). The unit tests consisted of student drawings, annotations, and writings, culminating into student-developed systems models. Growth from pre- to post-test was observed in both the identification of system elements and the linkages between elements. System elements included in the models were common classroom features, such as windows, lights, and temperature control, suggesting that rooting the unit in place-based teaching may support ST skills.

Urban energy simulation research has been explored to forecast the impact of urban developments on energy footprints. However, the achievement of accuracy, scalability, and applicability is still unfulfilled in addressing site-specific conditions and unbuilt development scenarios. This research aims to investigate the integration method of urban modeling, simulation, and machine learning (ML) predictions for the forecasting of the solar radiation of urban development plans in the United States. The research consisted of a case study of Smart Growth development in the southern Kansas City metropolitan area. First, this study analyzed Smart Growth regulations and created urban models using parametric Building Information Modeling (BIM). Then, a simulation interface was created to perform simulation iterations. The simulation results were then used to create ML models for context-specific solar radiation prediction. For ML model creation, four algorithms were compared and tested with several data diagnosis techniques. The simulation results indicated that solar radiation levels are associated with block and building configurations, which are specified in the Smart Growth regulations. Among the four ML models, XGBoost had higher predictability for multiple urban blocks. The results also showed that the performance of ML algorithms is sensitive to data diagnosis and model selection techniques.