As the current environmental crisis and depletion of our energy resources are pushing the Architecture, Engineering, and Construction (AEC) industry toward the design and construction of High-Performance (HP) buildings, new organizational and technological methods of practice, such as Integrated Design Process (IDP) and Building Information Modeling (BIM), have emerged to facilitate this transition. Consequently, Architecture schools are left with the duty of training practitioners with the required holistic vision and technical knowledge for designing HP buildings, technological abilities to work with new BIM tools, collaboration skills to work with cross-disciplinary team members, and theoretical knowledge to run the new processes. Scholars of architectural education are faced with a significant theoretical and practical knowledge gap on how to add all these new layers of knowledge and skills to what is an already saturated curriculum in architecture schools. To address this need, we developed a conceptual framework for teaching an integrated and BIM-based HP design studio for the MS program in Building Science. The experience was successful in creating an effective systematic method for integrating HP design elements in the students' projects, with all the teams achieving their project performance targets in six distinct HP categories of energy consumption, greenhouse gas emissions, health and wellbeing, water management, and resiliency, while meeting reasonable architectural qualities and economic criteria. The key elements of this pedagogical approach, including teamwork, a structured and iterative design process, decision-making mechanism with a high level of attention given to various performance metrics, the use of related BIM technologies, and the evaluation techniques, are introduced, discussed, and recommendations are proposed for future applications.

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Recent studies have focused on data-driven methods for building energy efficiency, by using simulated or empirical data, for energy-based design assessment rather than the common physics-based techniques, which are mostly time-consuming. In this paper, the feasibility of using seven different Machine Learning models, including three single models and four ensemble ones, is studied to predict annual energy demand and thermal comfort of the model. For this purpose, 3024 synthetic samples of a single zone model with seven input features are simulated through the EnergyPlus engine for training in addition to 360 unseen samples as testing data for accuracy reporting. Heating and cooling demands, in addition to five annual thermal comfort indices, are calculated for each data point and used as target indices. Results show Extremely Randomized Trees and Random Forest models had the highest R2 of 0.99 and 0.85 for cooling and heating demands respectively. Also, the R2 of these models for predicting annual comfort was between 0.71 and 0.95. Results are then used to develop a prediction framework of thermal comfort and energy demand performance in the early stages of building design, where most of the information about building characteristics is not yet known.@en

Purpose: In this study, a novel framework based on deep learning models is presented to assess energy and environmental performance of a given building space layout, facilitating the decision-making process at the early-stage design. Design/methodology/approach: A methodology using an image-based deep learning model called pix2pix is proposed to predict the overall daylight, energy and ventilation performance of a given residential building space layout. The proposed methodology is then evaluated by being applied to 300 sample apartment units in Tehran, Iran. Four pix2pix models were trained to predict illuminance, spatial daylight autonomy (sDA), primary energy intensity and ventilation maps. The simulation results were considered ground truth. Findings: The results showed an average structural similarity index measure (SSIM) of 0.86 and 0.81 for the predicted illuminance and sDA maps, respectively, and an average score of 88% for the predicted primary energy intensity and ventilation representative maps, each of which is outputted within three seconds. Originality/value: The proposed framework in this study helps upskilling the design professionals involved with the architecture, engineering and construction (AEC) industry through engaging artificial intelligence in human–computer interactions. The specific novelties of this research are: first, evaluating indoor environmental metrics (daylight and ventilation) alongside the energy performance of space layouts using pix2pix model, second, widening the assessment scope to a group of spaces forming an apartment layout at five different floors and third, incorporating the impact of building context on the intended objectives.

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Rapid urbanization and global warming have increased heat stress in urban areas. This in turn makes using indoor space more compelling and leads to more energy consumption. Therefore, paying attention to outdoor spaces design with thermal comfort in mind becomes more important since outdoor spaces can host a variety of activities. This research aims to introduce a machine learning-based framework to predict the effects of different urban configurations (i.e. different greening configurations and types, different façade materials, and different urban geometry) on outdoor thermal comfort through training a pix2pix Convolutional generative adversarial network (cGAN) model. For the training of the machine learning model, a dataset consisting of 208 coupled pictures of input and output has been created. The simulation of this data has been carried out by ENVI-met. The resulting machine learning model had a Structural Similarity Index (SSIM) of 96% on the test dataset with the highest SSIM of 97.08 and lowest of 94.43 which shows the high accuracy of the model and it could have reached an answer in 3 s compared to the 30-min average time for ENVI-met simulation. The resulting model shows great promise for assisting researchers and urban designers in studying existing urban contexts or planning new developments. HIGHLIGHTS Machine learning use in outdoor thermal comfort assessment has been investigated. Vegetation, urban geometry, surface albedo, and water bodies have been studied parameters. Vegetation and street orientation have the highest and water bodies have the least impact on outdoor thermal comfort. Pix2pix algorithm implementation could create thermal comfort maps with 96% SSIM.

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Building energy simulation software are in no short supply. However, their excessive complexity makes many of them inaccessible to building engineers and architects, for early-stage design decisions. Within this context, many research groups and software developers have been working on tools that 1) are publicly-available, 2) require minimal input data, 3) have short run times, and 4) are suitable for benchmarking purposes and calculating energy or cost-savings. Recent trends show that many of these tools leverage web interfaces to visualize energy data for benchmarking, as well as simulation results for building energy analysis. The current review paper aims to provide detailed information and discussion of twenty-five web-based energy simulation tools, developed in the past few years. Different aspects of the tools, including calculation method, inputs, outputs, and capabilities, are investigated. The strengths and limitations of the tools and their future opportunities for developers and researchers are presented and discussed. Finally, a decision matrix is proposed to help users with the tool selection process, based on four main criteria, i.e. accessibility, capabilities, flexibility, and comprehensiveness.@en

The growing interest in reducing energy consumption and the associated environmental impacts are promoting energy efficiency in buildings. Comprehensive energy consumption models affected by occupant behavior are needed to assess the techno-economic implications of adopting energy conservation measures (ECM). In this study, occupant energy behavior (OEB) in university dormitories was determined based on field studies. The main contribution of this study is to identify the sensitivity of OEB scenarios (austerity, normal, energy spender) on energy savings via energy retrofit and present a decision-making tool to help prioritize ECMs based on payback and thermal comfort improvements. ECMs could improve energy efficiency by 32%, 56%, and 60% with the energy spender, normal, and austerity OEB models, respectively. Furthermore, the effectiveness of the ECMs does not follow a consistent pattern. The most effective actions in the normal OEB models are wall insulation and airtightness, and in the energy spender models, HVAC and lighting systems are the most effective ECMs. The integrated analysis of ECMs including energy savings, thermal comfort improvement, and paybacks is required considering different OEB scenarios because our study showed that an ECM might be a mid-cost in the case of energy spender, and a low-cost ECM in the case of austerity users.@en