Different weather datasets are widely available in building energy modeling. However, choosing the right weather data became a questionable decision because of increasing concerns about the causes of climate change on the built environment. This study analyzes and compares different types of weather files using statistical considerations to determine to what extent Typical Meteorological Years (TMYs) data represent climatic conditions of a location for building energy modeling. Results for ASHRAE 90.1-2013 secondary school prototype building located in Houston, TX, showed that

energy models using TMY weather files underestimate total energy consumption and heating loads while overestimating cooling loads. This study recommended using multiyear energy modeling for design-decision making.

Reducing energy consumption in buildings is pivotal to reaching the global net-zero target as they contribute about 40% of energy-related carbon dioxide emissions. To achieve this holistic optimization of energy consumption in the global building stock, achieving net zero energy status only for a few buildings sparsely distributed across the national landscape may not yield the desirable outcome. A nascent evolving modeling schema called urban building energy modeling tries to address this gap by strategizing retrofit strategies for buildings on an urban scale. However, implementing such frameworks is primarily limited only to developed countries because of the availability of a rich existing database of digitized building footprints, which is not the case in developing countries. This study tries to bridge this gap by developing a framework using satellite imagery and machine learning techniques for developing a community building energy model and implementing the framework on a case study from India for validation. Employing this framework can help particularly in developing countries where building footprint details are not digitally available to arrive at appropriate energy reduction strategies community-wide.

The U.S. Department of Energy (DOE) offers building reference prototypes for energy use modeling in commercial and residential buildings. However, these reference prototypes have traditionally been treated in isolation, neglecting the impact of neighboring objects on local microclimate. In urban energy models, where the intricate interaction of urban elements significantly shapes environmental conditions, it becomes more important to reconsider the conventional treatment of building reference prototypes. In this paper we aim to discern potential disparities in energy consumption estimations using DOE prototypes at an urban scale. The Urban Modeling Interface (UMI) was chosen as the simulation platform to incorporate the shadow effect from neighboring objects on building energy use across six scenarios with different shadow coverage by neighboring objects. We found that trees as neighboring structures can decrease cooling load by up to 29%. These results highlight the importance of considering the urban context in energy use estimation of buildings.

This study presents a comprehensive approach to calibrating a building energy model using Bayesian calibration. The calibration process involves identifying the most influential parameters, defining prior distributions, and developing a meta-model for efficiency. Furthermore, a likelihood function is defined to quantify the model's ability to explain observed data, and Bayesian calibration is performed, resulting in posterior probability distributions for both parameters and simulation results. The analysis demonstrates that this calibration framework is efficient in fine-tuning a building energy model, resulting in simulation outputs closely aligned with metered data. Concurrently, it assigns a specific level of uncertainty to the obtained results. However, when the calibrated model is tested against a separate dataset, it exhibits challenges in predicting the observed data. The particular Case Study presented highlights the importance of high-quality and consistent observed data for better prediction ability. Furthermore, it also highlights additional potential influencing factors currently under investigation, contributing to the development of an effective working tool.

Residential buildings in Jordan make up 72% of the total share of buildings. Despite the increased awareness of Embodied Carbon (EC) in building design, it is still not common practice in the Jordanian construction sector to consider the embodied carbon of materials. This study aims to evaluate the carbon emissions intensity of building materials in dwellings, which constitute the dominant segment of the market share in Jordan.

The study proposes a method aligned with the method used to produce Environmental Product Declarations (EPDs), which is based on the Life Cycle Assessment (LCA) framework. This can then be used to evaluate the EC of housing units in Jordan. Alternative materials or strategies could be suggested to reduce the EC of Jordanian housing stock.

Keywords: Embodied Carbon emissions; LCA; EPDs; building materials.

This research delves into the factors affecting residents' perceptions of potential increases in electricity costs within residential buildings. Analyzing demographic variables like gender, age, employment, education, income, and household size, the study seeks their correlation with electricity consumption behavior. It also explores the influence of psychographic factors such as Knowledge (K), Environmental Awareness (EA), and Environmental Attitude (AT) on the willingness to pay (WTP) for increased electricity prices. Utilizing Spearman rank-order correlation, the study highlights the nuanced, occasionally indirect impact of demographic attributes on WTP, as well as the substantial but intricate roles of environmental awareness and attitude. A notable discovery is the significant yet nuanced influence of personal attitudes on WTP, contrasting with the milder correlations of knowledge and awareness. The outcomes illuminate the dynamic interplay between demographic characteristics, environmental cognition, and financial attitudes toward electricity costs. These insights provide crucial implications for developing comprehensive energy policies and engagement strategies to foster sustainable energy practices.

Internal thermal mass (ITM) as a well-known passive technique that provides lag and damping for extreme diurnal temperature fluctuations when combined with natural ventilation and night flushing. Thermally massive concrete slab elements, vital for ITM, often feature a compact solid design, limiting their thermal storage potential due to restricted contact with indoor airflow. With the advent of architectural geometry and digital fabrication, floor elements can be designed and fabricated with greater exposed surface area under the constraint of the same material volume, thus maintaining the same embodied carbon footprint, but increasing its thermal performance potential. This study examines the impact of increasing exposed surface area of ITM through geometric modulation while keeping the material volume constant to reduce the building’s embodied carbon. The objective is to investigate how varying ITM surface area, within a fixed material volume constraint, influences the indoor thermal comfort in a free-running building. We examine a case study of a midsize office building in two climatic zones (Tucson, AZ and San Diego, CA) with an increased ITM surface area of the structural slab. We then apply natural ventilation optimization and night flushing to further increase the ITM performance. The results show that in the hot-dry and marine climates expanding ITM surface area by 1.7 times yields improvement of 3% and 7% annual thermal comfort hours when coupled with natural ventilation. These findings suggest a substantial energy-saving potential for enlarging the surface area of ITM in buildings within the studied climates.

Integration of Building Information Modeling (BIM) and Building Energy Modeling (BEM) has potential to provide streamlined and accurate building energy predictions, steering architects towards sustainable building design. Yet, integration and interoperability between BIM and BEM tools pose major challenges to the Architecture, Engineering, and Construction (AEC) industry. In this research, BEM tools from three categories of BIM-integrated (Systems Analysis and Sefaira), BIM-interoperable (IDA ICE and GBS), and BIM-separated (eQUEST) BEM tools were investigated. The study evaluated BEM tools’ interoperability and integration with BIM, as well as accuracy in predicting buildings’ energy use. The study showed that despite the substantial BIM to BEM improvements, further developments on BIM-BEM integration and interoperability are necessary. If achieved, new developments could result in fully integrated and collaborative workflows.

This paper introduces a new algorithm that utilizes an iterative process to automatically generate thermal zones of energy models from building floor geometries when the actual HVAC zones are unknown or not yet designed, adhering to the ASHRAE standard 90.1 appendix G method. Our evaluations demonstrate that it is faster and less prone to error than the state-of-the-art auto-zoning tools, while its simulation results are close to those of a manually zoned energy model. It has potential applications in early-phase architecture design explorations and urban scale energy modeling.

Communities in rural Alaska face many housing challenges. Existing housing is often overcrowded, outdated, and not well designed for extreme weather conditions experienced in the arctic climate. In addition, heating oil and electricity are notably higher than average of the U.S. Increases in weather extremes and climate change have impacted Native Alaskan housing exacerbating energy inefficiencies and energy burden and impacting indoor air quality. Some retrofits through weatherization programs have been implemented and have helped promote improvements in existing housing in these communities. However, more alternatives are needed to help support housing improvements in these highly energy burdened areas. In order to address these challenges and improve energy efficiency, building energy simulation is a tool that can be used to estimate energy consumption of homes, and to estimate energy savings potential resulting from energy efficiency improvements both for individual and groups of homes. It can also be used to help identify the most effective energy efficiency measures to implement according to community needs. This study aims to develop a residential building prototype model for modeling rural communities in Alaska. This is used to evaluate energy consumption and preliminary energy savings potential estimates of these existing homes.

The perfect prediction of Diffuse Horizontal Irradiance (DHI) for the following day is essential for maintaining a stable power supply and minimizing energy losses. In this study, the proposed model predicts the DHI for an entire year using only two weeks of DHI data and readily obtainable meteorological parameters. The model proposed in this research maintains the reliability of the physical equations inherent in existing decomposition models while incorporating reinforcement learning to enhance error reduction. The model proposed has demonstrated the capability to perform long-term solar decomposition calculations without the need for further model updates.

There has been growing attention on energy-efficient buildings with Thermal Energy Storage Systems (TESS), which are designed not only to reduce their energy consumption but also take advantage of time-of-use (TOU) prices to reduce energy costs by efficiently scheduling and shifting loads. This paper proposes effective modeling solutions to simulate and validate a specific optimal control algorithm designed to operate the TESS and HVAC systems. Two unique modeling strategies are implemented on two widely used simulation platforms in the HVAC industry, EnergyPlus and Modelica, to represent the TESS and HVAC systems. The in-depth comparative analysis of these simulation platforms is provided highlighting their respective advantages and limitations. Then, the proposed approaches are demonstrated and discussed through case studies.

This study aims at dynamically controlling the cooling setpoint temperatures based on comfort-driven temperature distributions for two scenarios without and with ceiling fans. Leveraging a subset of the ASHRAE Global Occupant Behavior Database, real-time data from a thermal comfort field study in Singapore were extracted. To assess the energy implications of the dynamic setpoints, we simulated the cooling energy use for medium office building in Miami. Our results show that incorporating dynamic changes in occupant comfort can tailor HVAC systems while yielding potential energy savings of 1.3% for the case without fans and 4.9% for the case with fans.

The relationship between sustainable architectural design and human thermal comfort is significant. Proper design and construction methods are crucial in reducing energy consumption and enhancing people's comfort. Traditional Iranian cities, constructed 150 years ago with limited knowledge of air circulation and ventilation, employed passive systems to achieve satisfactory thermal comfort using natural resources. This paper investigates the environmental aspects of Shanashir, a unique architectural element in Bushehr, a hot and humid city in Iran, and its impact on human thermal comfort. Shanashir, a narrow balcony with a wooden shading façade, offers various environmental benefits. This research explores different Shanashir types and characteristics, using Design Builder and Ladybug software to analyze their impact on thermal comfort based on the Universal Thermal Climate Index (UTCI). The results of this research outline the optimum physical characteristics of Shanashir to maximize thermal comfort in semi-outdoor spaces and indicate that a Shanashir with moderate height and depth (1.2meters) and with a 45-degree angle roof provides fewer days with intense heat stress and a higher comfort zone.

The rapid development of 3D concrete printing technology has brought significant advancements to the construction industry. However, the hygrothermal behavior of 3D concrete printed walls remains an understudied area, despite its critical implications for building durability and performance. This study explores the hygrothermal performance of 3D concrete printed wall assemblies, focusing on moisture accumulation, frost damage, mold growth, and corrosion risks. Employing the WUFI 2D simulation tool, we compare various insulation materials, concrete types, and wall designs to identify the most effective wall assemblies? for 3D concrete printing. Our findings indicate that cellulose insulation, due to its higher moisture content, is less suitable for 3D concrete printed walls, posing a higher risk of frost damage. Conversely, insulation materials with higher density and vapor resistance demonstrate better hygrothermal performance. The wall assembly with a layer of concrete bead connecting the interior and the exterior concrete layers leads to higher water content. Moreover, varying levels of vapor resistance in different concrete types affect the overall drying capability. Despite high relative humidity levels, no visible mold growth is predicted in the simulated assemblies, suggesting adequate mold resistance in both insulations and concrete.

As societies worldwide strive to reduce carbon footprints and transition toward cleaner energy sources, grid-integrated district energy systems (DES) emerge as a pivotal player in achieving these objectives. The escalating complexity of DES necessitates adaptive, synergistic, and hierarchical control of heterogeneous systems to achieve common energy and cost conservation goals. Prior research highlights several challenges of model-based control techniques for DES, such as limited access to computational tools, prolonged durations to digital twin development, and the complexities associated with control design. In contrast, model-free control methodologies appear as a viable alternative. As a response, our study explores a reinforcement learning-based (RL) supervisory control to minimize the operational costs in a university campus DES. To enhance overall system efficiency, we utilize resource flexibility to improve DES operations by responding to fluctuations in utility prices. In this paper, we demonstrate the toolchain, and virtual testbed development, engineer a suitable RL reward, along with the learning from challenges. From the case study, the RL agent showcases a significant 32% net operational cost savings and a 13% peak demand reduction compared to the conventional thermal load following control. This research signifies the potential of RL-based control systems in optimizing the performance of complex DES and multi-energy systems involving several control points.

As a result of the recent increase in the need for silicon in solar panels, thin-film photovoltaic modules have the capacity to substantially penetrate the market. Thin-film technologies for photovoltaic, as a viable alternative for special implementation as presented in this project, have the potential to deliver a good performance. Due to the integration of photovoltaic on solar shading products unconsidered areas can be used for energy harvesting and has an positive influence for the total energy. The adaptability of thin-film cells opens up numerous application possibilities, especially in terms of surface cladding, as it can be considered a slim layer and does not require a costly metal structure for installation. A thin-film photovoltaic implementation on adaptive shading products is presented in this paper on a demonstrator building in Freiburg. The aim of this work is to give an overview of different thin-film solar cell technologies for applying on solar shading products as well as profitability investigation of such technologies.

This paper presents a novel model-free multi-agent Reinforcement Learning (RL) control method to enhance the resilience of community energy systems in island mode, which coordinates multiple objectives without the necessity of identifying system models that require expert knowledge. Specifically, a community-level coordinator agent is designed to allocate renewable energy resources among different buildings, and multiple building-level agents are developed to optimize load schedules based on limited energy resources and requirements of building loads and occupants’ comfort. In a two-day evaluation, our RL approach demonstrated a similar performance against MPC without requiring system models and formulation of optimization problems as required in MPC.

This paper presents a simulation-based assessment on how the deployment of ASHRAE Guideline 36: High-Performance Sequences of Operation for HVAC (G36) impacts the energy performance of G36. In this assessment, an EnergyPlus model and a control sequence in G36 are co-simulated. This EnergyPlus model represents a variable air volume system that serves Oak Ridge National Laboratory’s Flexible Research Platform 2 Building. When performing the assessment, we consider six cases, to represent various deployments. In Case 1 and 2, ideal and poor local control loops (LCLs) are considered, respectively. In Case 3 and 4, ideal LCLs with positive and negative sensor bias are considered, respectively. In Case 5 and 6, poor LCLs with positive and negative sensor bias are considered, respectively. Those assessment results suggests 1C sensor bias leads to 2.5 time changes in the energy savings from G36. They also reveal that the control performance of LCL result in 2.3 time differences in the peak power.

In land-scarce urban contexts like Singapore, achieving on-site net zero energy design is challenging due to limited space for photovoltaic (PV) installation and intense pressure to develop sites to maximize density. This study puts forward methods to identify an optimum (highest possible) density for net zero energy (NZE) residential precincts. A parametric model integrates building energy use and on-site renewable energy generation, with two optimization methods suggesting a gross plot ratio of 2.5 to 2.9 as the maximum viable density for net and near zero energy in Singapore. Results inform city planning for NZE districts, aiding policymakers, planners, developers and architects in identifying target densities and building layouts for energy-efficient urban design.