Search Results (85)

This study investigates the effectiveness of passive design in low-rise residential buildings located in arid desert climates, using the Dubai Solar Decathlon Middle East (SDME) competition as a case study. This full-scale experiment offers a unique opportunity to evaluate design solutions under controlled, realistic conditions; prescriptive, modeled performance; and monitored performance assessments. The prescriptive assessment reviews geometry, orientation, envelope thermal properties, and shading. Most houses adopt compact forms, with envelope-to-volume and envelope-to-floor area ratios averaging 1 and 3.7, respectively, and window-to-wall ratios of approximately 17%, favoring north-facing openings to optimize daylight while reducing heat gain. Shading is strategically applied, horizontal on south façades and vertical on east and west. The thermal properties significantly exceed the local code requirements, with wall performance up to 80% better than that mandated. The modeled assessment uses Building Energy Models (BEMs) to simulate the impact of prescriptive measures on energy performance. Three variations are applied: assigning minimum local code requirements to all the houses to isolate the geometry (baseline); removing shading; and applying actual envelope properties. Geometry alone accounts for up to 60% of the variation in cooling intensity; shading reduces loads by 6.5%, and enhanced envelopes lower demand by 14%. The monitored assessment uses contest-period data. Indoor temperatures remain stable (22–25 °C) despite outdoor fluctuations. Energy use confirms that houses with good designs and airtightness have lower cooling loads. Airtightness varies widely (avg. 14.5 m³/h/m²), with some well-designed houses underperforming due to construction flaws. These findings highlight the critical role of passive design as the first layer for improving the energy performance of the built environment and advancing toward net-zero targets, specifically in arid desert climates. Full article

Energy retrofitting of historic buildings presents complex challenges, particularly when using internal insulation strategies. While such interventions can enhance thermal comfort and reduce energy demand, they can also pose risks of condensation and mold formation, thereby reducing usable space. This paper proposes an evaluation methodology for assessing the performance of internal insulating panels within a multicriteria framework to support decision-making during the design phase. The approach, scalable to various contexts, is grounded in a digital workflow that integrates heritage building information modeling (HBIM), visual programming (VP), and building energy modeling (BEM) to create a decision-support tool for renovation designers. The methodology, tested on a building located in Bologna (Italy), allows for assessing internal insulation systems with varying thermophysical properties and performance characteristics, and evaluating how they affect space- and wall-level key performance indicators, including condensation risk, energy efficiency improvement, and usable space reduction. The research was conducted under the Horizon Europe HERIT4AGES project, which aims to develop reversible, innovative insulation panels fabricated from local and recycled materials for historic building retrofitting. Full article

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) in Building Energy Management Systems (BEMSs) offers transformative potential for improving energy efficiency, enhancing occupant comfort, and supporting grid stability. However, the adoption of these technologies in the European Union (EU) is significantly influenced by a complex regulatory landscape, including the EU AI Act, the General Data Protection Regulation (GDPR), the EU Cybersecurity Act, and the Energy Performance of Buildings Directive (EPBD). This review systematically examines the legal, technological, and economic implications of these regulations on AI- and IoT-driven BEMS. Following the PRISMA-ScR guidelines, 64 relevant sources were reviewed, comprising 34 peer-reviewed articles and 30 regulatory or policy documents. First, legal and regulatory barriers that may hinder innovation are identified, including data protection constraints, cybersecurity compliance, liability concerns, and interoperability requirements. Second, technological challenges in designing regulatory-compliant AI and IoT solutions are examined, with a focus on data privacy-preserving architectures (e.g., edge computing versus cloud processing), explainability requirements for AI decision-making, and cybersecurity resilience. Finally, the economic opportunities arising from regulatory alignment are highlighted, demonstrating how compliant AI and IoT-based BEMS can enable energy savings, operational efficiencies, and new business models in smart buildings. By synthesizing current research and policy developments, this review offers a comprehensive framework for understanding the intersection of regulatory requirements and technological innovation in AI-driven building management. Strategies are discussed for navigating regulatory constraints while leveraging AI and IoT for energy-efficient, intelligent building operations. The insights presented aim to support researchers, policymakers, and industry stakeholders in advancing regulatory-compliant BEMS that balance innovation, security, and sustainability. Full article

The current climate and energy crises demand innovative approaches to operating buildings more sustainably. HVAC systems, which significantly contribute to a building’s energy consumption, have been a major focus of research aimed at improving operational efficiency. However, a critical factor often overlooked is the seasonal and hourly variation in solar radiation and the resulting solar heat gain, which heats specific rooms differently depending on their orientation, type, and location within the building. This study proposes a simulation-based strategy to reduce HVAC energy use in hotels by allocating guests to rooms with more favorable thermal characteristics depending on the season. A high-resolution building energy model (BEM) was developed to represent a real 17-floor hotel tower in Madrid, incorporating detailed geometry and surrounding shading context. The model includes 439 internal thermal zones and simulates solar radiation using EnergyPlus’ Radiance module. The simulation results revealed large room-by-room differences in thermal energy demand. When applying an energetically optimized guest allocation strategy based on these simulations and using real occupancy data, potential reductions in HVAC energy demand were estimated to reach around 6% during summer and up to 20% in winter. These findings demonstrate that data-driven guest allocation, informed by physics-based building simulations, can provide substantial energy savings without requiring physical renovations or equipment upgrades, offering a promising approach for more sustainable hotel operation. Full article

Building information modeling (BIM) has revolutionized integrated planning by optimizing costs, schedule, and material use. However, building energy modeling (BEM) remains underutilized in early design stages due to interoperability challenges between BIM and BEM tools. This study addresses these challenges by exploring standardized exchange requirements and introducing a novel toolchain that bridges BIM and BEM workflows. In the BIM2IndiLight project, over 400 standardized properties for daylighting, artificial lighting, and façade systems were validated, revealing the advantages and limitations of parameter standardization. Building on these insights, the BIM2BEM-Flow project developed a three-step toolchain that efficiently manages project- and company-specific properties, defines mapping rules, and integrates parameters via a BIM plugin for validated IFC export. The results demonstrate that combining standardized properties with a flexible, workflow-driven toolchain significantly enhances data exchange and interoperability between BIM and BEM. This integrated approach supports early-stage energy performance optimization and offers a promising pathway toward more efficient design processes in the AECO industry. Full article

The residential sector accounts for over a third of the world’s energy use. Even though this ratio is lower in Mexico, there is a pressing housing deficit, especially regarding low-cost homes. This research aimed to create a reference building (RB) to understand the current energy consumption of multi-family buildings across different climatic zones. The aim was to assess their energy performance and promote reduced energy requirements as a guideline for designing and constructing affordable, low-energy, or zero-energy buildings. The present work conducts a diagnosis of the current energy consumption of multi-family buildings in eight cities in Mexico. First, a reference building was developed to represent typical Mexican building geometry and construction practices, and then the building’s fixed and variable energy requirements were simulated. Finally, a comparison was made between the energy requirement and the data reported by the national energy survey. Therefore, it was possible to generate a reference building from national data sources complying with national regulations, where materials, occupant behavior, and equipment were chosen to help represent the building’s thermal behavior. Domestic water heating was identified as a driver of variable energy requirements in all cities. In contrast, the simulated heating and cooling requirements were directly linked to the city’s climate. Electricity bills tended to mostly correspond with the results that excluded the use of heating systems. Lastly, while comparing LPG (Liquified Petroleum Gas) consumption was challenging due to the unavailability of national data, LPG requirements were closely estimated for temperate cities. The definition of a reference building is an important step towards developing nZEB in Mexico, as these buildings are valuable tools that can contribute to the energy evaluation of specific types of buildings. This characteristic makes them convenient for revising a building code or setting new national energy policy goals. Full article

Current building energy modeling (BEM) tools lack the capability to inherently simulate the impacts of urban microclimates on building energy performance. While efforts have been made to integrate BEM with Urban Microclimate Modeling (UMM) tools, their ability to capture spatial and seasonal microclimate variations remains limited. This review critically evaluates existing urban microclimate-integrated BEM approaches and their effectiveness in modeling the complex interactions between urban form, microclimate, and building energy performance. Through an analysis of 94 research articles, the review first examines the influence of urban form on microclimates, followed by an assessment of how microclimatic conditions impact building energy use. Additionally, it evaluates conventional modeling frameworks employed in BEM tools and their limitations in representing dynamic microclimatic variations. The findings emphasize the non-linear heat exchange relationships between urban form and microclimate, typically modeled using computationally intensive Computational Fluid Dynamics (CFD)-based UMM tools. This review introduces a classification of heat exchange types: atmospheric heat exchange, involving air temperature, wind, and humidity, and non-atmospheric heat exchange, driven by radiative interactions with surrounding urban surfaces. The study further highlights that modifying standard weather files and heat transfer coefficients alone is insufficient for BEM tools to accurately capture near-surface microclimate variations. By identifying critical insights and research gaps, this review establishes a foundation for advancing next-generation urban microclimate-integrated BEM approaches, emphasizing the need for computationally efficient and dynamically responsive modeling techniques. Full article

This article raises awareness of Building Information Management (BIM) and its significance for Construction 4.0. BIM is often mistakenly understood only as a 3D model of a building object, but its true potential lies in the information associated with the model (e.g., mechanical and physical properties, costs, etc.). Models can subsequently be used in the building energy management (BEM) at all stages of the building object’s life cycle. This article focuses on the possibility of creating a model using available libraries in the Czech Republic provided by manufacturers and suppliers of building materials and the subsequent use of the model for energy modelling. The results obtained from computational modelling are then compared with real values measured on a timber construction located in Ostrava (Czech Republic). These results show that properly configured BIM modelling allows faster data processing while maintaining the quality of outputs and results. Additionally, there is potential to eliminate common pitfalls in the design and subsequent processing of thermal assessments of building objects. Full article

Modular construction provides numerous benefits over traditional methods, especially when combined with digital technologies, offering a faster, safer, leaner, and more sustainable construction environment. This literature review follows the PRISMA method to assess recent advancements in digital-oriented modular construction towards a sustainable and climate-neutral built environment, identifying research trends and gaps based on three pillars: digital tools, building solutions, and environmental sustainability. This review examines the integration of digital technologies with modular construction methods, extending the analysis to circular and bioclimatic efforts, renewable energy sources, and passive building design strategies. While most articles focus on BIM uses, there is an increasing emphasis on IoT applications that leverage real-time data to achieve sustainability goals. However, no full-scale automated Digital Twin was found in this context. Additionally, Building Energy Modelling (BEM) and Life Cycle Assessment (LCA) tools are frequently discussed, reflecting the push for climate-friendly housing. Despite the interest in parametric and generative design, the integration of machine learning and artificial intelligence applications for sustainable modular construction strategies remains underexplored. Only a few papers acknowledged reaching nZEB requirements despite the great emphasis on passive building solutions and renewable energy sources that contribute to this goal. However, material circularity has yet to achieve its full potential for sustainable modular construction. Moreover, there is some interest in off-grid modular buildings, although further research should be undertaken to analyse the modular construction feasibility for sustainable off-grid communities. Furthermore, the findings highlight the potential of digitalisation in modular construction to enhance efficiency and ensure environmental sustainability within the Architecture, Engineering, and Construction (AEC) sector. Full article

Reducing the energy consumption of the existing building stock is of paramount importance in the race to reach national and international climate goals. While multiple initiatives are in place and provide guidance, heritage-protected buildings are often not part of the equation. Protected buildings make up a large share of the existing building stock and therefore offer large savings potential. In Trondheim, Norway, alone, that share is close to 10%, which demands the establishment of representative retrofitting cases. A case study of the central buildings on the NTNU campus was established to specifically test passive retrofitting measures, which are greatly affected by heritage protection. The application of measures selected in collaboration with heritage authorities led to overall energy savings of 16% to 18%, while the energy for heating alone was reduced by 34% to 40%. The reductions were especially prominent during cold winter months, where overall consumption peaks were reduced by up to 37%, greatly decreasing the dependence on cold outdoor temperatures. The results make a case for the application of passive retrofitting measures to heritage-protected buildings despite them not reaching deep energy retrofitting goals, especially in cold climates and alongside other energy-saving or -producing measures. Full article

Conducting accurate and quick energy analyses for retrofitting purposes became crucial for Architecture, Engineering, Construction, and Operations (AECO) markets worldwide. This paper investigates the possibility of determining and implementing an architect-friendly BIM-based energy analysis for Morocco’s Energy Efficiency Retrofitting (EER). For this matter, a socio-technical approach is adopted. The technical part of the study assesses two Building Information Modeling (BIM) tools (ArchiCAD v26 and Revit v23) regarding their Building Energy Modeling (BEM) capabilities for EER. Their evaluation uses a confirmed case of EER located in Marrakech as a baseline to compare the two tools. The social part investigates the AECO market of Marrakech, where the baseline is located, to anticipate the strengths and limits that might influence the implementation of the BIM-based BEM for an EER workflow in architecture studios. The technical part underlines the significant potential of the chosen BIM tools: ArchiCAD is more flexible, customizable, and accurate regarding energy analysis results, while Revit allows for the strong integration of regulations within its process. The social investigation showcases the studied market’s potential for adopting BIM and BEM for EER but highlights the issue of persisting 2D Computer-Aided Design (CAD) workflows. The same social investigation also suggests that combining BIM tools (ArchiCAD and Revit) in the same workflow might benefit the studied market more because of AECO professionals’ diverse needs. These findings constitute a first base for the national implementation of a BIM-based BEM for EER. They also hold the potential to be used in emerging economies with similar AECO markets. Full article

BIM (building information modeling) is widely recognized for enhancing the efficiency and precision of building energy modeling (BEM), primarily by reducing model development time and improving model accuracy. This paper presents a novel framework leveraging “openBIM” to standardize and harmonize BIM-driven solutions for energy simulations, facilitating comprehensive operational carbon impact assessments. Unlike existing approaches, our framework uniquely integrates information delivery specifications (IDS) with openBIM standards to define the minimum data requirements within the IFC schema, tailored to various levels of development (LOD). This innovation ensures consistent data exchange and interoperability across diverse energy modeling and simulation tools, addressing common challenges of data fragmentation and inaccuracy in operational carbon assessments. By advancing the current state of the art, the proposed framework empowers energy modelers, LCA analysts, and asset managers to streamline IDS implementation, fostering more efficient and reliable construction industry practices. This research thus marks a significant step towards achieving more sustainable building projects through enhanced data-driven insights. Full article

With the increasing concern about global warming and future climate change, attention has been drawn to the need to reduce building energy use through improving buildings’ energy efficiency. Existing residential buildings constitute the largest percentage of energy demand and carbon dioxide emissions, and hence, offer significant potential for energy savings and reductions in greenhouse gas emissions. This review aimed to provide an in-depth analysis of current research on improving the energy efficiency of existing residential buildings in Libya and neighbouring Mediterranean countries, with a focus on research methods and tools utilised in this domain. This helped to identify potential areas of intervention to improve the energy efficiency of existing residential stock in Libya. Under identified themes, this study systematically analysed 44 publications of high relevance to the subject area found in Scopus, ScienceDirect, and Google Scholar. The results reveal that while energy retrofitting is a research area of interest in the region considered, studies in the Libyan context are limited. There is also limited attention to achieving net zero energy and embodied carbon reductions, specifically in the Libyan context. Moreover, some weaknesses were identified for most of the studies reviewed, including those in the Libyan context, related to the credibility and reliability of the energy models used in the various literature. Full article

The building sector is a major contributor to resource consumption, energy use, and greenhouse gas emissions. Sustainable architecture offers a solution, leveraging Building Energy Modeling (BEM) for early-stage design optimization. This study explores the use of genetic algorithms for optimizing sustainable design strategies holistically. A comprehensive analysis and optimization model was developed using genetic algorithms to individually optimize various sustainable strategies. The optimized strategies were then applied to a pre-existing building in Kabul City, a region facing significant environmental challenges. To enhance accuracy, this study integrated energy simulations with Computational Fluid Dynamics (CFD). This research combines genetic algorithms with energy simulation and CFD analysis to optimize building design for a specific climate. Furthermore, it validates the optimized strategies through a real-world case study building. Optimizing the Window-to-Wall Ratio (WWR) and shading devices based on solar exposure significantly improved the building’s energy performance. South (S)-facing single windows and specific combinations of opposing and adjacent windows emerged as optimal configurations. The strategic optimization of building component materials led to substantial energy savings: a 58.6% reduction in window energy loss, 78.3% in wall loss, and 69.5% in roof loss. Additionally, the optimized pre-existing building achieved a 48.1% reduction in cooling demand, a 97.5% reduction in heating demand, and an overall energy reduction of 84.4%. Improved natural ventilation and controlled solar gain led to a 72.2% reduction in peak-month CO₂ emissions. While this study focused on applicable passive design strategies, the integration of advanced technologies like Phase Change Materials (PCMs), kinetic shading devices, and renewable energy systems can further improve building performance and contribute to achieving net-zero buildings. Full article

Model calibration refines design-stage inputs to align with real-world building performance. Accurate parameter selection, especially for highly sensitive variables like air leakage, is crucial. This study compared two building energy model calibration methods. The “classic” method adjusted indoor air capacitance, internal mass, and air infiltration, while a novel method focused on capacitance and internal mass, using empirical data for infiltration. The infiltration values were calculated using the decay equation and the EnergyPlus equations with site-specific coefficients. A triple validation assessed model performance in terms of temperature (CIBSE TM63), energy consumption (minimization), and indoor air quality (represented by ${\mathrm{CO}}_2$ levels in accordance with the ASTM D5157 Standard). Results demonstrated the novel method’s superiority across all three performance metrics. All calibrated models met the CIBSE TM63 criteria even during the validation period, which was five times longer than the training period. Compared to the classic method, models incorporating dynamic empirical infiltration showed a 29% and 26% improvement in MAE and RMSE, respectively, in temperature prediction. In energy consumption results, the novel method models presented a 31% reduction, and for ${\mathrm{CO}}_2$ level agreement, these models achieved a 130% higher R² value than the classic model. In addition, the classic method’s infiltration values failed to meet ASTM D5157 requirements, suggesting reliance on unrealistic parameter values for accurate temperature representation. The incorporation of calculated air leakage data into the BEM allowed a more realistic estimation of capacitance and internal mass values, emphasizing the importance of accurate air infiltration modeling for overall model reliability. Full article