Building Energy Performance Research Articles

Thermal and energy efficiency in 3D-printed buildings: Review of geometric design, materials and printing processes

Applying 3D printing in construction is a promising, sustainable alternative to conventional methods. However, the thermal and energy performance of 3D-printed buildings remains underexplored, particularly regarding the interactions between geometric design, material selection, and printing parameters. This review addresses this gap by critically examining how the main steps of the printing process impact the thermal efficiency of 3D-printed buildings. Key findings indicate that material development needs to focus on mixes that balance thermal properties,printability,and structural integrity. The review demonstrates how optimising wall cross-sections and cavity shapes can reduce thermal conductivity and enhance energy efficiency. Innovative geometric designs, such as cellular and honeycomb structures, have shown improvements in thermal insulation. Optimising printing parameters, such as extrusion rate and nozzle travel speed, is crucial to minimising thermal bridges and reducing material anisotropy. Advanced numerical models, validated by experimental data and incorporating factors like inhomogeneous porosity, anisotropy, and surface roughness, are essential for accurately predicting the thermal behaviour of 3D-printed structures. The review underscores the need for large-scale, long-term performance studies of 3D-printed buildings under diverse climatic conditions. Additionally, developing standardised fabrication protocols and testing methods is essential for ensuring consistent quality and broader adoption. Addressing these research gaps is crucial to fully realising the potential of 3D printing in sustainable construction and accelerating the adoption of additive manufacturing in the construction sector.

An integrated model of the driving mechanism for the building energy performance gap

The gap between the designed and actual energy performance of buildings (BEPG) poses a severe challenge to achieving building energy efficiency goals and has garnered global attention. However, previous studies have predominantly concentrated on the identification of factors influencing BEPG and have not systematically explored the interrelationships among these factors. In this study, the Integrated Decision-Making combined with Trial Evaluation Laboratory and Adversarial Interpretive Structural Modeling (DEMATEL-AISM) were employed to explore the driving mechanism for BEPG. Initially, a total of 40 influencing factors were identified through literature review and interviews, and 18 representative factors were selected from the building lifecycle perspective. Subsequently, the importance level and causal attributes of each influential factor were assessed by the DEMATEL method. Furthermore, a multi-level hierarchy was constructed based on the AISM model to illustrate the intricate interrelationships among the representative factors. The results indicated that there were seven cause factors and eleven effect factors for BEPG, with the six most significant factors were setting errors in simulation, lack of supervision, performance of equipment, differences in building performance, occupant behavior, and improper design. Additionally, the hierarchical structure model was categorized into five levels, which comprehensively indicated the driving relationship between the 18 influencing factors and the generation mechanism of BEPG. Finally, nine targeted strategies to address BEPG were proposed. This study offers new insights into the mechanisms of BEPG formation through the integration of DEMATEL and AISM, providing a robust framework for researchers and practitioners to systematically understand and address BEPG, as well as essential guidance for policymakers to develop effective strategies to reduce BEPG.

A nonparametric least squares regression method for forecasting building energy performance

The Convex Nonparametric Least Squares (CNLS) method assumes that the regression function is either concave or convex to forecast building energy performance. However, there may be instances where the regression function exhibits both concave and convex patterns, rendering this assumption invalid. This paper aims to address this drawback and to derive a new method called Monotone Nonparametric Least Squares (MNLS), which incorporates both concavity and convexity constraints in CNLS. It is proved that MNLS has a better goodness-of-fit performance compared to CNLS. Since MNLS contains both concave and convex portions, it is not sufficient to rely solely on the concavity assumption (or convexity assumption) during the forecasting process of building energy performance. To tackle this issue, using both concave and convex portions separately and then combining the resulting forecasts is suggested. An illustrative example is provided, and the energy performance of Hong Kong secondary schools is used as an application to demonstrate the goodness-of-fit of MNLS.

Impacts of the 4.5 and 8.5 RCP global climate scenarios on nearly zero energy building performance: Sensitivity analysis about operative conditions of insulation materials and PV system degradation

The paper gives a directive toward assessment of resilience of building designed to comply the nearly zero energy standard in the current conditions and also points out the gap in the existing approach for evaluating the effect that the climate changes could have on building material behavior and systems degradation. A method is introduced for evaluating how the variation of energy need and in-situ production can modify the energy balance under the representative concentration pathway of the intermediate (RCP 4.5) and the worst emissions scenario (RCP 8.5). The results for a case study indicate that under RCP 4.5 scenario, the polyethylene foam insulation has the higher increment of heating demand (+ 9.8%) instead the cooling demand decreases by −4.2% (RCP 8.5) and −4.6% (RCP 4.5). The innovative materials are less affected (<1%) by temperature variations. On the other hand, the PV system degradation causes the reduction of in-situ production from −6.9% (RCP 8.5) and −8.0% (RCP 4.5). However, the analysis of the hourly balance suggests that the building resilience would be not compromised. Indeed, by evaluating the ratio between the self-consumption and the total request, it increases compared to the current conditions of around 2%.

Balcony design recommendations to enhance daylight, thermal and energy performance of mixed-mode office buildings

The use of balconies in hot climates holds the potential to block direct solar radiation thus reducing energy consumption for cooling and enhancing visual comfort. However, balconies may also diminish daylight availability within the room, thereby affecting occupants’ satisfaction and wellbeing. This study aimed to provide balcony design recommendations through a parametric analysis of various combinations of balcony design parameters (balcony depth, width, location on the façade, and parapet type), along with key building design parameters (façade orientation, floor level, and glazed door width), considering a multi-domain objectives assessment. The results demonstrate that balcony design can effectively enhance visual comfort, thermal performance, and energy efficiency while ensuring optimal daylight availability in office rooms located in subtropical climates. Recommendations for balcony design were tailored to each façade orientation and thoughtfully combined with the glazed door width. One optimal combination, featuring a 3-meter-wide glazed door and a 2-meter-deep balcony, proved beneficial for all façade orientations across all floor levels. However, the choice of parapet type should align with the balcony’s location, which is directly correlated with the room’s depth. The insights gained from this study offer valuable information for building designers, highlighting that balconies should not be designed solely as decorative façade elements or spaces for building services. Furthermore, the cross-analysis method developed in this study can be applied by researchers and designers to their own case studies.

Multi-objective optimization for energy-efficient building design considering urban heat island effects

Building energy performance (BEP) associated with climate change and urban heat island effects (UHI) play an important role in urban sustainable development. To predict and optimize BEP under various socioeconomic scenarios, a new framework combining the physical simulation modeling integrated explainable machine learning and multi-objective optimization is proposed in this study. A Grasshopper-based simulation model incorporates BO-LGBM (Bayesian optimization-LightGBM) is developed to construct a solid prediction system, which tends to tune the hyperparameters accurately and explain more details with the aid of SHapley Additive explanation (SHAP). Two major aspects, including the building energy use intensity and indoor thermal comfort, are modeled by considering the different Shared Socioeconomic Pathways (SSPs) climate change scenarios in the near and far future. A multi-objective optimization method is employed to find an optimal solution for energy-efficient building design under constraints or uncertainties. Key findings include a 54% improvement in the Pareto front for building energy optimization and a significant impact of SSP585 scenarios on future energy consumption. The main novelty lies in the incorporation of machine learning into a physical model to achieve energy-efficient building design in urban contexts by considering UHI effects and climate change, offering actionable strategies for BEP assessment and promoting sustainable city planning.

A probabilistic framework for stranding risk assessment and EPBD IV scenario analysis for the Italian building stock

The building sector is a major contributor to global GHG emissions, constituting an essential player in national and global decarbonization strategies. In this framework, governments have started requiring higher building energy performances to reduce emissions, with the new European Energy Performance of Buildings Directive (EPBD) representing a further step in this direction. Such requirements could be enforced at a large scale with specific penalties for the property owners, such as the stranding of non-compliant assets, making them not rentable or marketable until requirements are met. A few countries have already applied these measures but with a limited extent and effective implications compared to the decarbonization goals. Thus, while the consequent impact is potentially considerable given that a significant share of existing buildings would be stranded, a high uncertainty characterizes its assessment, calling for a probabilistic approach. However, no probabilistic methods have been formulated for stranding risk. This paper critically reviews the state-of-the-art approaches for stranding assessment of buildings. A probabilistic framework based on methodologies derived from natcat risk assessment is also presented. A scenario-based analysis is carried out on the Italian building stock and compared to the impact of natural hazards. Results reveal that stranding risk can be relatively significant in terms of expected monetary losses. The categorization of this risk as sustainability-induced “creative destruction” is also discussed. The findings provide insights into which policies would be required to implement decarbonization pathways for the building stock while underlining how energy retrofit design can adapt to mitigate stranding risk.

Analysis of energy performance and load matching characteristics of various building integrated photovoltaic (BIPV) systems in office building

Building Integrated Photovoltaic (BIPV) is a key technology for achieving zero-energy buildings by generating electricity and reducing energy consumption. Although many studies have analyzed the impact of BIPV on building energy efficiency, there is a lack of comprehensive comparative analysis of various BIPV systems. This study compared the impact of various BIPV systems on office building energy performance and provided optimal solutions for different climate zones. Simulation models for PV rooftop, PV window, and PV shading systems were developed and validated in EnergyPlus. The energy performance of BIPV systems in different Chinese climates was evaluated using these models. The results show that the PV window and PV shading have the most power generation capability in Beijing, and the PV rooftop has the most power generation capability in Kunming. Energy savings in Kunming are the highest with PV rooftop, PV windows, and PV shading at 111.78 %, 39.36 %, and 44.43 %, respectively. Finally, the study calculated the self-sufficiency rate (SSR) and self-consumption rate (SCR), and their ratio, along with the load matching ratio, were used to assess loading matching. Optimal BIPV schemes were identified for each climate zone, revealing that the PV rooftop combined with the PV shading system achieved the best load matching in Kunming, Guangzhou, Changsha, Beijing, and Harbin, with ratios of 82.00 %, 69.38 %, 70.88 %, 74.78 % and 67.27 %, respectively. These results provide a valuable reference for implementing the BIPV system to achieve zero energy consumption in office buildings under different climatic conditions in China.

Optimal HVAC setpoints for energy efficiency and thermal comfort in Chinese residential buildings

The easiest way to improve energy efficiency and thermal comfort in existing buildings is to adjust setpoints. However, although Chinese standards regulated the heating and cooling setpoints for different climate zones in China, they lacked consideration for occupants’ thermal comfort and ignored the ventilation setpoints. Therefore, this study aimed to identify optimal heating, cooling, and ventilation setpoints at the design stage to enhance building energy performance and indoor thermal comfort in Chinese residential buildings. This study used DesignBuilder to simulate the energy usage and thermal comfort with different setpoints and judged which combination was the best. To conduct this study across the whole of China, the typical cities in different climate zones were selected by using machine learning. Results indicated that adjusting setpoints in Harbin, Beijing, and Shanghai could increase comfortable days by 43.95 %, 54.23 %, and 23.36 % respectively, with minimal impact on energy usage. In Guangzhou, energy usage could be reduced by 7.56 % with a 5.91 % decrease in comfortable days, while Kunming could see an 11.11 % increase in comfortable days with a 5.92 % rise in energy consumption. Additionally, sensitivity analysis revealed that activity template variation was most critical for energy usage in Harbin, Beijing and equipment power density variation was most important for thermal comfort in Shanghai, Guangzhou, and Kunming. Furthermore, proposed setpoint combinations demonstrated resilience to future weather conditions in most typical cities, except for Guangzhou. These findings provide valuable insights for designers and researchers aiming to retrofit residential buildings, emphasizing the importance of considering both energy efficiency and occupant comfort. This study introduces innovative approaches like comprehensive setpoint optimization and machine learning for typical city selection, providing practical solutions to improve energy efficiency and thermal comfort in residential buildings.

Spent Coffee Grounds-Based Thermoplaster System to Improve Heritage Building Energy Efficiency: A Case Study in Madonie Park in Sicily

This study reports on the application of an innovative plastering system that reuses organic waste, namely spent coffee grounds (SCG), to improve energy efficiency in historical buildings according to the European Green Deal. The case study was conducted in the village of Polizzi Generosa, selected from 21 small villages located in the extensive UNESCO Geopark of Madonie Park in Sicily. Over time, traditional plasters used in Madonie buildings have shown durability issues due to thermal and hygrometric stresses caused by significant temperature fluctuations in the area. Moreover, much of the considered architectural heritage lacks energy efficiency. Given the global increase in coffee production and the need for more sustainable waste management systems, this investigation proposes an ecological method to reuse SCG in plaster formulation, thereby enhancing the circular economy. To achieve this, many thermoplaster formulations were developed, and the best-performing one, considering both material and aesthetic compatibility with historical buildings, was selected for a real-world application. Additionally, virtual modeling and energy simulations were conducted to test the energy performance of a traditional building in Polizzi Generosa using SCG-based thermoplaster in comparison to traditional lime mortar and commercial alternatives. The real-world application demonstrated the technical feasibility of the process, and the energy simulations showed an improved building masonry energy performance of 0.788 W/m2K and an 11% improvement compared to traditional plaster. Results clearly indicate that SCG can be successfully reused to produce eco-friendly bio composite plasters, providing a more sustainable housing option. This approach offers a durable and cost-effective alternative for housing solutions that meet regulatory requirements for energy efficiency, serving as a smart, highly sustainable, and long-lasting choice for the construction sector. Finally, this result supports the research goal of transforming the 21 municipalities of Madonie into smart and green villages, with the “Smart Coffee-House” exemplifying intelligent rehabilitation processes of existing heritage buildings.

MULTI-OBJECTIVE OPTIMIZATION STUDY OF RESIDENTIAL ENERGY CONSUMPTION BASED ON EDH AND THE STOCHASTIC OPERATION OF AIR CONDITIONING

ABSTRACT Current requirements for residential energy-efficient design solutions in China are relatively homogeneous, requiring only improvements in overall building energy performance, often ignoring the energy differences between different residential housing units. This paper proposes a multi-objective research framework based on an occupant behavior model of air conditioning and a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to study energy-efficient design solutions for residential envelopes. The optimal energy-saving design scheme has a 22.56% and 35.34% reduction in energy consumption difference between residential units and overall building energy consumption, respectively, both of which have been optimized substantially. Moreover, the overall energy performance of the building can be improved when the “energy performance difference between housing units” (EDH) is reduced, which verifies that EDH is an important criterion for improving the energy performance of residential housing units.

Audit of opportunities for improving the energy performance of office buildings by renewable sources

Audit of opportunities for improving the energy performance of office buildings by renewable sources

APPLICATION OF MODULAR CSIPS TO TRADITIONAL KOREAN HANOK TIMBER STRUCTURES FOR THERMAL AND ENERGY PERFORMANCE

ABSTRACT This study examines the thermal and energy performance capabilities of composite structural insulation panels (CSIPs) for traditional Korean architecture, specifically Hanok. Hanok buildings, renowned for their cultural and architectural significance, often face thermal efficiency and energy consumption challenges. This research aims to explore the environmental potential of CSIPs to improve the thermal performance of Hanok structures while maintaining the traditional aesthetics of these buildings. Simulations were conducted to analyze the thermal behavior, heat flow, and energy use intensity (EUI) by wall cases of Hanok buildings, applying continuous-fiber-reinforced thermoplastic composites as a type of CSIP. The findings demonstrated that applying continuous-fiber-reinforced thermoplastic composites (CFRTCs) to the target Hanok building significantly enhanced the thermal performance, including the thermal bridge and envelope airtightness, as these composites can reduce the thermal conductivity and minimize heat loss, thus improving the insulation capabilities. Furthermore, adopting modular CSIPs in Hanok architecture offers environmental benefits and aligns with sustainability and constructability goals. By reducing reliance on mechanical heating and cooling systems, CSIP-enhanced Hanok buildings can contribute to lower energy consumption and greenhouse gas emissions. This simulation study highlights the significant potential of CSIPs with fiber-glass-reinforced thermoset (FRT) polymer skins to enhance the thermal and energy performance of traditional Korean Hanok buildings. CSIPs as applied to the target Hanok can potentially reduce heating energy consumption by approximately 21% to 29%, leading to overall energy savings of about 4% to 5% in site EUI and CO2 emissions levels per unit area.

Multi-Objective Optimization for the Energy, Economic, and Environmental Performance of High-Rise Residential Buildings in Areas of Northwestern China with Different Solar Radiation

Multi-Objective Optimization for the Energy, Economic, and Environmental Performance of High-Rise Residential Buildings in Areas of Northwestern China with Different Solar Radiation

On the Role of the Building Envelope on the Urban Heat Island Mitigation and Building Energy Performance in Mediterranean Cities: A Case Study in Southern Italy

The urban heat island (UHI) effect is one of the largest climate-related issues concerning our cities due to the localized temperature increase in highly urbanized areas. This paper aims to investigate the impact of UHI mitigation techniques in promoting climate resilience, by reducing urban air temperatures and cooling energy consumption in buildings. To this end, four mitigation solutions regarding the building envelope—green roofs, green walls, cool roofs, and cool walls—were investigated for the city of Bari in Southern Italy and compared with the current baseline scenario. Hence, five scenarios were simulated—using the ENVI-met microclimate software—during three representative summer days, and the resulting microclimate changes were assessed. Based on these analyses, new climate files—one for each scenario—were generated and used as input to run energy simulations in EnergyPlus to estimate the building cooling consumption. Coupling the microclimate and the consumption outcomes, the mitigation strategies were evaluated from both an urban and building point of view. The study shows that urban characteristics, mainly geometry and materials, are crucial for the UHI phenomenon. All the applied technologies seem to be effective. However, green walls proved to be more efficient in reducing outdoor temperatures (1 °C reduction in daily temperatures), while cool walls performed better in reducing cooling energy consumption, with an overall saving of 6% compared to the current scenario.

Impact of glazing type, window-to-wall ratio, and orientation on building energy savings quality: A parametric analysis in Algerian climatic conditions

Opaque surfaces, such as walls, are well-known for their significant contributions to heat loss and energy demands in buildings. However, transparent surfaces, such as windows, are equally critical to a building's energy performance. The design of these transparent elements requires a careful balance of various factors, including window size, glazing type, and orientation, each of which plays a pivotal role in enhancing energy efficiency. This study explores the optimization of these factors during the design process, emphasizing their impact on the overall building performance.This research evaluates the potential energy savings in a building archetype representative of the Algerian building stock. Utilizing the EnergyPlus simulation tool, the study conducted 1152 simulations on a baseline model to generate a comprehensive dataset detailing the building's energy demands for heating and cooling across various climatic conditions. The findings reveal that annual energy savings for this type of housing essentially depend on its climatic zone and can range from 6.92 % for a hot semi-arid climate (Bsh) to reach a maximum of 9.75 % in a cold semi-arid climate (Bsk), a window-to-wall ratio (WWR) of 60 % typically maximizes energy efficiency, low-E glazing proved most effective in most cases, although regions needing significant solar protection favored alternative glazing types. Optimal window orientation generally trends Eastward, except in regions where southern exposure better supports solar management, highlighting the complex relationship between architectural design choices and energy efficiency.

Semantic enrichment for BIM-based building energy performance simulations using semantic textual similarity and fine-tuning multilingual LLM

To achieve the global targets of the Paris Agreement of limiting global warming, it is necessary to reduce the operational energy of buildings, which are responsible for around 30% of the global greenhouse gas emissions. Building Energy Performance Simulation (BEPS) is an established method to estimate the building’s energy demand in early design stages. Building Information Models (BIM) provides geometric and semantic information to create precise Building Energy Models (BEM) in early design stages. However, manual enrichment of missing semantic information is still a time-consuming and laborious process. Therefore, we propose a novel methodology to automatically enrich missing information to BIM using Semantic Textual Similarity (STS) and fine-tuned Large Language Models (LLM). For every IfcSpace, we match room-specific space types and constructions with missing thermal properties using the semantic most similar pairs of the BIM model and the according databases. We use three real-world case studies to fine-tune LLMs, and two case studies evaluate the whole methodology. Different fine-tuning strategies, such as using different loss functions, adding opposing word pairs or domain-specific abbreviations, significantly improve the accuracy of the matching. At the same time, however, findings show that semantic matching based on multilingual fine-tuned LLM performs worse than translated, monolingually fine-tuned LLM. Finally, BEPS results from automatically enriched BEM only slightly deviate from manually enriched BEM.

Assessment of the potential correlation between Smart Readiness Indicator and energy performance in a dataset of buildings in South Tyrol

The Energy Performance of Buildings Directive introduced the Smart Readiness Indicator (SRI), a common European system for assessing buildings’ smart readiness. While not mandatory across the European Union, some countries have initiated trial phases, and several studies already examined its application in different Member States. In this work, we present the findings of an extensive application of the SRI methodology on a sample of 59 recently constructed or recently renovated high-performance buildings in South Tyrol, Italy. Our research focused on two main objectives: (1) characterizing the level of smart readiness of state-of-the-art modern buildings in the Italian Alpine region of South Tyrol, and (2) discussing the potential correlation between the level of smart readiness and building energy performance class. Specifically, the HVAC and building systems with the most widespread adoption of advanced smart technologies were identified, as well as those configurations with traditional control solutions suggesting potential for improvement. The analysis proved that the different building categories present various levels of smart readiness, which is higher for the local office buildings and lower for the residential ones. No particular correlation was found between smart readiness and energy performance class, suggesting the need of adopting the SRI to give a more comprehensive characterization of the building performances.

Recent advances in data mining and machine learning for enhanced building energy management

Due to the recent advancements in the Internet of Things and data science techniques, a wide range of studies have investigated the use of data mining (DM) and machine learning (ML) algorithms to enhance building energy management (BEM). However, different classes of DM and ML algorithms feature different mechanisms and capabilities, resulting in their distinct roles and performance in BEM. Appropriate integration of different categories of DM and ML algorithms in BEM is essential to promote their wide application and provide guidance for new topic areas. This study presents a literature review of the use of DM and ML techniques in key areas of BEM, including building performance evaluation, energy usage prediction, and demand flexibility optimization. The review categorizes DM and ML techniques into three main categories, including supervised DM, unsupervised DM, and reinforcement learning (RL). Unsupervised techniques are primarily used for building energy performance assessment, while supervised techniques are mainly employed for building performance benchmarking and energy usage prediction. RL has been utilized for optimal building control to improve efficiency, demand flexibility, and indoor thermal comfort. The strengths, shortcomings, and integration of these methods in terms of their applications in BEM are discussed, along with some suggestions for future research in this field.

Towards harmonising energy performance certificate indicators in Europe

Achieving the European Union's ambitious goal of transforming buildings into carbon-neutral assets is crucial for combating climate change and improving energy efficiency. The harmonization of Energy Performance Certificates (EPCs) plays a vital role in this effort. It can provide a standardized measure of building energy performance, which is crucial for benchmarking and improving energy efficiency across Member States. In the context of the recast Energy Performance of Buildings Directive (EPBD) of 2024, this study examines the progress made by EU Member States in standardizing EPCs. The research aims to evaluate the implementation of EPC indicators, focusing on data quality, calculation methods, and costs. The methodological procedure followed for the study led to the development of a dedicated Cross Comparative Matrix (CCM) to assess the implementation of EPC indicators across 27 EU Member States and their reading feature allowed the declination into three scenarios for different EPC data analysis: qualitative, quantitative and burden costs. The findings reveal significant disparities, particularly in areas such as thermal comfort and smart readiness, due to the lack of a common methodology and local regulatory differences. While key indicators like energy performance class and primary energy use are widely implemented, compliance with EU standards varies. The study concludes by recommending standardized methodologies and enhanced assessor training to improve EPC quality, harmonization, and effectiveness. This research contributes to policy discussions by offering a comprehensive framework for EPC evaluation and insights into improving data quality, reliability, and accessibility. The originality of this study lies in its cross-national analysis, highlighting the need for a unified EPC scheme to promote building sustainability across the EU.