Building Energy Consumption Research Articles

AI-driven decarbonization of buildings: Leveraging predictive analytics and automation for sustainable energy management

The decarbonization of buildings is a pivotal strategy in addressing global climate change, and artificial intelligence (AI) is increasingly recognized as a powerful tool in this process. This paper explores the application of AI in optimizing building energy consumption, focusing on predictive analytics, real-time energy monitoring, and smart energy systems. By leveraging machine learning algorithms, AI enhances energy efficiency through precise automation, particularly in heating, ventilation, and air conditioning (HVAC) systems. AI-powered demand response solutions dynamically adjust energy use based on factors like occupancy, weather patterns, and energy prices, further minimizing carbon footprints. Additionally, the integration of renewable energy sources such as solar and wind with AI-driven energy management systems enables smarter utilization of clean energy. The paper examines both new construction projects and the retrofitting of existing infrastructures, offering insights into AI’s role in reducing carbon emissions and improving energy sustainability. Case studies highlight successful implementations, underscoring AI’s ability to drive significant energy savings and carbon reduction. The study also discusses challenges such as data privacy, the high cost of AI technology, and the regulatory framework required for widespread adoption. The findings present AI as an essential component of the future of sustainable, carbon-neutral buildings.

Application of a decision tree approach to predict energy consumption in lightweight buildings under subtropical climate

PurposeLightweight building systems have emerged as alternatives to reduce the high environmental impact of conventional masonry. However, in subtropical climates, the low thermal inertia of lightweight envelopes negatively affects energy performance. The purpose of this paper is to investigate the thermophysical parameters that influence heating and cooling energy consumption in lightweight residential buildings under subtropical climates and develop a model to predict these parameters using statistical and machine learning tools.Design/methodology/approachA database was created with computer simulation data on the energy performance of 2048 building conditions generated by factorial combination of 10 parameters. Sensitivity analysis was performed to identify which parameters contribute most to energy performance indicators. Subsequently, decision trees were created using a classification and regression tree (CART) algorithm to visualize parameters and improve energy performance indicators, particularly cooling energy consumption.FindingsLow thermal transmittance and ground contact are interesting strategies for low thermal capacity buildings. Furthermore, the findings showed that relying only on the most influential properties does not ensure good energy performance; rather, it is the adequate combination of envelope properties that leads to good energy efficiency. The tree developed by CART can be used as a guide to assist designers and researchers in the initial selection of building envelopes, demonstrating the impact of each choice on electrical energy consumption for indoor climate control.Originality/valueBy adopting a global approach to assess the thermal performance of lightweight buildings, this study makes a significant contribution to synthesizing the results of a complex and time-consuming methodology into a guide for optimizing envelope design decisions and directing efforts and resources toward efficient strategies.

A Review on the Effective Utilization of Organic Phase Change Materials for Energy Efficiency in Buildings

Energy efficiency is critical for achieving building sustainability because it means that fewer resources are consumed. In this context, the advancement of phase-changing materials has attracted attention with regard to the integration and management of energy efficiency in construction projects. Buildings consume 40% of the global energy output annually, accounting for one-third of the global greenhouse gas emissions. For hot weather-prone construction, PCMs should have a melting temperature of 25–50 °C. For more than 30 years, researchers worldwide have experimented with PCMs at various temperatures, but few studies have been conducted in hot or harsh environments. According to recent studies, the amount of PCMs in construction materials has been limited to 20%, and exceeding this ratio was shown to significantly affect the compressive strength of concrete specimens. In this study, various phase-changing concrete materials were investigated to reduce the thermal energy consumption of buildings. This paper aims to provide an overview of the current state-of-the-art phase change materials for constructing thermal energy storage building materials. It also includes a brief review of the most recent developments in phase change technologies and their encapsulation techniques based on thermophysical properties. Implementing PCM technology in buildings will also maintain good indoor air quality. These materials are widely used in various real-time applications to significantly enhance thermal comfort in buildings.

Artificial Neural Network to Predict Curvature Light Shelf Design Related Daylighting Optimization on Office Spaces

Energy Optimization in building design field now has been revolutionized due to AI and machine learning applications. Leveraging daylight to reduce artificial lighting consumption holds promise for significant energy savings, yet the nonlinear nature of daylight patterns poses challenges in prediction and optimization. This study proposes a novel approach to automated light shelf design using machine learning algorithms, specifically artificial neural networks (ANNs) such as recurrent neural networks (RNN) by long short term memory (LSTM), to accelerate daylighting simulation and optimization processes. The methodology employed two distinct approaches: Firstly, we employed the theoretical-analytical approach to explore methods for utilizing machine learning in natural lighting and light shelf parameters. Second, the practical and applied method involved creating a predictive model for designing the light shelf using appropriate AI and ML techniques. This model is based on an office geometry model at the El Arish weather file in Egypt, four-dimensional training room models with three internal zones-oriented south. Rhinoceros and Grasshopper, two parametric simulation tools, are used to normalize and optimize light shelf parameters like geometry cross-section, curvature surface, width, height position, depth, tilt angle, and reflectance materials. Then, the Galapagos plug-in and Colibri2 are used for dataset creation and optimization. The results demonstrate that automated light shelf operation has a significant impact on internal daylighting quality. RNNs enable rapid prediction of optimization models, reducing time consumption in the early design phase. ML facilitates decision-making by generating evaluative criteria from user-selected design options. RNNs were classified as good and bad and used LSTM to enhance prediction accuracy for efficient illuminance values metric in zones 1 and 2. Challenges include increasing the simulation procedure's efficiency. The results of model accuracy reached 99%. Hence, future research should prioritize resolving the previously identified concerns. In conclusion, this study underscores the importance of ML-driven approaches in early design phases to optimize building energy consumption and pave the way for sustainable architectural practices.

Digital Twins for Reducing Energy Consumption in Buildings: A Review

This research investigates the use of digital twin (DT) technology to improve building energy management and analyse occupant behaviour. DTs perform the function of acting as virtual replicas of physical assets, which facilitates real-time monitoring, predictive maintenance, and data-driven decision-making. Consequently, energy performance and occupant comfort can be enhanced. This study evaluates the efficiency of DTs in optimising energy usage by a mix of a systematic literature review and scientometric analysis of 466 articles from the Scopus database. Among the main obstacles noted are interoperability issues, privacy and data quality difficulties, and the requirement for a more thorough integration of digital and physical interactions. The results highlight the necessity of standardised frameworks to direct DT implementations and suggest areas for further study, especially in the areas of improving cybersecurity and incorporating occupant behaviour into DT models. This research makes practical recommendations for using DT technology to increase energy efficiency and sustainability in the built environment.

The Regulation of Temperature Fluctuations and Energy Consumption in Buildings Using Phase Change Material–Gypsum Boards in Summer

This study examined the thermal performance of Comfortboard23, a commercial gypsum board from Knauf infused with phase change material (PCM). Structural characterization using XRD and SEM confirmed the presence of microencapsulated PCM within the gypsum matrix. The study does not provide a direct comparison between Comfortboard23 and other PCM-integrated gypsum boards on the market. This is a limitation of the research. A comprehensive comparison would involve testing multiple products under identical conditions, and evaluating factors such as thermal performance, cost-effectiveness, durability, and ease of installation. Thermal characterization involved a novel low-scale thermal chamber to measure U-value, thermal conductivity, heat storage capacity, and dynamic thermal response. Results showed incorporating PCM decreased the U-value by 2% compared to standard gypsum boards. Additionally, PCM inclusion increased heat storage capacity by around 45% and improved dynamic thermal characteristics by decreasing thermal stability coefficient from 0.92 to 0.76 and increasing thermal lag from 0.27 to 0.49 h. The 45% increase in heat storage capacity of Comfortboard23 could lead to a 10–20% reduction in heating and cooling energy consumption, improved thermal comfort, and potential HVAC downsizing. Exact benefits depend on climate, building design, and occupancy patterns, necessitating further research in diverse real-world settings. The findings demonstrate Comfortboard23’s potential for enhancing thermal energy storage in buildings, contributing to energy savings, improved thermal comfort, and reduced temperature fluctuations across varying daily temperatures. Overall, the study highlights the promise of Comfortboard23 as an energy-efficient PCM-integrated building material.

An Integrated Urban Building Energy Benchmarking Workflow to Support Urban Energy Evaluation: A Case Study of Sheffield UK

Understanding energy demand and supply flow at a large urban scale is an essential step for urban designers, planners and policymakers in investigating how buildings within an existing urban context could be designed as a whole to support the future sustainable built environment. The contemporary approach is to model energy use activities at various building and urban scales. This, albeit a practical approach, poses significant challenges in acquiring good quality data concerning buildings and their interactions at an urban scale at an affordable price. This paper presents a streamlined benchmarking methodology with a parametric modelling workflow to complement the mainstream urban building energy modelling (UBEM) approach. The proposed building energy benchmarking workflow integrates multiple databases concerning building energy consumption, energy generation and underlying grid infrastructure. Parametric modelling serves as a tool for integrating databases through the underlying sortable geometric characteristics. This is envisaged to afford stockholders, such as policymakers or urban planners, greater flexibility to investigate energy demand and supply scenarios at an urban neighbourhood scale and further explore potential applications. Using the proposed workflow, we look at renewable solar energy to experiment with offsetting urban building energy consumption through reconfiguring existing electricity microgrids in the Sheffield city centre. The result of this study demonstrates how the presented urban building energy benchmarking (UBEB) workflow would afford capabilities and flexibility to support stakeholders, e.g., urban planners, policymakers, and end-users, to better understand existing barriers and explore actionable opportunities via re-configurable electricity microgrids.

Analysis of airtightness performance of residential areas in Mediterranean climate: Balıkesir houses

Airtightness is one of the crucial physical characteristics of building envelope that affect the building thermal performance and energy consumption. The airtightness of the building is influenced by the quality of workmanship and various design parameters such as window/wall ratio, type of joinery, size of the usage area, wall material, and thermal insulation on the external wall. In this study aiming to determine the airtightness performance of buildings in the Mediterranean climate of Balıkesir houses, airtightness conditions of 43 different houses were determined by blower door test measurement. The air exchange rate (n50) values of 43 houses/residences were obtained between 1.94 and 49.02 h−1 and compared with the current various standards. The effect level of the design parameters on the air tightness of the building envelope was obtained as a result of the analysis performed with analysis of variance and post hoc methods. According to the analysis, joinery type was the most effective design parameter in terms of airtightness, followed by usage area and transparency of facades. The result will help in deciding on the design parameters affecting the airtightness of the building envelope for energy efficiency in the Mediterranean climate.

Innovative passive techniques and PCM integration in building envelopes for enhanced energy efficiency in desert regions

Rising energy demands in buildings for heating, cooling and operating appliances underscore the importance of implementing energy-efficient solutions. Passive cooling techniques provide a sustainable alternative to conventional, energy-intensive cooling systems, promising reduced energy consumption in buildings. Using a dynamic heat transfer model validated with ANSYS Fluent, this study examined three configurations: integrating phase change materials at the brick's centerline with cavities filled with air, EPS insulation, and emissivity coating. Additionally, the benefits of combining all three passive methods were evaluated. Results indicated that among the evaluated PCMs, capric acid demonstrated superior performance. While each passive method studied offers distinct advantages in enhancing thermal performance and achieving energy efficiency, assessing their combined effects revealed significant insights. Specifically, integrating capric acid at the brick's centerline, while simultaneously configuring EPS insulation in the upper cavities and emissivity coating in the lower cavities, yielded superior performance. This configuration significantly reduced inner surface temperature by approximately 6.6 °C and minimized heat flux by about 58.4 % compared to traditional bricks. Achieving energy savings of 56.24 %, this configuration shows potential to enhance both thermal comfort and energy efficiency in building applications.

Assessing the Influence of Occupancy Factors on Energy Performance in US Small Office Buildings

Office buildings are responsible for about 35% of the total electricity in the US and over 70% of building energy consumption occurs during occupancy periods. Therefore, understanding occupancy behavior is crucial for reducing building energy consumption. However, given the stochastic nature of occupant behavior, identifying which occupancy parameters have the most impact on energy consumption poses a considerable challenge. This study aims to investigate and quantify the impact of various occupancy parameters on the energy performance of a US small-sized office building using an EnergyPlus-based nationwide energy simulation. First, dynamic occupancy schedules are created based on different occupancy parameters using an agent-based model. Next, the generated dynamic occupancy schedules are integrated into a small office building model from the Department of Energy’s prototypes. This creates a dataset of occupancy parameters and building energy performance across various climate zones. Finally, various feature selection and statistical analysis methods are applied to the generated dataset. This helps identify significant occupancy parameters and quantify their impact on building energy performance across different climate zones. According to the results of the study, buildings located in cool marine, mixed marine, and warm marine climate zones had lower total energy consumption compared to other zones. Additionally, feature selection methods identified “Occupant Density” as the primary significant variable impacting energy consumption, across all climate zones. These findings offer valuable insights into the influential occupancy parameters across various climate zones, highlighting the importance of tailoring occupancy schedules to enhance energy efficiency. They provide practical guidance that can be directly applied to optimize energy consumption and achieve significant energy savings in small office settings with different weather conditions.

Exploratory Analysis of a Novel Modular Green Wall’s Impact on Indoor Temperature and Energy Consumption in Residential Buildings: A Case Study from Belgium

One possible solution that mitigates the effects of climate change is the implementation of vertical greenery systems, which have the potential to reduce the need for cooling and provide energy savings for heating. This paper evaluates the effects of an innovative modular green wall on indoor temperature and energy use in a residential case study building. This research was carried out on a residential house in the city of Ghent, Belgium, whose southwest facade is covered with a specific type of modular green wall (a structure with a specific substrate and plants that have the ability to purify water so that it can be reused in the house). The monitoring process included four different temperatures (in front of and behind the green wall, in the substrate, and on the wall without greenery) during winter and summer periods. To analyze the effect on the internal temperature and energy use, a DesignBuilder simulation model was built and validated against these experimental results. This green wall has proven to have the greatest effect during the hottest summer days by reducing the indoor temperature by up to 3.5 °C. It also effectively increases the indoor temperature by up to 1.4 °C on a cold winter day, leading to energy savings of 6% on an annual basis.

Bridging to Commercialization: Record-Breaking of Ultra-Large and Superior Cyclic Stability Tungsten Oxide Electrochromic Smart Window.

Electrochromic smart windows (ESWs) can significantly reduce energy consumption in buildings, but their cost-effective, large-scale production remains a challenge. In this study, the instability of black phosphorus is leveraged to induce the growth of the tungsten oxide film through its decomposition process, inspired by the 2D material-assisted in situ growth (TAIG) method. This approach results in the preparation of large-scale, high-performance WO3-x·nH2O (n<2) films. Characterization techniques and DFT calculations confirm efficient regulation ofstructural water and oxygen vacancies during TAIG preparation. The WO3-x·nH2O films exhibit excellent electrochromic (EC) properties, including high transmittance modulation (74.2%@1100nm), fast switching time (tc=5.5s, tb=3.8s), high coloration efficiency (124.7cm2C-1), and superior cyclic stability (transmittance modulation retained 94.7% after 20000 cycles). Ultra-largeWO3-x·nH2O film are prepared via a simple immersion process, and fabricated into a large-area ESW under facile laboratory conditions, demonstrating the economic and practical feasibility of this approach in industrial-scale production. Operated by the intelligent control circuit, the ESWexhibits remarkable EC properties and cyclic stability This research represents a milestone in improving the performance and industrial-scale production of ESWs, bridging the gap to the commercialization of EC technology.

Studying of Mechanical Behavior of Waste Materials Modified Cement Mortar

Large-scale environmental problems have been triggered by ceramic tiles from demolished buildings and animal bone waste. Therefore, optimizing their potential for reuse and recycling is an important goal of sustainable solid waste management. The primary objective of this study is to evaluate the feasibility of partial replacement of the fine aggregates in cement mortar with a combination of natural animal bone powder and industrial waste materials which is wall tiles ceramic powder. This study measures thermal and mechanical characteristics. Recycling construction waste is one of many ways to deny waste material from entering landfills and create new eco-friendly material that reduces energy consumption in buildings. Modified mortar cement specimens with 5% up to 25% mixtures were combined using a 1:3 ratio and a 0.5 W/C ratio to see how the ratios would affect the material’s properties. The addition of a superplasticizer boosted the cement mortar’s workability and compressive strength. The compressive strength, flexural strength, density, and thermal conductivity were evaluated for each mortar mixture ratio. Results showed that compared to regular cement mortar, cement mortar combined with 20% bone and ceramic powder wastes reached the optimal replacement percentage. Compressive strength increased by 54% and flexural strength by about 67%. At the 28-day mark, thermal insulation was lowered by 25%.It is concluded from this study that it is possible to use bone waste as a natural material with ceramic waste as an industrial material to modify the insulation and mechanical properties of cement mortar.

Simulation-based design: minimizing energy consumption in residential buildings through optimal thermal insulation

Purpose This study aims to optimize the energy consumption of residential buildings in mild and humid climates. It investigates the use of thermal insulation to reduce thermal load through energy simulation analysis. Design/methodology/approach A residential building located in Rasht city, Iran (a mild and humid climate zone), is simulated using DesignBuilder software. Subsequently, the minimum thermal resistance for external walls and roof is analyzed along with its impact on building energy consumption and carbon emissions. Findings The simulation results indicated a 26.5% reduction in heat loss through the walls and a 14.2% reduction through the roof due to optimal thermal insulation. Furthermore, optimal insulation led to a 19.2% reduction in cooling system energy use, a 12% reduction in heating system energy use and a combined 15.3% reduction in total energy consumption for cooling and heating. Originality/value This optimization process leads to several benefits: reduced costs associated with thermal and cooling energy losses in buildings, improved building performance against atmospheric factors and, ultimately, a reduction in energy consumption across the building industry. This research can be valuable to various stakeholders, including the construction industry and building sector, municipalities and engineering systems, building owners and contractors and environmental organizations. By implementing these findings, they can improve the state of modern building insulation and achieve greater energy efficiency.

A new validated TRNSYS module for phase change material-filled multi-glazed windows

Incorporating phase change materials (PCM) into multi-glazed windows (MW) has been recognized as an effective way to regulate the indoor thermal environment and reduce the energy consumption of buildings. However, the design and calculation of PCM-filled multi-glazed windows (MW + PCM) remain challenging due to the lack of general transient simulation methods and integrated tools. To address this issue, the present study developed a dynamic coupled thermal and optical model for MW + PCM based on the enthalpy-temperature and interface energy balance methods. Then, the model was implemented in C++ and integrated into the TRNSYS software as a new module (Type 2602). In parallel, a test platform comprising two test chambers was established to evaluate the effect of PCM on the thermal and optical properties of the window and to validate the newly proposed module. It was found that compared to the traditional triple-glazed window (TW), the PCM-filled triple-glazed window (TW + PCM) effectively reduces the irradiance through the window by 34.17 % and the maximum temperature of the inner surface by approximately 5.56 °C. While the increase in external surface radiation flux density from 600 to 1200 W/m2 has a diminishing effect on the heat flux variation rate during the heat dissipation process for TW + PCM, it effectively shortens the latent heat absorption time by 0.50 h and increases the temperature difference between the inner and outer surfaces by 3.02 °C. The new module demonstrates satisfactory accuracy through experimental validation and simulation comparisons, with the maximum heat flux coefficient of variance of the root mean square error (CV(RMSE)) less than 6.35 % and 11.25 % under TW and TW + PCM configurations.

An interactive clustering method via metric learning for analyzing energy consumption patterns

Accurate analysis of energy consumption patterns is the key to achieving the goal of sustainable building energy efficiency, and clustering analysis in data mining techniques has shown efficacy in this regard. However, traditional clustering methods suffer from several issues including lack of users’ participation and low quality of results. To address these challenges, this paper proposes an interactive clustering method based on metric learning. It allows users to interact with clustering models by manipulating initial clusters and inject their domain knowledge for quality improvement. Meanwhile, a metric learning technique is applied to learn a distance metric based on the initial clustering results to improve the quality and flexibility of clustering. Accordingly, a latent graph that imitates the proximity of consumers can be constructed to propagate the clustering intent of users to larger unseen data. In collaboration with five experts in the field of building energy consumption, case studies demonstrate that our method can effectively recognize four types of daily consumption patterns. Compared with the classical k-means method, the mean variance of data features, the Davies-Bouldin Index (DBI) has decreased by 11.19 % and 21.54 %, respectively, and the Calinski-Harabasz (CH) has improved by 10.72 %.

Multi-pronged strategies for enhancing building envelopes toward nearly-zero energy in hot climatic regions

Abstract Growing concerns about climate change and rising energy demands necessitate advancements in building energy efficiency. This study investigates the effectiveness of radiative coatings and thermal insulation, both individually and combined, in reducing energy consumption and carbon footprint for buildings in hot and humid climates. This research contributes to a growing body of knowledge by comprehensively evaluating the combined effects of these strategies. A comparative analysis was conducted using data on energy usage and carbon emissions.&amp;#xD;The research highlights the effectiveness of envelope-enhancing techniques in reducing energy consumption and carbon emissions. The application of radiative coating led to a significant 13.1% decrease in energy usage, totaling 681.95 MWh, and corresponding emissions of 482.14 tons of CO2. Radiative coating offers the most cost-effective solution with an LCOS of $0.045/kWh. When integrating thermal insulation with radiative coating, there was a substantial 12.0% reduction in energy consumption, amounting to 690.39 MWh, and emissions of 488.11 tons of CO2. The integrated model provides significant energy savings at a slightly higher LCOS of $0.052/kWh, making it a balanced choice between efficiency and cost-effectiveness compared to using thermal insulation alone. Moreover, the study emphasizes that the combination of Glazing Integrated Photovoltaic (GIPV) with radiative coating can lead to the creation of nearly zero-energy buildings, resulting in a significant energy savings of 34.9%. These results underscore the efficacy of these technologies in achieving significant energy savings and environmental benefits.&amp;#xD;This study demonstrates that radiative coatings significantly reduce energy consumption and carbon footprints. The combined method with thermal insulation reduces energy, suggesting further optimization strategies in hot and humid conditions. The results of this investigation recommend utilizing Glazing Integrated Photovoltaic (GIPV) to achieve nearly zero-energy buildings. Such integrated solutions not only improve energy efficiency but also make a substantial contribution to environmental sustainability in the building sector.&amp;#xD;

A Janus Spectrally Selective Glazing Toward All-Season Energy-Efficient Windows.

Windows offer the most promising avenue for mitigating energy consumption and reducing greenhouse gas emissions. However, the balance between comfortable natural lighting and all-season energy savings is often neglected in extensive explorations of energy-efficient windows. Herein, a Janus glazing is proposed that enables the switch of passive radiative cooling and heating under the precondition of conveying sufficient natural light. Measurement results indicate that the Janus window maintains a visible transmittance of 0.47, while possesses a near-infrared (NIR) reflectivity/absorptivity of 0.75/0.71 and a mid-infrared (MIR) emissivity of 0.94/0.13 for the cooling and heating modes, respectively. As demonstrated by the outdoor test, the Janus window realizes a reduction of 7.1°C for room cooling and an increase of 0.4°C for room heating compared with commercial low-e window, potentially conserving 13%-53% of the total building energy consumption across China. Meanwhile, attributed to the photothermal effect, the Janus window can elevate the surface temperature by 6.1°C compared with the low-e window, which can effectively reduce fogging occurrences on the window surface for ensuring sunlight entrance in the cold-weather conditions. This strategy offers novel prospects for enhancing energy efficiency in diverse applications, including architectural windows, greenhouse cultivation, photovoltaic generation, etc.

Twisted tapes in solar chimney-earth air heat exchangers for equipment downsizing and passive cooling

Harnessing solar and geothermal energy in hot and arid climates is an effective strategy to reduce building energy consumption. This research investigates integrating Twisted Tapes (TT) into Earth Air Heat Exchanger (EAHE) pipes within a Solar Chimney (SC)-EAHE system, aiming to reduce EAHE pipe length while meeting ventilation requirements and decreasing cooling demand. Two key innovations are introduced: first, this study pioneers the integration of TT into a SC-EAHE system; second, it comprehensively explores TT geometry optimization, analyzing the effects of width ratio and rotation rate across 77 configurations. Numerical simulations are conducted in ANSYS Fluent using the k-ε RNG turbulence model to investigate thermal performance and airflow characteristics. Findings reveal that TT integration results in a 153 % increase in Nusselt number. Additionally, it is shown that a 1 m SC diameter achieves ventilation of 4.8 Air Changes per Hour (ACH) using TT with a width ratio and rotation rate of 0.5, while reducing EAHE pipe length by 2.6 %. Increasing the SC diameter to 1.25 m, with a TT width ratio of 0.6 and rotation rate of 1.25, leads to a 13 % (35 m) reduction in EAHE pipe length while meeting a minimum outlet temperature of 290.15 K and ACH requirements. However, the benefit diminishes beyond a 1.25 m SC diameter. This study contributes to understanding of integrating TT into SC-EAHE systems, addressing the critical need for sustainable energy solutions and offering a novel and energy-efficient approach for passive cooling in extreme climates.

Prioritizing sustainability indicators for Uganda's built environment: Expert perspectives using the Delphi technique

The growing emphasis on energy efficiency within the built environment, driven by global concerns over climate change, has resulted in notable shifts in building energy consumption patterns. Achieving reductions in building energy use and CO₂ emissions through sustainable strategies necessitates the development of country-specific policies. While numerous energy assessment tools are available internationally, they often lack contextual relevance for Uganda and many Sub-Saharan African countries.Therefore, there is a pressing need for a tool tailored to Uganda's distinct climatic conditions. This study aimed to identify key building sustainability indicators to promote energy efficiency in Ugandan buildings. Utilizing the Delphi Technique, experts from academia, construction, energy consulting, and government agencies evaluated 35 building indicators over two assessment rounds. The consensus among experts was validated using the Interrater Agreement, with a significant Kendall's W value exceeding 0.6, rising from 0.655 in the first round to 0.704 in the second. Construction consultants showed the highest W value (0.77) in the second round, followed by energy consultants (0.74), while academicians had the lowest (0.7). The Chi-square test demonstrated strong agreement in ranking indicators across 34 degrees of freedom. The most significant indicators identified include "B20 - Optimization of energy use," "B8 - Advanced design and construction technology," and "B27 - Natural ventilation design," among others. The Delphi Technique was instrumental in achieving expert consensus, while interrater agreement was used to validate and ensure the reliability of the findings. The developed framework provides critical insights for policymakers, practitioners, and researchers seeking to advance energy efficiency and sustainability within Uganda's built environment.