Search Results (638)

This study explores the intersection of sociocultural factors, particularly privacy, with energy consumption patterns in residential buildings in Riyadh, Saudi Arabia. While cultural values around privacy have long been recognised as influential in residential design, the impact of these values on energy consumption is underexplored. This research aims to fill this gap by examining how privacy needs, residents’ preferences, and open layouts affect energy efficiency, particularly in terms of natural light and ventilation. A mixed-methods approach was employed, including semi-structured interviews with engineers, data collected from 108 respondents via an online survey, a case study of a residential building in Riyadh, and building performance simulations using IES software. The study also assessed actual energy consumption data and indoor lighting as potential implications of privacy concerns, causing changes in behavioural control of systems (e.g., windows, blinds, lighting, etc.). It focuses on the relationship between privacy needs, energy use, and natural daylight distribution. The IES simulation results for the studied residential building show an annual energy consumption of 24,000 kWh, primarily due to cooling loads and artificial lighting caused by privacy measures applied by the residents. The findings reveal that privacy-driven design choices and occupant behaviours, such as the use of full window shutters, frosted glazing and limited window operation, significantly reduce daylight availability and natural ventilation, leading to increased reliance on artificial lighting and air conditioning. This study highlights the need for human-centric design approaches that address the interplay between sociocultural factors, particularly reinforcing cultural sensitivity, and building performance, offering insights for future sustainable housing developments in Riyadh and similar contexts. Full article

Sustainable Urban Drainage Systems (SUDSs) are essential for stormwater management in urban areas, with varying hydrological, social, ecological, and economic benefits. Nevertheless, choosing the SUDS most appropriate for public spaces poses a challenge when balancing details/specifications against community decisions, primarily social implications and perceptions. Building on the SUDS design pillar of the amenity, this study outlines a three-phase methodological framework for selecting SUDS based on social facilitation. The first phase introduces the application of the Partial Least Squares Structural Equation Modeling (PLS-SEM) and Classificatory Expectation–Maximization (CEM) techniques by modeling complex social interdependencies to find critical components related to urban planning. A Likert scale survey was also conducted with 440 urban dwellers in Tunja (Colombia), which identified three dimensions: Residential Satisfaction (RS), Resilience and Adaptation to Climate Change (RACC), and Community Participation (CP). In the second phase, the factors identified above were transformed into eight operational criteria, which were weighted using the Analytic Hierarchy Process (AHP) with the collaboration of 35 international experts in SUDS planning and implementation. In the third phase, these weighted criteria were used to evaluate and classify 13 types of SUDSs based on the experts’ assessments of their sub-criteria. The results deliver a clear message: cities must concentrate on solutions that will guarantee that water is managed to the best of their ability, not just safely, and that also enhance climate resilience, energy efficiency, and the ways in which public space is used. Among those options considered, infiltration ponds, green roofs, rain gardens, wetlands, and the like were the best-performing options, providing real and concrete uses in promoting a more resilient and sustainable urban water system. The methodology was also used in a real case in Tunja, Colombia. In its results, this approach proved not only pragmatic but also useful for all concerned, showing that the socio-cultural dimensions can be truly integrated into planning SUDSs and ensuring success. Full article

The integration of artificial intelligence into the Industrial Internet of Things (IIoT), supported by edge computing architectures, marks a new paradigm of intelligent automation. Tiny Machine Learning (TinyML) is emerging as a key technology that enables the deployment of machine learning models on ultra-low-power devices. This study presents a systematic review of 110 peer-reviewed publications (2020–2025) identified from Scopus, Web of Science, and IEEE Xplore following the PRISMA protocol. Bibliometric and thematic analyses were conducted using Biblioshiny and VOSviewer to identify major trends, architectural approaches, and industrial applications of TinyML. The results reveal four principal research clusters: edge intelligence and energy efficiency, federated and explainable learning, human-centric systems, and sustainable resource management. Importantly, the surveyed industrial implementations report measurable gains—typically reducing inference latency to the millisecond range, lowering on-device energy cost to the sub-milliwatt regime, and sustaining high task accuracy, thereby substantiating the practical feasibility of TinyML in real IIoT settings. The analysis indicates a conceptual shift from engineering- and energy-focused studies toward cognitive, ethical, and security-oriented perspectives aligned with the principles of Industry 5.0. TinyML is positioned as a catalyst for the transition from automation to cognitive autonomy and as a technological foundation for building energy-efficient, ethical, and sustainable industrial ecosystems. Full article

This research has successfully addressed the challenges attributed with SMS, including the fragmented data, heavy reliance on experience, and lack of life cycle integration. This study presents the development and validation of a novel sustainable material selection (SMS) model using Artificial Intelligence (AI). The proposed model structures the process around four core life cycle phases—design, construction, operation and maintenance, and end of life—and incorporates a dual-interface system. This includes a main credits interface for high-level tracking of 100 total credits to trace the dynamics of SMS in relation to energy efficiency, indoor air quality, site selection, and efficient use of water. Further, it includes a detailed credit interface for granular assessment of specific material properties. A key innovation is the formalization of closed-loop feedback mechanisms between phases, ensuring that practical insights from construction and operation inform earlier design choices. The model’s functionality is demonstrated through a proof of concept for SMS considering thermal properties, showcasing its ability to contextualize benchmarks by climate, map properties to building components via a weighted networking system, and rank materials using a comprehensive database sourced from the academic literature. Automated scoring aligns with green building certification tiers, with an integrated alert system flagging suboptimal performance. The proposed model was validated through a structured practitioner survey, and the collected responses were analysed using descriptive and inferential statistical analysis. The result presents a scalable quantitative AI-assisted decision-making support model for optimizing material selection across different project phases. This work paves the way for further research with additional assessment criteria and better integration of AI and Machine Learning for SMS. Full article

Industrial Control Systems (ICS) are fundamental to the operation, monitoring, and automation of critical infrastructure in sectors such as energy, water utilities, manufacturing, transportation, and oil and gas. According to the Purdue Model, ICS encompasses tightly coupled OT and IT layers, becoming increasingly interconnected. Smart grids represent a critical class of ICS; thus, this survey examines encryption and relevant protocols in smart grid communications, with findings extendable to other ICS. Encryption techniques implemented at both the protocol and network layers are among the most effective cybersecurity strategies for protecting communications in increasingly interconnected ICS environments. This paper provides a comprehensive survey of encryption practices within the smart grid as the primary ICS application domain, focusing on protocol-level solutions (e.g., DNP3, IEC 60870-5-104, IEC 61850, ICCP/TASE.2, Modbus, OPC UA, and MQTT) and network-level mechanisms (e.g., VPNs, IPsec, and MACsec). We evaluate these technologies in terms of security, performance, and deployability in legacy and heterogeneous systems that include renewable energy resources. Key implementation challenges are explored, including real-time operational constraints, cryptographic key management, interoperability across platforms, and alignment with NERC CIP, IEC 62351, and IEC 62443. The survey highlights emerging trends such as lightweight Transport Layer Security (TLS) for constrained devices, post-quantum cryptography, and Zero Trust architectures. Our goal is to provide a practical resource for building resilient smart grid security frameworks, with takeaways that generalize to other ICS. Full article

Building Information Modeling (BIM) is increasingly used to support green building design practices, yet its alignment with established green building assessment (GBA) tools remains underexamined. This study evaluates the extent to which Autodesk Revit, as a BIM tool, supports the calculation of energy-related indicators in GBA tools such as the Leadership in Energy and Environmental Design (LEED) method. A quasi-empirical, multi-method approach was employed, combining content analysis, a Revit-based simulation of a residential building, and structured evaluation by a panel of four experts. Using both subjective and objective measures, the experts assessed Revit’s effectiveness and the role of Revit’s media channels—modeling, simulation, data integration, and text documentation—in supporting and calculating LEED Energy and Atmosphere (EA) indicators. Results reveal that Revit is capable of effectively supporting 7 out of 11 LEED EA indicators. The highly supported indicators included minimum energy performance, building-level energy metering, optimized energy performance, advanced energy metering, renewable energy production, and enhanced refrigerant management while the fundamental refrigerant management indicator was evaluated as a moderately supported indicator. These highly supported indicators are core energy-related indicators; three of them are prerequisite indicators, while the remaining are credit indicators that cover 66.7% of the weight assigned for the EA indicators. The results also demonstrated that the remaining four indicators—fundamental commissioning and verification, enhanced commissioning, demand response, green power, and carbon offsets—were evaluated as poorly supported by Revit. The consistency of results across two rounds of survey, along with the expert’s consensus on 73% (8 out of 11) of the examined indicators, provides empirical validation of Rivet’s capacity to support LEED GBA. Findings also showed that modeling and simulation, followed by data integration, are the most impactful channels in supporting and calculating LEED EA criteria and requirements, with significant statistical correlation confirmed through Kendall’s Tau correlation. The findings have theoretical and practical implications for designers, green building practitioners, and BIM developers and suggest areas for further research. Full article

Museums open to the public must reconcile heritage preservation requirements with energy-conscious microclimate management and visitors’ environmental experience. In historic buildings, indoor conditions are typically controlled primarily for preventive conservation, while opportunities for detailed assessment of human comfort are often limited by existing monitoring systems and operational constraints. This study investigates visitors’ perceptions of thermal conditions and indoor air quality (IAQ) in two branches of the National Museum in Krakow (NMK) characterized by different microclimate-control strategies: the mechanically ventilated and air-conditioned Cloth Hall and the predominantly passively controlled Bishop Erazm Ciołek Palace. A pilot survey was conducted in spring 2023 to capture subjective assessments of thermal sensation and perceived IAQ. These perceptions were contextualized using long-term air temperature and relative humidity data (2013–2023) routinely monitored for conservation purposes. Environmental data were analyzed to assess the stability of indoor conditions and to provide background for interpreting survey responses, rather than to perform a normative evaluation of thermal comfort. The results indicate that visitors frequently perceived the indoor environment as slightly warm and reported lower air quality in the Palace, where air was often described as stale or stuffy. These perceptions occurred despite relatively small differences in monitored air temperature and relative humidity between the two buildings. The findings suggest that ventilation strategy, air exchange effectiveness, odor accumulation, room configuration, and lighting conditions may influence perceived environmental quality more strongly than temperature or humidity alone. Although limited in scope, this pilot study highlights the value of incorporating visitor perception into discussions of energy-conscious microclimate management in museums and indicates directions for further multidisciplinary research. Full article

Indoor air quality (IAQ) in high-rise residential buildings is an increasing concern, especially in hot and humid climates where prolonged indoor exposure elevates health risks. This study evaluates the performance of Fresh Air Handling Units (FAHUs) using two complementary approaches: (1) real-time sensor data to quantify IAQ conditions and (2) occupant survey responses to capture perceived comfort and pollution indicators. The results show that floor level did not predict satisfaction, even though AQI data revealed clear differences between flats, suggesting perceptions are driven more by sensory cues than by actual pollutant levels. Longer weekday exposure emerged as a stronger predictor of dissatisfaction. These gaps between perceived and measured IAQ highlight the need for improved ventilation scheduling and greater occupant awareness. FAHUs were found to be inefficient, consuming 21–26% of total building energy while lacking pollutant-specific monitoring capabilities. To address these issues, the study recommends the integration of IoT-enabled sensors for real-time pollutant detection, enhanced facade sealing to minimize external infiltration, and the upgrade of filtration systems with HEPA filters and UV purification. Additionally, AI-driven predictive maintenance and automated ventilation optimization through Building Management Systems (BMS) are suggested. These findings offer valuable insights for improving IAQ management in high-rise buildings, with future research focusing on AI-based predictive modeling for dynamic air quality control. Full article

Reducing energy demand and carbon emissions in residential buildings requires more than technological upgrades; it demands a nuanced understanding of occupant behaviour. Residential energy use is shaped by both physical design and human actions, yet behavioural factors remain underexplored, contributing to the energy performance gap. This study addresses this issue by developing and validating a behavioural framework grounded in the Theory of Planned Behaviour (TPB) to examine how attitudes, social norms, perceived control, and environmental awareness influence energy-related decisions. Data were collected through an online survey of 310 households in metropolitan Sydney and analysed using Stata v17 software employing principal component analysis and regression modelling. Results reveal that environmental awareness is the most significant predictor of pro-environmental intention, which strongly correlates with actual behavioural outcomes. While attitudes and perceived control were generally positive, subjective norms and awareness remained moderate, limiting behavioural change. The proposed framework demonstrates strong validity and reliability, offering a practical tool for policymakers, designers, and educators to integrate behavioural insights into sustainable building strategies. By prioritising awareness campaigns and normative interventions, stakeholders can complement technical retrofits with behavioural measures, accelerating progress towards low-carbon housing and benefiting both households and the broader community. Full article

Optimizing window insulation is crucial for reducing heat loss and energy use in industrial buildings in Northeast China’s severe cold regions. Based on six typical building prototypes identified via cluster analysis of field survey data, this study used DesignBuilder (Version 6.1.0.006) to simulate the influence of key parameters for insulation materials (type, thickness, emissivity) and installation methods (position, air cavity, operation). Simulations reveal that the energy-saving potential is inversely proportional to a building’s existing thermal performance, reaching a maximum of 10.3%. Regarding material selection, results indicate that reducing surface emissivity from 0.92 to 0.05 effectively substitutes for approximately 20 mm of physical insulation thickness. Transparent films prioritize daytime comfort, raising nighttime temperatures by 1.5 °C, whereas opaque panels excel at nighttime insulation with a 2.28 °C increase. Techno-economic analysis identifies low-emissivity foil combined with EPS or XPS as the most cost-effective strategy, achieving rapid payback periods of 0.6–3.2 years. Regarding installation, an external configuration with a 20 mm air cavity and vertical operation was identified as optimal, yielding 1.5–2.0% greater energy savings than an internal setup. This study provides tailored retrofitting strategies for industrial building windows in these regions. Full article

Buildings account for a substantial share of global energy use, with cooling and heating systems contributing significantly to this demand. Conventional fixed setpoint temperatures overlook occupants’ thermal adaptability, often resulting in unnecessary energy consumption. Although adaptive setpoint temperatures have been investigated in residential and conventional office buildings, their applicability to public buildings, where occupancy is highly variable and indoor–outdoor thermal exchange occurs frequently, remains insufficiently explored. This study examines the performance of an adaptive cooling setpoint strategy in a public building in South Korea through simulation and in situ evaluation. A calibrated simulation model was used to compare cooling energy consumption between fixed and adaptive setpoint temperatures. Simulations indicated an overall 9.0% reduction in cooling energy use, with monthly savings exceeding 11.0% during cooling-dominant months. Validation results confirmed a 7.7% daily energy reduction, while survey results verified that occupant thermal comfort was maintained. The study findings indicate that adaptive thermal comfort-based setpoint temperature control shows promise for effective application in public buildings with similar operational characteristics, improving energy efficiency without compromising occupant comfort. This approach offers a practical pathway for sustainable HVAC operation in buildings with dynamic occupancy and operation features. Full article

Over the last 40 years, the Kingdom of Saudi Arabia (KSA) has experienced economic growth that has driven urbanization and infrastructure improvements. However, this has also led to high resource use and poor planning, exacerbating climate challenges and underscoring the need for international cooperation. Given the substantial energy use associated with buildings, sustainable global building standards have been developed. Saudi Vision 2030 encourages sustainable practices in energy, housing, and water by adopting green building standards to guide environmentally friendly initiatives. This study provides an overview of the current status of green building rating systems in KSA and examines the principal obstacles faced during their implementation. Utilizing importance-performance analysis (IPA), the study identifies and evaluates strategies to advance green building ratings, drawing upon survey data from diverse stakeholders. Major barriers include low awareness across the public and private sectors and technical challenges such as a shortage of qualified professionals, limited information, and unreliable resources. The strategies proposed aim to establish clear standards for sustainable construction and promote targeted awareness campaigns with industry leaders and government, highlighting the long-term environmental and financial advantages of green buildings. Identifying these barriers and evaluating interventions will help to advance green building rating systems and sustainability in KSA and worldwide. Full article

Digitalization is becoming one of the key trends in contemporary construction, playing a particularly important role in the building operation phase. This phase represents the longest period of a building’s life cycle and is simultaneously associated with high operational costs. The aim of the presented research was to analyze the views of experts and professionals working in the field of building management and operation on the use of digital tools, their perception of the level of digitalization, and the potential for further development. The research was conducted in the form of a questionnaire survey. The results show that in most cases, basic software tools prevail, while the use of advanced platforms such as CMMS (Computerized Maintenance Management System) or CAFM (Computer-Aided Facility Management) systems remains limited. Only one quarter of respondents actively use IoT sensors, which represent an innovative element with high potential for efficient building operation and sustainability. Paradoxically, some respondents perceive even the use of basic software as representing significant digitalization. The most digitalized areas include financial administration, security systems, and energy management, while digital building passports and workspace management remain on the periphery. The findings highlight the uneven application of digital tools and the need for their broader implementation, which can significantly contribute to the efficiency and sustainability of building management. Full article

The global freight transportation industry has experienced exponential growth, significantly contributing to economic development. However, this expansion has also led to considerable environmental challenges, particularly due to the sector’s dependence on fossil fuels and inefficient logistical practices, resulting in high carbon emissions, air pollution, noise pollution, and resource depletion. The complex problems facing the freight transportation sector are directly impacting several United Nations Sustainable Development Goals (SDGs), particularly SDG 2, SDG 3, SDG 7, SDG 9, SDG 11, SDG 12, and SDG 13. This study addresses these challenges by first examining the direct contribution of sustainable freight transportation to the United Nations Sustainable Development Goals (SDGs). Building on this foundation, the paper explores the transformative potential of artificial intelligence (AI) to enhance sustainability in freight transportation. Focusing on advanced analytics, predictive modeling, and real-time optimization, AI provides opportunities to improve route planning, energy efficiency, and emission reduction, while supporting more resilient and sustainable logistics systems. The paper introduces a holistic framework, integrating AI seamlessly throughout the entire freight logistics process. To contextualize these insights, an empirical survey was conducted among Moroccan freight transportation companies, highlighting current practices, the perceived effectiveness of AI adoption, and the level of confidence in achieving long-term carbon neutrality targets. Finally, the paper introduces a practical framework for integrating AI into freight transportation systems, aligning technological innovation with sustainability goals, and offering actionable guidance for both industry stakeholders and policymakers. Full article

Achieving the decarbonization of the building stock by 2050 requires not only technological innovation but also strategies capable of addressing energy poverty, which threatens to exclude millions of households from a fair transition. Measuring this phenomenon remains challenging: at the European level, monitoring systems rely mainly on aggregated statistics, useful for territorial comparisons but often too approximate to describe the conditions of individual households and dwellings. This paper proposes a building-scale methodology that integrates socio-economic and technical data collected directly through surveys, interviews, and utility bills. The approach was applied to a private multi-family building built in the early twentieth century in Turin (Italy), involving 16 households. Results indicate that 31% of households exceed the 10% energy expenditure threshold, with heating emerging as the main cost driver. Correlation analyses suggest that single parameters such as income or dwelling size are not sufficient on their own to explain vulnerability, whereas the integration of socio-technical factors provides a more detailed picture of household conditions. Based on this evidence, four intervention strategies were developed, ranging from the insulation of the envelope to the installation of photovoltaics, conceived to be implemented progressively according to real technical and economic constraints. The novelty of this study lies in linking building-scale evidence with concrete design solutions, bridging the gap between measurement and action. Full article