Search Results (312)

Population aging is driving the need for unobtrusive, continuous monitoring solutions in residential care environments. Radio-frequency (RF)-based technologies such as Ultra-Wideband (UWB) and millimeter-wave (mmWave) radar are particularly attractive for providing detailed information on presence and movement while preserving privacy. Building on a UWB–mmWave localization system deployed in a senior living residence, this paper focuses on the data-processing methodology for extracting quantitative mobility indicators from long-term indoor monitoring data. The system combines a device-free mmWave radar setup in bedrooms and bathrooms with a tag-based UWB positioning system in common areas. For mmWave data, an adaptive short-term average/long-term average (STA/LTA) detector operating on an aggregated, normalized radar energy signal is used to classify micro- and macromovements into bedroom occupancy and non-sedentary activity episodes. For UWB data, a partially constrained Kalman filter with a nearly constant velocity dynamics model and floor-plan information yields smoothed trajectories, from which daily gait- and mobility-related metrics are derived. The approach is illustrated using one-day samples from three users as a proof of concept. The proposed methodology provides individualized indicators of bedroom occupancy, sedentary behavior, and mobility in shared spaces, supporting the feasibility of combined UWB and mmWave radar sensing for longitudinal routine analysis in real-world elderly care environments. Full article

This paper presents the development and implementation of a consumer Digital Twin (DT) for energy demand prediction under the SENDER (Sustainable Consumer Engagement and Demand Response) project, funded by HORIZON 2020. This project aims to engage consumers in the energy sector with innovative energy service applications to achieve proactive Demand Response (DR) and optimized usage of Renewable Energy Sources (RES). The proposed DT model is designed to digitally represent occupant behaviors and energy consumption patterns using Artificial Neural Networks (ANN), which enable continuous learning by processing real-time and historical data in different pilot sites and seasons. The DT development incorporates the International Energy Agency (IEA)—Energy in Buildings and Communities (EBC) Annex 66 and Drivers-Needs-Actions-Systems (DNAS) framework to standardize occupant behavior modeling. The research methodology consists of the following steps: (i) a mock-up simulation environment for three pilot sites was created, (ii) the DT was trained and calibrated using the artificial data from the previous step, and (iii) the DT model was validated with real data from the Alginet pilot site in Spain. Results showed a strong correlation between DT predictions and mock-up data, with a maximum deviation of ±2%. Finally, a set of selected Key Performance Indicators (KPIs) was defined and categorized in order to evaluate the system’s technical effectiveness. Full article

Leadership, organizational culture, and their effects on employee attitudes are key focuses in organizational behavior studies. In this research, there were two main aims: first, to explore how organizational trust influences the relationship between leadership styles and organizational culture, and second, to clarify the mediating role of organizational culture in the link between leadership styles and affective occupational commitment among academics in Istanbul, Türkiye. Participants were selected from various states and private universities using convenience sampling. A cross-sectional survey involving quantitative methods were used to examine the relationships between key variables. The data was collected from 352 respondents. Effective leaders build trust and adapt organizational culture, which fosters employee commitment to their roles. Despite the relevant theoretical framework, there is a strong and positive link between trust in organization, leadership, and organizational culture, so the hypothesized moderating role of trust in organization was rejected. In contrast, the findings confirm the mediating role of organizational culture in the connection between leadership styles and affective occupational commitment. Moreover, another significant research finding was that leadership styles are effective when an organization has a supportive and innovative culture. Most academics expressed dissatisfaction with the effectiveness of leadership and their work environment, indicating challenges in promoting a positive culture. This study clarifies the dimensions of leadership styles, organizational trust, culture, and emotional commitment, thereby contributing to the literature concerning these areas. Full article

Ensuring thermal comfort in waiting areas is essential for visitor satisfaction and well-being. In the context of nearly zero-energy buildings (nZEBs), these spaces—typically characterized by short-term occupancy, transient user behavior, and the need for rapid temperature adjustment—pose specific challenges for HVAC control in balancing comfort and energy demand. This study investigates the influence of a ceiling-mounted fan coil unit (FCU) operating in heating mode on thermal comfort conditions in an nZEB office waiting area. Measurements were conducted at multiple points within the space to assess microclimate parameters, followed by the calculation of the predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD) indices, supported by occupant feedback collected through short interviews. The results showed that although the FCU effectively increased the average temperature, its intermittent operation and localized air jets during the heating phase caused temporary discomfort near the unit. Occupant feedback confirmed sensations of discomfort due to strong air movement during FCU operation but indicated slightly higher overall dissatisfaction and smaller variability compared to model-based PPD values, reflecting the averaging effect of occupant perception over time. These findings highlight the need for optimized FCU control strategies in waiting areas, such as operating at reduced fan speeds and preheating the heat exchanger, to enhance occupant comfort. This study contributes to improving HVAC control concepts for semi-transient spaces in nZEBs. Full article

This paper presents a multiscale monitoring and management framework designed to enhance energy and indoor environmental performance in retrofitted public schools. The proposed system comprises three layers: (i) a cost-effective sensor network deployed at building, room, and device levels; (ii) a data processing layer supporting redundancy, fault detection, and consistency scoring; and (iii) a role-adaptive interface providing customized dashboards for managers, educators, and students. The framework was deployed in two Mediterranean schools undergoing photovoltaic (PV) integration and envelope rehabilitation. The monitoring layer captures key parameters including temperature, humidity, CO₂, PM2.5, occupancy, and circuit-level energy use, enabling multiscale analysis of demand-side behavior and local PV utilization. Data from a full academic year demonstrate a reduction in lighting energy use of up to 22%, classroom-level savings of 10–15%, and an increase in PV self-consumption from 60% to 75%. These improvements were achieved without compromising indoor comfort, as validated by stable environmental conditions aligned with recognized thresholds. The synchronized collection of energy and environmental data allows transparent evaluation of behavioral engagement, operating patterns, and system effectiveness. This research shows that cost-effective, role-adaptive monitoring platforms can support resilience and decarbonization goals in public-sector buildings, particularly where commercial building management systems are financially or technically unfeasible. Full article

The indoor environment of buildings is of fundamental importance for the health of people and other living organisms residing in them. From this perspective, key factors include indoor temperature, relative humidity and the concentration of CO₂ or other pollutants. These healthy indoor conditions are typically maintained through functional heating and ventilation systems. However, in the case of indoor humidity, increasing moisture levels when they are low can be relatively challenging. There are more energy-efficient solutions that can be combined with ventilation systems. These include, for example, placing plants and green walls in the interior, which have a significant impact not only on microclimatic and acoustic conditions of the interior, but also on the overall psychological well-being of occupants. Green elements contribute to the effective regulation of CO₂ and certain other harmful substances within the indoor environment. Another possible solution involves the use of sorption-active materials in the form of cladding panels—elements capable of functioning as indoor regulators, i.e., absorbing moisture and releasing it back into the indoor environment when necessary. This study investigates the moisture behavior of natural composites based on montmorillonite clay and straw fibers, as well as their possible integration with green elements to create healthy indoor conditions for their inhabitants. The developed clay composite can be classified as water and steam absorption class WSIII according to DIN 18948—the moisture buffering capacity value was 152.73 g/m² after 12 h. Based on the research results, it can be stated that these composites could serve as interior cladding elements in synergy with green elements (Chlorophytum comosum, Epipremnum aureum), ideally regulating the indoor microclimatic conditions, especially as an effective solution for short-term humidity changes. The maximum difference in relative humidity between the reference testing chamber (without green elements and clay plates) and the chamber containing plant Chlorophytum comosum and three clay composite plates was 23.04%. Full article

Urban morphology, climate, and occupant behavior significantly affect urban building energy consumption. This study analyzed 200 example blocks with 4754 buildings in Harbin, China, a representative city with a severe cold climate, to calculate urban morphology and climate factors. A questionnaire was conducted to quantify the data on the energy use behaviors of building occupants. Linear and nonlinear methods were used to explore correlations between these three types of factors and energy consumption. An agent-based modeling (ABM) approach was applied to establish a city-scale energy consumption simulation model, and simulations of energy-saving scenarios were carried out to derive optimization strategies. Key findings include: (1) the living area is the most significant determinant of daily energy use intensity (EUI), contributing 24.42%; (2) the floor area ratio (FAR) most influences annual electricity EUI (30.55%), while building height (BH) has the largest impact on heating EUI (32.62%); and (3) altering urban morphology and climatic factors by one unit can, respectively, reduce energy consumption by up to 13.0 and 224.7 kWh/m² annually. Increasing energy-saving awareness campaigns can reduce household EUI by 30.6127 kWh/m². This study provides strategic recommendations for urban energy-saving planning in cold regions. Full article

Occupant modeling has emerged as a critical component in human-centric building design and operation, offering detailed insights into energy performance, comfort optimization, and behavior-driven control strategies. This study systematically examines occupant modeling (OM) in building design through a review of 312 articles, highlighting critical gaps between theoretical frameworks and real-world applications. Key dimensions of occupant modeling, including methodological classification, data frameworks, application scenarios and model selection strategies, are examined. The interpretability, advantages and disadvantages of 5 modeling methods are demonstrated, and the tools, algorithms and applications are analyzed. In addition, common input, output and application scenarios are sorted out and the data streams are presented. Results have shown that hybrid models represent breakthroughs but require validation beyond idealized scenarios. Meanwhile, with 88.7% of output derived from simulated results, risking self-reinforcing biases despite empirical inputs. Standardized protocols for model validation and hybrid modeling frameworks are urgently needed. To support model selection, a decision-oriented framework is proposed, integrating modeling goals, data characteristics, behavioral complexity, and platform interoperability. Future priorities include merging high explanatory methods with powerful predictive methods, advancing BIM-IoT symbiosis for adaptive digital twin, expanding to interdisciplinary projects, and establishing ethical data governance to align technical advancements with equitable, occupant-centric design. Full article

Conventional approaches to building energy retrofit decision-making struggle to generalize across diverse building characteristics, climate conditions, and occupant behaviors, and often lack interpretability. Generative AI, particularly Large Language Models (LLMs), offers a promising solution because they learn from extensive, heterogeneous data and can articulate inferences in transparent natural language. However, their capabilities in retrofit decision-making remain underexplored. This study evaluates six widely used LLMs on two objectives: determining the retrofit measure that maximizes CO₂ reduction (a technical task) and minimizes the payback period (a sociotechnical task). We assessed performance across accuracy, consistency, sensitivity, and reasoning. The evaluation used 400 residential buildings from a nationwide, simulation-based database. The results reveal that LLMs vary across cases, with consistently strong technical-task performance but notably weaker performance on the sociotechnical one, highlighting limitations in handling complex economic and contextual trade-offs. The models consistently identify a near-optimal solution for the technical task (Top-5 accuracy reaching 92.8%), although their ability to pinpoint the single best option is limited (Top-1 accuracy reaching 54.5%). While models approximate engineering logic by prioritizing location and geometry, their reasoning processes are oversimplified. These findings suggest LLMs are promising for technical advisory tools but not yet reliable for standalone retrofit decision-making. Full article

Multi-story residential buildings present distinct challenges for demand-side management due to shared infrastructure, diverse occupant behaviors, and complex load profiles. Although demand-side management strategies are well established in industrial sectors, their application in high-density residential communities remains limited. This study proposes a cost-optimized energy management framework for urban multi-story apartment buildings, integrating rooftop solar photovoltaic (PV) generation, shared battery energy storage, and flexible electric vehicle (EV) charging. A Mixed-Integer Linear Programming (MILP) model is developed to simulate 24 h energy operations across nine architecturally identical apartments equipped with the same set of smart appliances but exhibiting varied usage patterns to reflect occupant diversity. A Mixed-Integer Linear Programming (MILP) model is developed to simulate 24 h energy operations across nine architecturally identical apartments equipped with the same set of smart appliances but exhibiting varied usage patterns to reflect occupant diversity. EVs are modeled as flexible common loads under strata ownership, alongside shared facilities such as hot water systems and pool pumps. The optimization framework ensures equitable access to battery storage and prioritizes energy allocation from the most cost-effective source solar, battery, or grid on an hourly basis. Two seasonal scenarios, representing summer (February) and spring (September), are evaluated using location-specific irradiance data from Joondalup, Western Australia. The results demonstrate that flexible EV charging enhances solar utilization, mitigates peak grid demand, and supports fairness in shared energy usage. In the high-solar summer scenario, the total building energy cost was reduced to AUD 29.95/day, while in the spring scenario with lower solar availability, the cost remained moderate at AUD 31.92/day. At the apartment level, energy bills were reduced by approximately 34–38% compared to a grid-only baseline. Additionally, the system achieved solar export revenues of up to AUD 4.19/day. These findings underscore the techno-economic effectiveness of the proposed optimization framework in enabling cost-efficient, low-carbon, and grid-friendly energy management in multi-residential urban settings. Full article

Buildings in Indonesia’s tropical climate face significant barriers to energy efficiency due to high cooling loads and electricity intensity. Previous studies have primarily addressed technical optimization or policy frameworks, but few have provided an integrated and data-driven evaluation model for tropical conditions. This study develops an Internet of Things (IoT) and EDGE-based hybrid framework to support the transition toward Net-Zero-Energy Buildings (NZEBs) while maintaining occupant comfort. The research combines real-time IoT monitoring at the LLDIKTI Region XIII Office Building in Banda Aceh with simulation-based assessment using Excellence in Design for Greater Efficiencies (EDGE). Baseline energy performance was established from architectural data, historical electricity use, and live monitoring of HVAC systems, lighting, temperature, humidity, and CO₂ concentration. Intervention scenarios—including building envelope enhancement, lighting optimization, and adaptive HVAC control—were simulated and validated against empirical data. Results demonstrate that integrating IoT-driven control with passive design measures achieves up to 31.49% reduction in energy use intensity, along with 24.7% improvement in water efficiency and 22.3% material resource savings. These findings enhance indoor environmental quality and enable adaptive responses to user behavior. The study concludes that the proposed IoT–EDGE framework offers a replicable and context-sensitive pathway for achieving net-zero energy operations in tropical office buildings, with quantifiable environmental benefits that support sustainable public facility management in Indonesia. Full article

Windows play a crucial role in maintaining indoor thermal comfort, influenced by occupant behavior, passive design strategies, and advanced technologies that contribute to sustainable building practices. Despite advancements in adaptive and occupant-centric design, critical gaps remain unresolved in understanding of multi-climate adaptability, the complex interrelation between window operation and occupant behavior, and the integration of occupant roles into energy-related strategies under emerging technologies. This scoping review synthesizes peer-reviewed studies to assess the importance of window design (geometry, glazing, shading), operational strategies (manual control to AI-driven systems), and technological approaches (passive to smart systems) on thermal comfort, energy performance, and occupant behavior. Using bibliometric and scientometric analyses, the review focuses on four primary research clusters: thermal comfort and occupant behavior, window operation strategies, their impact on energy performance, and sustainability, with an emphasis on emerging trends. The findings highlight that glazing technologies, shading systems, and operational choices have a significant impact on both comfort and energy efficiency. The study develops a framework linking thermal comfort to window operation, occupant behavior, and climate context while conceptualizing a comprehensive design matrix and outlining future research directions aligned with the Sustainable Development Goals (SDG 3: health and well-being, SDG 7: clean energy, and SDG 11: sustainable cities and communities). Full article

Various studies on the envelope renovation of existing residential buildings have quantified energy savings effects across various climate conditions and building types yet have also reported discrepancies between predicted and actual energy savings performance. Given that identical technical improvements can yield substantially different actual outcomes depending on occupants’ behavioral adaptation patterns, renovation effect evaluation requires a multifaceted approach incorporating occupant behavioral changes. This case study empirically analyzed the effects of envelope renovation on occupants’ actual heating operation patterns. Envelope renovation effects applied to a 30-year-old apartment were analyzed by subdividing temperature conditions, with comparative evaluation using a non-renovated adjacent unit within the same building as a reference. While recognizing the inherent limitations of single-case analysis, this study presents a novel methodological framework for capturing subtle behavioral shifts through high-resolution temperature-specific analysis. Change-point models utilizing utility billing data were employed to analyze threshold temperature changes, and daily heating water-consumption estimation algorithms were applied to track heating pattern changes according to outdoor temperature variations. Results showed heating energy reduction despite more severe climate conditions post-renovation, with particularly pronounced savings under mild conditions. The upper limit of temperature ranges showing high heating dependency shifted downward from pre-renovation levels, improving to levels lower than the reference unit’s upper limit, demonstrating envelope performance enhancement effects. These results provide quantitative evidence that envelope improvements directly influence occupants’ heating decision-making criteria, though broader validation across multiple cases would strengthen these findings. This study quantifies envelope renovation effects not only in terms of energy savings, but also from the perspectives of occupant behavioral changes and comparison with reference units, presenting a novel evaluation methodology for effective energy efficiency improvements in aging buildings. Full article

In Northeast China’s severe cold regions, increasingly airtight rural dwellings face a critical challenge: traditional biomass-fueled heating and cooking generate severe indoor particulate matter (PM) pollution, creating a sharp trade-off between maintaining thermal comfort and ensuring safe indoor air quality through ventilation. While multi-objective optimization is widely applied to urban buildings, its use to develop practical, behavior-based ventilation strategies for resource-constrained rural dwellings in this context represents a significant research gap. This study integrates field measurements of occupant behavior and environmental parameters from 192 households with a coupled thermal-PM_2.5 predictive model. The NSGA-II genetic algorithm was employed to perform a multi-objective optimization, balancing PM reduction against thermal comfort. The optimization reveals that short, high-intensity ventilation bursts are allowed. A typical optimized event can reduce post-cooking PM_2.5 concentrations to near-guideline levels while maintaining the indoor temperature within the residents’ adaptive comfort zone. This research provides the first evidence-based, region-specific natural ventilation guidelines for these dwellings. The findings offer a practical, no-cost strategy to mitigate health risks from indoor air pollution without significant energy penalties, providing a theoretical basis for future smart ventilation system design. Full article

This research investigates the integration of green cores as central biophilic elements in residential architecture, proposing a climate-responsive design methodology grounded in architectural optimization. The study begins with the full-scale refurbishment of a compact urban apartment, wherein interior partitions, fenestration and material systems were reconfigured to embed vegetated zones within the architectural core. Light exposure, ventilation potential and spatial coherence were maximized through data-driven design strategies and structural modifications. Integrated planting modules equipped with PAR-specific LED systems ensure sustained vegetation growth, while embedded environmental infrastructure supports automated irrigation and continuous microclimate monitoring. This plant-centered spatial model is evaluated using quantifiable performance metrics, establishing a replicable framework for optimized indoor ecosystems. Photosynthetically active radiation (PAR)-specific LED systems and embedded environmental infrastructure were incorporated to maintain vegetation viability and enable microclimate regulation. A programmable irrigation system linked to environmental sensors allows automated resource management, ensuring efficient plant sustenance. The configuration is assessed using measurable indicators such as daylight factor, solar exposure, passive thermal behavior and similar elements. Additionally, a post-occupancy expert assessment was conducted with several architects evaluating different aspects confirming the architectural and spatial improvements achieved through the refurbishment. This study not only demonstrates a viable architectural prototype but also opens future avenues for the development of metabolically active buildings, integration with decentralized energy and water systems, and the computational optimization of living infrastructure across varying climatic zones. Full article