Search Results (440)

High-rise building facades offer an alternative site for installing photovoltaic panels, which are traditionally placed on rooftops. The unique spatial configuration of high-rise buildings, characterized by a small footprint area relative to their height, supports the application of vertical facades for this purpose. Photovoltaic panels installed in these areas not only generate electricity but also enhance the aesthetic dimension of the urban landscape. The proposed methodology uses the EnergyPlus software to simulate the electricity generation of photovoltaic panels mounted on the walls of high-rise buildings in the city of Kragujevac, Serbia. A technical, economic, and environmental analysis was conducted for two scenarios: (1) photovoltaic panels installed on two facade areas with the highest solar potential, and (2) photovoltaic panels installed on all four available facade areas. In Scenario 1, the annual reduction in electricity consumption, annual cost savings in electricity consumption, and investment payback period range from 13 to 38%, 11 to 31%, and 8.4 to 10.6 years, respectively. In Scenario 2, these values range from 23 to 58%, 18 to 47%, and 10.9 to 12.9 years, respectively. The results indicate that southeast and southwest facades consistently achieve higher levels of electricity generation, underscoring the importance of prioritizing high-performing orientations rather than maximizing overall surface coverage. The methodology is particularly efficient for analyzing the solar potential of numerous buildings with comparable shapes, which is a characteristic commonly found in Eastern European architecture from the late 20th century. The study demonstrates the applicability of the proposed methodology as a practical and adaptable tool for assessing early-stage solar potential and providing decision support in urban energy planning. The approach addresses the identified methodological gap by offering a low-cost, flexible framework for assessing solar potential across diverse urban contexts and building typologies, while significantly simplifying the modeling process. Full article

With the growing global deployment of Fiber-to-the-Home (FTTH) networks driven by the demand for ensuring high-capacity broadband services, mobile network operators (MNOs) face challenges of excessive energy consumption (EC) of wired optical access networks (OANs). This paper presents a comprehensive review of methods aimed at improving the energy efficiency (EE) of wired access passive optical networks (PONs) and active optical networks (AONs). The most important energy management and power-saving methods for Optical Line Terminals (OLTs) and Optical Network Units (ONUs), as key OAN components, are overviewed in the paper. Special attention in the paper is further given to analyzing the impact of a constant increase in the number of subscribers and average data rate per subscriber on global instantaneous power and annual energy consumption trends of FTTH Gigabit PONs (GPONs) and FTTH point-to-point (P-t-P) networks. The analysis combines the real ONU/OLT device-level power profiles and the number of installed OLT and ONU devices with data traffic and subscriber growth projections for the period 2025–2035. A comparative EE analysis is performed for different MNO FTTH OAN architectures and technologies, point-of-presence (PoP) subscriber capacities, and GPON-to-P-t-P subscriber distribution ratios. The findings indicate that different FTTH PON and AON architectures, FTTH technologies, and PON-to-AON subscriber distributions can yield significantly different EE gains in the future. This review paper can serve as a decision-making guide for MNOs in balancing performance and sustainability goals, and as a reference for researchers, engineers, and policymakers engaged in designing next-generation wired optical access networks with minimized environmental impact. Full article

This paper introduces a new quadruped spider robot platform specializing in environmental reconnaissance and mapping. The robot measures 180 mm × 180 mm × 95 mm and weighs 385 g, including the battery, providing a compact yet capable platform for reconnaissance missions. The robot consists of an ESP32 microcontroller and eight servos that are disposed in a biomimetic layout to achieve the biological gait of an arachnid. One of the major design revolutions is in the power distribution network (PDN) of the robot, in which two DC-DC buck converters (LM2596M) are used to isolate the power domains of the computation and the mechanical subsystems, thereby enhancing reliability and the lifespan of the robot. The theoretical analysis demonstrates that this dual-domain architecture reduces computational-domain voltage fluctuations by 85.9% compared to single-converter designs, with a measured voltage stability improving from 0.87 V to 0.12 V under servo load spikes. Its proprietary Bluetooth protocol allows for both the sending and receiving of controls and environmental data with fewer than 120 ms of latency at up to 12 m of distance. The robot’s mapping system employs a novel motion-compensated probabilistic algorithm that integrates ultrasonic sensor data with IMU-based motion estimation using recursive Bayesian updates. The occupancy grid uses 5 cm × 5 cm cells with confidence tracking, where each cell’s probability is updated using recursive Bayesian inference with confidence weighting to guide data fusion. Experimental verification in different environments indicates that the mapping accuracy (92.7% to ground-truth measurements) and stable pattern of the sensor reading remain, even when measuring the complex gait transition. Long-range field tests conducted over 100 m traversals in challenging outdoor environments with slopes of up to 15° and obstacle densities of 0.3 objects/m² demonstrate sustained performance, with 89.2% mapping accuracy. The energy saving of the robot was an 86.4% operating-time improvement over the single-regulator designs. This work contributes to the championing of low-cost, high-performance robotic platforms for reconnaissance tasks, especially in search and rescue, the exploration of hazardous environments, and educational robotics. Full article

Historic buildings are often assumed to have poor energy performance, and energy optimization of the buildings is perceived as threatening their cultural values. This study tests these assumptions. First, it examines the energy performance of Danish historic apartment buildings (buildings constructed before 1950 with a high preservation value, according to the national SAVE system (Survey of Architectural Values in the Built Environment)). Second, it assesses the extent to which the energy improvements in the historic buildings conflict with their historic value. An analysis of energy performance certificates (EPC) in 13,000 Danish historic apartment buildings reveals that they perform no differently than apartment buildings with a low preservation value, with 46% of historic apartment buildings achieving an EPC rating of “C”. Nevertheless, significant potential for further energy improvements is identified. Expert interviews and three case studies indicate that typical interventions for enhancing buildings’ energy performance rarely interfere with its historic values. This is partly due to structural conditions where shoulder-by-shoulder location, high building compactness, and supply with district heating gives a beneficial foundation for a high energy performance. Potential conflicts between energy improvements and historic values exist but are often resolved through dialogue between local authorities and owners about the interventions. Full article

This paper aims to fill a critical cap in hybrid-electric propulsion (HEP) research by investigating the feasibility of its application on a ‘large-cabin’ business aircraft by 2040, for which key requirements are a long range of at least 6297 km (3400 nmi), and a cruise speed of Mach 0.85. Based upon a representative baseline ‘large-cabin’ aircraft, a time-stepping simulation for the distinct phases of an NBAA mission, consisting of takeoff, climb, cruise, landing, and a reserve segment is developed for turbofan, series, and parallel architectures. The simulation enables analysis of range, specific air range, battery weight, battery volume, and energy consumption for various degrees of hybridization and battery specific energy densities. The results find that while both series and parallel architectures are able to meet the requisite range targets, the parallel architecture is better suited as the overall drivetrain weight is lower. The parallel HEP architecture enables the aircraft to fly a maximum distance of 7082 km (3824 nmi), with a 5% energy hybridization. Over a typical 5556 km (3000 nmi) mission this equates to fuel savings of 847 kg compared to a turbofan. The HEP ‘large-cabin’ aircraft is viable provided battery technology reaches a specific energy density of at least 800 Wh/kg. Full article

Power converters play an important role in incorporating renewable energy sources into power systems. Among different converter designs, Buck and Boost converters are popular, as they use fewer components and deliver cost savings and high efficiency. However, Boost converters are known as non–minimum phase systems, imposing harder constraints for designing a robust converter. Developing an efficient controller for these topologies can be difficult since they exhibit nonlinearity and distortion in high frequency modes. The Lyapunov-based Adaptive Backstepping Control (ABSC) technology is used to regulate suitable outputs for these structures. This approach is an updated version of the technique that uses the stability Lyapunov function to produce increased stability and resistance to fluctuations in real-world circumstances. However, in real-time situations, disturbances with larger ranges such as supply voltage changes, parameter variations, and noise may have a negative impact on the operation of this strategy. To increase the controller’s flexibility under more difficult working settings, the most appropriate first gains must be established. To solve these concerns, the ABSC’s performance is optimized using the Reinforcement Learning (RL) adaptive technique. RL has several advantages, including lower susceptibility to error, more trustworthy findings obtained from data gathering from the environment, perfect model behavior within a certain context, and better frequency matching in real-time applications. Random exploration, on the other hand, can have disastrous effects and produce unexpected results in real-world situations. As a result, we choose the Deep Deterministic Policy Gradient (DDPG) approach, which uses a deterministic action function rather than a stochastic one. Its key advantages include effective handling of continuous action spaces, improved sample efficiency through off-policy learning, and faster convergence via its actor–critic architecture that balances value estimation and policy optimization. Furthermore, this technique uses the Grey Wolf Optimization (GWO) algorithm to improve the initial set of gains, resulting in more reliable outcomes and quicker dynamics. The GWO technique is notable for its disciplined and nature-inspired approach, which leads to faster decision-making and greater accuracy than other optimization methods. This method considers the system as a black box without its exact mathematical modeling, leading to lower complexity and computational burden. The effectiveness of this strategy is tested in both modeling and experimental scenarios utilizing the Hardware-In-Loop (HIL) framework, with considerable results and decreased error sensitivity. Full article

This study examines transparent concrete (TC) utilizing bibliometric analysis of articles from the Scopus database to identify its performance, knowledge gaps, limitations, and applications. TC is a new type of sustainable building material that combines optical fibers with concrete and is lighter in weight than traditional concrete. Incorporating optical fibers in concrete enables light transmission, thereby reducing the need for artificial lighting in TC structures. TC is also referred to as light-transmitting concrete due to its unique properties. By utilizing natural light resources instead of electric lighting, buildings can better harness sunlight, providing both architectural beauty and energy savings. This approach decreases reliance on non-renewable resources and ultimately conserves energy. Scholars have focused a lot of attention on the superb light transmission and decorative appeal of TC. However, its applications in the construction sector have yet to gain traction due to the time-consuming production process, high labor costs, and limited studies on its durability and mechanical properties. This article reviews the applications, production processes, types of TC, bibliometric analysis, cost analysis, and the research findings related to mechanical, thermal, energy-saving, light-transmitting, and durability properties. TC showed a substantial decrease in the building’s total energy use and maintained strength comparable to conventional concrete. It also displayed minimal water resistance, porosity, and density, making it suitable for constructing buildings and lightweight road surfaces. Additionally, it offers notable aesthetic value. The study identifies gaps in durability and standardization while highlighting significant developments in TC’s mechanical behavior, thermal and energy performance, and applications. Furthermore, it summarizes the future research paths for TC, which are likely to enhance its implementation as a promising sustainable construction material. Full article

Despite growing interest in AI-driven Heating, Ventilation, and Air Conditioning (HVAC) systems, existing approaches often rely on static control strategies or offline simulations that fail to adapt to real-time environmental changes, especially in high-risk healthcare settings. There remains a critical gap in integrating dynamic, physics-informed control with human-centric design to simultaneously address infection control, energy efficiency, and occupant comfort in hospital environments. This study presents an AI-driven ventilation system integrating BIM, adaptive control, and computational fluid dynamics (CFD) to optimize hospital environments dynamically. The framework features (1) HVAC control using real-time sensor datasets; (2) CFD-validated architectural interventions (1.8 m partitions and the pressure range at a return vent); and (3) patient flow prediction for spatial efficiency. The system reduces airborne pathogen exposure by 61.96% (159 s vs. 418 s residence time) and achieves 51.85% energy savings (0.19 m/s airflow) while maintaining thermal comfort. Key innovations include adaptive energy management, pandemic-resilient design, and human-centric spatial planning. This work establishes a scalable model for sustainable hospitals that manages infection risk, energy use, and occupant comfort. Future directions include waste heat recovery and lifecycle analysis to further enhance dynamic system performance. Full article

Building geometric parameters are key factors influencing energy-efficient building design. However, the systematic influence of building geometric parameters on energy use intensity (EUI) across varying climate regions and building envelope thermal performance levels remains incompletely elucidated, hindering the quantitative assessment of their energy-saving benefits in diverse regions and operational scenarios. This study employs a zonal sensor-optimized coupled daylighting–thermal simulation to analyze the impact of building geometric parameters and their values on annual total EUI across different climate regions and building envelope thermal performance levels. The interquartile range (IQR), sensitivity analysis (SA), and energy saving rate (ESR) analysis are utilized. The results showed the following: (1) The energy-saving benefits of geometric parameters were the greatest in severe cold (SevC) and temperate regions (TRs), with IQRs ranging from 28.50 to 39.87 kWh/m², followed by hot summer–warm winter (HS-WW), cold (Cld), and hot summer–cold winter (HS-CW) regions. While high-performance building envelopes significantly reduce EUI, the energy-saving benefits associated with geometric parameters remain undiminished. (2) The WWR is the parameter most sensitive to EUI, with SA reaching a maximum of 41.19%, notably exceeding 20% in HS-CW regions, HS-WW regions, and TRs; floor height has the lowest sensitivity, with SA reaching a maximum of 5.65%. (3) In different climate regions, the influence of floor height and building footprint area on the ESR shifts between positive and negative correlations, while the WWR and window sill height consistently exhibit positive correlations with the ESR in all climate regions. This study provides a quantitative decision-making basis for optimizing building geometric parameters in different climate regions to achieve high-performance building shapes during the early stages of architectural design. Full article

The transportation sector is witnessing a paradigm shift toward more sustainable and efficient propulsion systems, with a particular focus on public transportation vehicles such as buses. In this context, hybrid powertrains combining internal combustion engines with electric propulsion systems have emerged as prominent contenders due to their ability to offer significant fuel savings and CO₂ emission reductions compared to conventional diesel powertrains. In this study, the simulation of a complete hybrid bus vehicle is carried out to evaluate the impact of two different hybrid powertrain architectures compared to the diesel reference one. The selected vehicle is a 12 m city bus that performs typical urban driving routes represented by real measured driving cycles. First, the vehicle model was developed using a state-of-the-art diesel powertrain (internal combustion engine) and validated against literature data. This model facilitates a comprehensive evaluation of system efficiency, fuel consumption, and CO₂ emissions while incorporating the effects of driving cycle variability. Subsequently, two different hybrid configurations (parallel P1 and series) are implemented in the model and compared to predict the relative energy consumption and environmental impact, highlighting advantages and challenges. Full article

To significantly reduce fuel consumption and improve fuel economy in hybrid tractor under complex working conditions, an energy—saving strategy based on working condition prediction and Deep Deterministic Policy Gradient and Fuzzy control (DDPG-Fuzzy) was proposed. Firstly, a hybrid tractor system dynamics model containing diesel, motor, and power battery was established. Secondly, a working condition prediction model for plowing velocity and resistance was constructed based on the adaptive cubic exponential smoothing method. Finally, a two-layer control architecture was designed. The upper layer adopted the DDPG algorithm, which takes demand torque, equivalent fuel consumption, and the State of Charge (SOC) as state inputs to optimize energy consumption by generating the diesel benchmark torque through the policy network. The lower layer introduced a fuzzy control compensation mechanism that calculates the torque correction based on the plowing velocity error and the plowing resistance deviation to adjust the power allocation. In light of on this, an energy—saving strategy for hybrid tractor based on working condition prediction and DDPG-Fuzzy control was proposed. Under a standard 140 s plowing cycle, the results showed that the working condition prediction model achieved mean prediction accuracies of 97% for plowing velocity and 96.8% for plowing resistance. Under plowing conditions, the proposed strategy reduced the equivalent fuel consumption by 9.7% compared to the power-following strategy, and reduced SOC by 4.4% while maintaining it within a reasonable range. By coordinating the operation of the diesel and motor within high-efficiency regions, this approach enhances fuel economy under complex working conditions. Full article

The cold-chain supply of perishable fruits continues to face challenges such as fuel wastage, fragmented stakeholder coordination, and limited real-time adaptability. Traditional solutions, based on static routing and centralized control, fall short in addressing the dynamic, distributed, and secure demands of modern food supply chains. This study presents a novel end-to-end architecture that integrates multi-agent reinforcement learning (MARL), blockchain technology, and generative artificial intelligence. The system features large language model (LLM)-mediated negotiation for inter-enterprise coordination, Pareto-based reward optimization balancing spoilage, energy consumption, delivery time, and climate and emission impact. Smart contracts and Non-Fungible Token (NFT)-based traceability are deployed over a private Ethereum blockchain to ensure compliance, trust, and decentralized governance. Modular agents—trained using centralized training with decentralized execution (CTDE)—handle routing, temperature regulation, spoilage prediction, inventory, and delivery scheduling. Generative AI simulates demand variability and disruption scenarios to strengthen resilient infrastructure. Experiments demonstrate up to 50% reduction in spoilage, 35% energy savings, and 25% lower emissions. The system also cuts travel time by 30% and improves delivery reliability and fruit quality. This work offers a scalable, intelligent, and sustainable supply chain framework, especially suitable for resource-constrained or intermittently connected environments, laying the foundation for future-ready food logistics systems. Full article

Sustainable shell structures are thin, curved systems such as domes, vaults, and cylindrical shells that achieve strength and stability primarily through membrane action, allowing significant material savings. Their sustainability lies in minimizing embodied energy and CO₂ emissions by using less material, integrating recycled or bio-based components, and applying optimization strategies to extend service life and enable reuse or recycling, all while maintaining structural performance and architectural quality. This review critically examines the state-of-the-art in sustainable shell structures, focusing on their buckling behavior and material-efficient design strategies. Integrating bibliometric analysis with thematic synthesis, the study identifies key research trends, theoretical advancements, and optimization tools that support structural efficiency. Emphasis is placed on recent developments in composite and bio-based materials, imperfection-sensitive buckling models, and performance-based design approaches. Advanced computational methods, including finite element analysis, machine learning, and digital twins, are highlighted as critical in enhancing predictive accuracy and sustainability outcomes. The findings underscore the dual challenge of achieving both structural stability and environmental responsibility, while outlining research gaps and future directions toward resilient, low-impact shell construction. Full article

Artificial intelligence (AI) is now the computational core of smart building automation, acting across the entire cyber–physical stack. This review surveys peer-reviewed work on the integration of AI with indoor environmental quality (IEQ) and energy performance, distinguishing itself by presenting a holistic synthesis of the complete technological evolution from IoT sensors to generative AI. We uniquely frame this progression within a human-centric architecture that integrates digital twins of both the building (DT-B) and its occupants (DT-H), providing a forward-looking perspective on occupant comfort and energy management. We find that deep reinforcement learning (DRL) agents, often developed within physics-calibrated digital twins, reduce annual HVAC demand by 10–35% while maintaining an operative temperature within ±0.5 °C and CO₂ below 800 ppm. These comfort and IAQ targets are consistent with ASHRAE Standard 55 (thermal environmental conditions) and ASHRAE Standard 62.1 (ventilation for acceptable indoor air quality); keeping the operative temperature within ±0.5 °C of the setpoint and indoor CO₂ near or below ~800 ppm reflects commonly adopted control tolerances and per-person outdoor air supply objectives. Regarding energy impacts, simulation studies commonly report higher double-digit reductions, whereas real building deployments typically achieve single- to low-double-digit savings; we therefore report simulation and field results separately. Supervised learners, including gradient boosting and various neural networks, achieve 87–97% accuracy for short-term load, comfort, and fault forecasting. Furthermore, unsupervised models successfully mine large-scale telemetry for anomalies and occupancy patterns, enabling adaptive ventilation that can cut sick building complaints by 40%. Despite these gains, deployment is hindered by fragmented datasets, interoperability issues between legacy BAS and modern IoT devices, and the computer energy and privacy–security costs of large models. The key research priorities include (1) open, high-fidelity IEQ benchmarks; (2) energy-aware, on-device learning architectures; (3) privacy-preserving federated frameworks; (4) hybrid, physics-informed models to win operator trust. Addressing these challenges is pivotal for scaling AI from isolated pilots to trustworthy, human-centric building ecosystems. Full article

Given the growing need for high-performance operation of 4WID-UGVs, coordinated optimization of trajectory tracking, vehicle stability, and energy efficiency poses a challenge. Existing control strategies often fail to effectively balance these multiple objectives, particularly in integrating energy-saving goals while ensuring precise trajectory following and stable vehicle motion. Thus, a hierarchical control architecture based on Model Predictive Control (MPC) is proposed. The upper-level controller focuses on trajectory tracking accuracy and computes the optimal longitudinal acceleration and additional yaw moment using a receding horizon optimization scheme. The lower-level controller formulates a multi-objective allocation model that integrates vehicle stability, energy consumption, and wheel utilization, translating the upper-level outputs into precise steering angles and torque commands for each wheel. This work innovatively integrates multi-objective optimization more comprehensively within the intelligent vehicle context. To validate the proposed approach, simulation experiments were conducted on S-shaped and circular paths. The results show that the proposed method can keep the average lateral and longitudinal tracking errors at about 0.2 m, while keeping the average efficiency of the wheel hub motor above 85%. This study provides a feasible and effective control strategy for achieving high-performance, energy-saving autonomous driving of distributed drive vehicles. Full article