Search Results (7,685)

This study examines energy savings and efficient use through the application of smart adaptive lighting within the framework of “smart energy”, “smart city”, and “smart village”. Adaptive street lighting systems have emerged as an effective solution for reducing energy consumption while maintaining traffic safety. However, existing studies typically evaluate energy performance under predefined traffic conditions or focus primarily on AI-based control strategies without systematically investigating the influence of object speed on energy savings. This study proposes a comprehensive methodology that combines CupCarbon traffic simulation, Shape-Preserving Cubic Hermite Interpolation (PCHIP), Monte Carlo simulation, sensitivity analysis, and machine learning to evaluate and predict the energy-saving performance of adaptive street lighting. Unlike previous approaches, the proposed framework establishes a continuous relationship between object speed and energy consumption, enabling the estimation of energy savings across the entire operating speed range while quantifying the effects of object speed, pole spacing, and pre-activation time. The machine learning models are employed to predict energy-saving results generated by the simulation framework. Six regression models were trained and validated using simulated datasets, with Gradient Boosting achieving the highest predictive accuracy. Moreover, the analysis demonstrated that adaptive street lighting scenarios operating at 50% power (50 W) under no-object conditions and 100% power (100 W) during object detection achieved energy savings of 19–37% per luminaire compared with conventional street lighting, depending on object speed. Furthermore, adaptive lighting operating at a constant 50% power (50 W) during object detection yielded substantially higher energy savings of 59–77% per luminaire, highlighting the significant influence of the lighting control strategy on overall energy efficiency. The results demonstrate that lower object speeds yield the greatest savings. The proposed methodology provides a robust and scalable framework for the design, optimization, and intelligent control of adaptive street lighting systems and offers a benchmark for evaluating the maximum theoretical energy-saving potential under controlled traffic conditions. Full article

Decarbonizing heavy-duty transport in the mining sector requires a deep understanding of the interplay between specific energy consumption, payload dynamics, and ambient thermal stressors. This study presents an integrated, physics-informed machine learning framework to compare the energy intensity of battery-electric (EV) and diesel internal combustion engine (ICE) tippers on a real quarry route in Poland. We develop a bidirectional, route-aware model using physical force balance and high-resolution elevation data to estimate net energy consumption and regenerative braking potential over a complete closed-loop cycle. Furthermore, an Artificial Intelligence analysis utilizing a Random Forest regressor is implemented to simulate and quantify the non-linear impacts of ambient temperature, haul road rolling resistance, and payload mass on the specific energy intensity ($E_{spec}$). Results indicate that while EV energy demand surges in sub-zero climates due to parasitic battery thermal management loads, electric powertrains exhibit a profound thermodynamic advantage during loaded downhill segments, acting as net energy generators via recuperation. The proposed multi-factor approach provides a robust predictive tool for optimizing fleet deployment, infrastructure positioning, and decarbonization pathways in transitionary mining environments. Full article

Kelp is a high-moisture, flexible, sheet-like marine biomass whose drying behavior is strongly affected by the coupled effects of radiative heating, convective vapor removal, internal moisture migration, tissue shrinkage, curling, and material overlap. Traditional sun drying and hot-air drying remain widely used but are limited by long processing cycles, environmental dependence, high energy consumption, and inconsistent product quality. With the development of infrared heating, heat-pump dehumidification, Internet of Things (IoT)-enabled sensing, fifth-generation (5G) mobile communication, multiphysics simulation, and digital control, kelp drying is progressively shifting toward monitored, model-assisted, and intelligent processing. This review critically summarizes recent advances in kelp and related seaweed drying, with particular emphasis on infrared-assisted heat and mass transfer, drying kinetics, coupled computational fluid dynamics–finite element method (CFD–FEM) simulation, quality evaluation, and intelligent control. Representative published studies demonstrate the engineering potential of these approaches. In a suspended infrared-array kelp drying system, an infrared power density of 1.2 kW m−2 combined with an air velocity of 3 m s−1 maintained the drying temperature at approximately 55–62 °C, while relative humidity decreased from about 80% to 20–30%. Under these conditions, the Page model achieved R² = 0.987 and RMSE = 0.019, the rehydration ratio exceeded 94%, and the total color difference remained below ΔE = 6.5. A recent CFD–FEM–MATLAB workflow further reported a composite operating-condition index of J = 0.4535, with mapped mean and maximum kelp surface temperatures of 62.23 and 63.57 °C, respectively. These quantitative results indicate that the key challenge in infrared kelp drying is not simply to increase heat input, but to coordinate radiation distribution, airflow organization, internal moisture transport, structural response, and quality preservation. Future research should therefore focus on experimentally validated heat–mass-transfer models, adaptive sensing and control, multi-objective optimization, and pilot-scale verification under realistic production conditions. Full article

Renewable Energy Communities (RECs) have gained traction in recent years as key instruments for the democratization of energy systems and the promotion of localized, sustainable energy production and consumption. The implementation of RECs has been underpinned by a variety of software programs and algorithms. This paper presents a systematic literature review of such methods following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology; moreover, following the PRISMA nomenclature, the review includes 420 reports, grouped into 311 unique studies, evaluating the literature according to REC development phases, methodological approach, software functions, openness, interoperability, and decision-support capabilities. Results show that most contributions focus on the modelling phase, specifically feasibility study and REC design, while stakeholder engagement, construction, and long-term operation remain less developed. Comprehensive software programs are mainly available for simulation and techno-economic analysis, whereas tools developed specifically for REC operation, monitoring, energy sharing, or decision support are often prototypes or case-specific implementations. These findings provide guidance for researchers, REC developers, policymakers, and community stakeholders seeking to select, improve, or regulate digital tools for RECs. Full article

This study presents an integrated computational framework combining Computational Fluid Dynamics (CFD), experimental validation, and surrogate modelling to analyse and optimise the thermal performance, thermodynamic stability, and energy efficiency of a refrigerated truck compartment. CFD simulations were conducted to investigate airflow distribution and temperature uniformity under operating temperatures ranging from 0 to 5 °C. The results showed that airflow circulation was primarily governed by the evaporator outlet, while recirculation zones enhanced air mixing but were insufficient to completely eliminate localised hotspots. Increasing the operating temperature from 0 to 5 °C resulted in a rise in the maximum compartment temperature from 6.26 to 10.01 °C. Thermodynamic stability analysis revealed that operation within the 3–5 °C range provided more stable thermal conditions due to reduced refrigeration load and improved temperature uniformity. Experimental measurements of airflow velocity and evaporator surface temperature demonstrated good agreement with CFD predictions, confirming the reliability of the numerical model. Furthermore, CFD-based optimisation reduced the electrical energy consumption of the eTRU system by 10.0%, decreasing the energy intensity from 0.117 to 0.105 kWh km⁻¹, while maintaining improved thermal stability throughout the refrigerated compartment. The surrogate model achieved excellent predictive performance with R² = 0.9576 and RMSE = 0.0386, demonstrating its suitability for rapid optimisation of refrigerated transport systems. The proposed framework offers an effective tool for improving thermal management, enhancing energy efficiency, and supporting the development of sustainable refrigerated transport systems. Full article

Comminution–classification circuits are difficult to optimize because hydraulic, granulometric, energy, and economic responses are nonlinearly coupled, while circuit simulation, equipment sizing, simulator benchmarking, and operating optimization are often treated separately. This study aimed to develop an open Python framework for steady-state simulation and five-objective optimization of a rod mill–hydrocyclone–ball mill circuit processing a gold ore. The framework integrates solid and water balances, Rosin–Rammler particle-size reconstruction, comminution and hydrocyclone models, preliminary equipment sizing, explicit feasibility constraints, and Multiobjective Particle Swarm Optimization (MOPSO). Its novelty lies in coupling complete-circuit simulation, simulator-to-simulator benchmarking against USIM PAC^®, model-based sizing, convergence diagnostics, and Pareto optimization within one transparent workflow. The benchmark produced zero or below ${10}^{-3}\%$ errors in solid balances and sizing differences of 1.07%, 8.21%, and 0.00% for the rod mill, ball mill, and hydrocyclone, respectively. Relative to the base case, the joint minimum-water, minimum-energy, and minimum-cost solution reduced specific water consumption by 24.50%, specific grinding energy by 4.24%, specific operating cost by 10.19%, and mass recirculation by 8.80%, while useful recovery decreased slightly from 82.67% to 81.78%. The maximum-recovery solution increased useful recovery to 84.45%, with higher water, energy, and operating-cost requirements. The framework supports reproducible evaluation of resource–recovery trade-offs in grinding–classification circuits. Full article

The semi-underground granary is a new type of energy-saving grain storage facility that can use shallow geothermal energy to reduce energy consumption during grain storage. However, unclear temperature fields and the lack of grain pile temperature control methods are not conducive to the design and application of semi-underground granaries. In this study, the temperature fields and temperature control methods for grain piles in a semi-underground granary were numerically investigated by using an experimentally verified COMSOL model and a collaborative simulation method combining steady-state heat transfer and dynamic heat transfer. Multiple grain storage temperature control methods for the semi-underground granary were presented to improve grain storage safety, including an intermediate floor slab, an embedded-pipe wall, floor burial depth, and envelope insulation. The results showed that there was significant spatial heterogeneity in the temperature field distribution of the grain pile in the semi-underground granary. The large thermal inertia of the soil and the stable low-temperature soil environment reduced the influence of outdoor air temperature variations on the grain pile temperature field. Installing an intermediate floor slab could achieve natural low-temperature grain storage in the underground section of the semi-underground granary. An embedded-pipe wall could effectively solve the problem of local temperature increases in grain piles caused by heat transfer through the granary walls. The floor burial depth of the semi-underground granary was a key influencing factor of heat transfer through the granary wall. Granary wall thickness had a significant impact on the thermal performance of the walls and the grain pile temperature field due to changes in wall insulation. These results can provide beneficial suggestions for guiding the design of grain storage temperature control methods in semi-underground granaries. Full article

Energy efficiency remains a major challenge in IoT-enabled wireless sensor networks because sensor nodes operate with limited battery capacity and are often deployed in environments where battery replacement is impractical. Existing clustering and routing protocols frequently optimize cluster-head selection and routing separately, leading to uneven energy consumption, premature node failure, increased routing overhead, and reduced network reliability. This paper proposes an Adaptive Multi-Agent Reinforcement Learning-Assisted Hybrid Bat-Artificial Bee Colony (MARL-BA-ABC) framework for joint cluster-head selection and routing optimization in IoT-enabled wireless sensor networks. The proposed framework combines the Bat Algorithm for local exploitation, the Artificial Bee Colony algorithm for global exploration, and Multi-Agent Reinforcement Learning for adaptive routing. Cluster-head selection and routing are jointly optimized using residual energy, communication distance, traffic load, node density, and link quality. A multi-objective optimization model is formulated to minimize energy consumption, end-to-end delay, routing overhead, and load imbalance while improving packet delivery ratio, residual energy preservation, and network lifetime. Simulation results show that the proposed MARL-BA-ABC framework outperforms Low-Energy Adaptive Clustering Hierarchy (LEACH), Hybrid Energy-Efficient Distributed Clustering (HEED), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Bat Algorithm (BA), and Artificial Bee Colony (ABC), achieving a First Node Death of 2200 rounds, residual energy of 1.32 J, packet delivery ratio of 98.1%, throughput of 410 kbps, and average end-to-end delay of 7.4 ms. Full article

As silicon scaling nears its physical limits and digital systems process ever-growing amounts of data, integrating computation directly within memory is emerging as a key strategy to overcome the constraints of conventional Von Neumann architectures. Approximate In-Memory Computation (IMC) with memristors offers a promising path toward energy-efficient processing for data-intensive applications. Recent adaptive approximate adders exploit operand magnitude to dynamically switch between exact and approximate computation, but typically ignore carry propagation across approximation boundaries, which can significantly degrade application-level robustness. This work introduces RapproX, a family of adaptive memristive approximate adders featuring a lightweight carry lookback mechanism that approximates carry interaction between exact and approximate regions. The proposed approach improves arithmetic robustness while introducing only minimal overhead and enabling resource-efficient implementations through memristor reuse. Experimental results demonstrate that the proposed approaches achieve superior arithmetic quality compared to State-of-the-Art (SoA) memristive approximate adders. More importantly, the carry lookback mechanism translates into substantial application-level benefits. In image processing, RapproX reduces energy consumption by up to 30.9% compared to the most competitive SoA design and by 50.3% compared to exact computation while maintaining roughly 43 dB Peak Signal-to-Noise Ratio (PSNR). Across a range of machine-learning workloads, including k-means, AlexNet on MNIST, and multiple CIFAR-10 models, RapproX preserves near-exact inference accuracy for the evaluated models at low-to-moderate k and maintains the energy advantages of adaptive approximation, while SoA approximations degrade markedly under the same conditions. These simulation-based results suggest that lightweight carry-aware approximation can improve the robustness of adaptive approximate in-memory computing with only marginal hardware overhead. Full article

Renewable energy communities are gaining importance as instruments for integrating decentralized renewable generation and enabling local flexibility provision. However, systematic comparisons of community performance under varying technical configurations and regulatory conditions remain scarce. This study combines a semi-structured literature review on system-level contributions of local energy systems with a quantitative techno-economic simulation of a reference urban energy community. The community—comprising eight apartment buildings equipped with rooftop photovoltaics, battery storage, electric vehicles, and heat pumps—was analyzed across 16 scenarios varying photovoltaic capacity, electric vehicle charging optimization, and energy-sharing incentives under four European regulatory frameworks (Germany, Austria, Spain, and Italy). Photovoltaic capacity emerges as the primary driver of energy autonomy, covering 16.3–30.8% of total community demand. Optimized electric vehicle charging significantly reduces grid dependency for vehicle loads (40.0–92.7%). Energy sharing contributes modestly to total consumption (0–3.4%) but substantially redistributes financial benefits among participants. Total community costs range from 23.6 k€ to 103.3 k€ across scenarios and countries, reflecting strong regulatory influence. The findings demonstrate that effective policy support requires coordinated design across sharing incentives, network tariffs, and flexibility pricing rather than isolated instrument deployment. Full article

Multi-access edge computing (MEC) enables computation-intensive perception and decision-making tasks in vehicular networks to be offloaded to nearby edge servers. Existing approaches usually fix the artificial intelligence (AI) inference model, overlooking how model selection jointly affects latency, energy consumption, and service reliability. We propose a hierarchical model selection and control (HMSC) framework based on deep reinforcement learning (DRL) for MEC-assisted vehicular networks. The framework couples a vehicle-layer MAPPO component that provides a communication interface representation for subchannel assignment and energy accounting with a centralized MEC-layer soft actor-critic (SAC) agent that, under SDN orchestration, adaptively selects lightweight or high-fidelity AI models and allocates computational resources. Accordingly, the core contribution of this paper lies in MEC-side model-aware computation control under an explicitly defined subchannel-contention abstraction, rather than in physical-layer transmit-power optimization. Both layers are guided by a composite objective that integrates normalized end-to-end (E2E) latency, normalized energy consumption, and a deadline-violation penalty. Using a discrete-time simulation framework, HMSC reduces E2E latency compared with static inference and non-hierarchical DRL baselines and sustains a higher deadline satisfaction ratio (DSR) under constrained uplink throughput and varying traffic loads. The learned policy is load-aware, favoring high-fidelity inference under light load and lightweight inference under congestion; a post hoc analysis using YOLOv5-family accuracy reference further quantifies the inference-quality implications of this adaptive selection behavior. These results show that coordinated MEC-side control of AI model selection and computation, under a shared deadline-aware objective, provides a robust latency–energy trade-off for MEC-assisted vehicular networks. Full article

The rapid growth of computing-intensive and latency-sensitive applications, including augmented reality, virtual reality, and self-driving systems, has increased the demand for low-latency and energy-efficient processing solutions. Mobile edge computing (MEC) has evolved as a transformative paradigm by relocating computation to the network edge, closer to end users. Unmanned aerial vehicles (UAVs) further strengthen MEC by offering flexible deployment, mobility, and reliable line-of-sight communication, making them suitable for temporary high-demand scenarios. Moreover, such latency-sensitive applications often generate numerous repetitive tasks and, thus, storing the results of these tasks can reduce both communication overhead and computational workload. However, jointly addressing the caching of task-results alongside offloading and resource allocation decisions in UAV-aided MEC networks remains a non-trivial challenge. In this study, an integrated task offloading and resource allocation with data caching (JORC) framework is proposed to address these challenges. The offloading and resource allocation problems are formulated as a Markov decision process and solved using the soft actor–critic reinforcement learning algorithm. In addition, dynamic and adaptive caching manages limited storage and reduces redundant computations by using a hybrid strategy that integrates the least-frequently used and least-recently used policies to reduce computational redundancy. Simulation results confirm that the proposed JORC framework substantially reduces latency, energy consumption, and overall system cost, while increasing the successful task completion ratio compared to existing baseline approaches. Full article

The integration of reversible solid oxide cell (rSOC) systems with industrial energy units can improve operational flexibility, but it also requires safe and efficient management of hydrogen-rich off-gas. This study presents the numerical design of an exhaust gas afterburner for a 10 kW rSOC stack, in which unreacted hydrogen mixed with steam is oxidized using the hot air stream employed for stack purging. A finite-volume CFD approach was applied using a non-premixed combustion model, a k–$\omega$ SST turbulence model, and GRI-Mech 3.0 chemistry, followed by a thermo-mechanical assessment of the chamber. For the reference case, the unreacted hydrogen stream was 1.16661 × 10⁻⁴ kg/s, corresponding to approximately 14 kW of chemical energy. The simulations predicted a localized reaction zone directly downstream of the burner outlet, accompanied by rapid hydrogen consumption and a fluid-temperature range of approximately 492–930 °C. The thermo-mechanical analysis predicted a maximum total deformation of 2.2189 mm and a maximum axial displacement of approximately 2.21 mm for the analyzed steady-state operating point. These results characterize the temperature, species, and deformation fields of the 10 kW reference configuration. The 100 kW and 1 MW variants should be treated only as preliminary geometric extrapolations, because they were not verified by separate CFD/CSD calculations. Full article

With the explosive growth in demand for artificial intelligence and computing power, the energy consumption of data centers has sharply increased, making their green and low-carbon operation an urgent need. Shared energy storage (SES), as a flexible regulation resource, can effectively facilitate renewable energy consumption and reduce system costs. However, existing research mostly regards data center loads as rigid loads, ignoring the elastic scheduling potential of latency-insensitive computing tasks, and lacking a game decision-making model from the perspective of SES operators to provide strategies for data center SES transactions. Therefore, this study constructs a SES trading optimization model that takes into account the flexibility of computing power. The upper-level targets profit maximization for the SES operator by optimizing charge and discharge strategies and service pricing, while the lower level minimizes the total energy cost of each computing center by jointly optimizing computing task scheduling and energy storage utilization plans. Finally, a bi-level Stackelberg game-based optimization model is constructed to solve the SES dispatch and, achieve benefit coordination between SES and computing center. The simulation results indicate that using SES without implementing load shifting cannot optimize the total cost of the computing power center, offering only storage revenue. Only by integrating SES with load shifting can a significant cost reduction be realized. The model presented in this article resulted in an SES revenue of 2605.34 yuan and a 3.14% reduction in computing center costs. This study provides a theoretical basis and decision-making support for shared energy storage participation in the computing power market and contributes to accelerating the coordinated development of computing power and electricity systems. Full article

The selection of materials for enhancing the energy performance of historic mosque structures, which form a significant part of Türkiye's cultural heritage, should be based on scientifically supported methodologies and should also consider their architectural nature. In this work, an innovative Energy3D–MADA approach is developed for the comparative evaluation of thermal performance and sustainable selection of heritage natural stones. The proposed framework concentrates on the thermal aspect, while environmental, economic, mechanical, and material-related acoustic indicators are used as supplementary decision indicators. The study examined eight representative heritage natural stones using a representative Ottoman composite masonry wall. For the four climatic regions of Türkiye, the energy performance was predicted, and 32 scenarios were obtained. The annual heating and cooling energy requirements, total operational energy, operational CO ₂ emissions, and costs of wall manufacturing were investigated through Energy3D. The results of the simulation indicated that the natural stones had a considerable impact on the performance of historic mosque buildings. Od Stone (Tuff) had the lowest heating and cooling annual energy demand, the minimum total operational energy consumption, and the fewest operational CO ₂ emissions among all the alternatives considered, while Red Granite had the greatest energy demand. Compared to Red Granite, Od Stone achieved reductions in annual heating energy of 31%, in total operational energy consumption of 22–25%, and in operational CO ₂ emissions of 22–25%, with only about a 1% increase in comparative initial construction cost. Spearman's rank correlation analysis (ρ) indicated the same ranking in all climate regions, which demonstrated the ranking consistency of the Energy3D-based analysis. Overall, the Energy3D simulation outcomes were combined with the LODECI, MEREC, SPC, SIWEC-M, and ALPAS techniques to formulate a decision-support system for the comparative analysis and prioritization of heritage natural stones, to support sustainable mosque design and heritage conservation planning. Full article