Search Results (27,473)

Accurate measurements of global horizontal irradiance (GHI) are fundamental for solar energy assessment. However, the cost and deployment constraints of standard pyranometers limit their widespread use. This work presents a low-cost pseudo-pyranometer based on photovoltaic current measurements combined with a hybrid physical-machine learning approach. A custom data acquisition system was developed and deployed in Piatra-Neamț, Romania, consisting of a Raspberry Pi 5, INA219 current sensor, and a 0.3 W photovoltaic panel mounted horizontally. One-minute resolution measurements were collected between August 2024 and June 2025 and augmented with modeled solar geometry and clear-sky irradiance using pvlib. Temporal effects were encoded using sinusoidal representations of the time of the day and the day of the year. Clear-sky current samples were identified using a tolerance-based normalization with respect to modeled clear-sky irradiance and used to train an artificial neural network to estimate the clear-sky panel current. Feature importance was assessed using SHAP analysis, highlighting the dominant role of solar geometry and temporal encoding. The resulting clear-sky current model was combined with measured current through a clearness index formulation to estimate GHI. To evaluate performance, the system was redeployed in parallel with a reference pyranometer in Cluj-Napoca, Romania, enabling direct comparison under real operating conditions. The results demonstrate that the proposed hybrid approach can approximate pyranometer measurements with low-cost hardware, supporting scalable and redeployable solar monitoring networks in geographically localized regions. Full article

Wireless sensor network (WSN) coverage optimization is a challenging high-dimensional and nonlinear problem that directly affects network performance, including sensing quality, energy efficiency, and system reliability. Although metaheuristic algorithms have been widely applied to this problem, many existing methods still suffer from premature convergence, insufficient population diversity, and an imbalance between exploration and exploitation. To address these issues, this paper proposes a multi-strategy enhanced enterprise development optimization algorithm (MEEDOA) inspired by business management mechanisms. The proposed method integrates a hybrid population initialization strategy, an adaptive activity switching mechanism based on performance feedback, a multi-elite collaborative learning strategy, and a Lévy flight-based stagnation escape mechanism. These strategies are tightly coupled within a unified adaptive framework to improve global search capability, convergence speed, and robustness. Furthermore, a mathematical model for WSN deployment is constructed based on a binary sensing model and discrete coverage evaluation. From the perspective of symmetry, the sensing regions of sensor nodes exhibit significant geometric symmetry in both two-dimensional and three-dimensional deployment spaces. In the two-dimensional case, the sensing and communication regions are modeled as concentric circular structures, while in the three-dimensional case, the sensing regions are represented by isotropic spheres with symmetric spatial distributions. Such symmetry properties provide an effective basis for describing coverage behavior, reducing redundant overlap, and improving the uniformity of node deployment. Meanwhile, the proposed MEEDOA preserves population diversity and enhances search balance, enabling the algorithm to better capture symmetric coverage patterns and more effectively explore complex spatial deployment configurations. Extensive experiments on CEC2014, CEC2017, CEC2020, and CEC2022 benchmark functions demonstrate that MEEDOA achieves superior convergence accuracy, faster convergence speed, and stronger robustness compared with several state-of-the-art algorithms. Additional simulation results in WSN deployment applications verify its effectiveness in improving coverage performance under both symmetric and irregular spatial deployment scenarios. The results indicate that the proposed MEEDOA provides a reliable and efficient solution for complex global optimization problems and practical engineering applications. Full article

Driven by the global energy transition and carbon neutrality goals, hybrid power systems have become a core technical path for energy conservation and carbon reduction in the transportation and power sectors, and the performance of energy management strategies directly determines the system’s overall energy efficiency. Traditional energy management methods have inherent bottlenecks of high model dependence and poor adaptability, making it difficult to satisfy real-time decision-making requirements under complex operating conditions. Deep Reinforcement Learning (DRL) provides an innovative solution to this technical bottleneck, and has become a cutting-edge research direction in this field. However, existing reviews have not yet constructed a full-chain analysis framework covering its algorithms, applications, verification, challenges and prospects. Focusing on the engineering application of DRL in the real-time energy management of hybrid power systems, this paper systematically sorts out domestic and international research results up to the first quarter of 2026. The core quantitative findings of this review are as follows: (1) DRL-based strategies can achieve 93–99.5% of the Dynamic Programming (DP) theoretical global optimum in fuel economy, which is 5–25% higher than rule-based methods; (2) DRL strategies only have 3.1–4.8% performance degradation under unseen operating conditions, which is significantly better than the 10.3–14.7% degradation of the Equivalent Consumption Minimization Strategy (ECMS); (3) Actor–Critic (AC) algorithms (Twin Delayed Deep Deterministic Policy Gradient (TD3)/Soft Actor–Critic (SAC)) have become the mainstream in this field, with a 3–5 times higher sample efficiency than value function-based algorithms; and (4) offline DRL and transfer learning can reduce the training time of DRL strategies by more than 80% while maintaining equivalent optimization performance. This paper first analyzes the essential attributes and core technical challenges of hybrid power system energy management; second, classifies DRL algorithms from the perspective of control engineering and analyzes their technical characteristics; third, disassembles the application design logic of DRL around four major scenarios: land vehicles, water vessels, aerial vehicles and fixed microgrids; fourth, summarizes the mainstream verification platforms and evaluation systems; fifth, analyzes core bottlenecks and cutting-edge solutions; and finally, prospects the development trends of next-generation intelligent energy management systems combined with cross-fusion technologies. This paper aims to build a complete technical system map for this field and promote the engineering deployment and practical application of intelligent energy management technologies integrating data and knowledge. Full article

Forecasting hydropower plant power production is a great challenge in the context of maintaining power system stability, reliability and efficiency, especially in an age with variable renewable energy sources when demand for electricity is steadily rising. Accurate forecasting methods are a crucial enabler for the operational existence of power systems that rely on renewable sources. And while in the pursuit of increased accuracy of predictions, many recent research works rely on artificial intelligence and machine learning techniques, this study proposes and adopts a more conventional approach with standardized mathematical models to address the problem of hydropower production forecasting. The model predicts the runoff–power relationship. It starts with the normalization of different rain phenomena as a part of the statistical characterization of runoff events. The system transforms rain occurrence to runoff events via the USDA SCS CN model and then feature vectors are composed, which are used to generate kernel coefficients via interpolation. Contrary to models based on artificial intelligence, the proposed approach has several practical advantages requiring a minimal set of input parameters, which significantly reduces data preprocessing demands and allows for a straightforward integration into existing systems, thereby lowering the cost and the implementation and deployment time. Furthermore, the simplicity and universality of the model make it so that it can be adapted across a wide range of hydropower plants of varying scales and with diverse hydrological and meteorological conditions. The model’s performance and prediction accuracy are evaluated using empirical data records of time series over a five-year period for the meteorological parameters and production of an existing real-life hydropower plant in Bulgaria. The performance of the newly proposed model is assessed using widely accepted statistical error metrics, namely, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), the Nash–Sutcliffe Efficiency (NSE) coefficient, and the Pearson correlation coefficient (R). These metrics provide a comprehensive assessment of the forecasts’ precision and effectiveness. The results show that the proposed model offers admissible accuracy with low computational effort. Thus, it can be successfully implemented in practice in a number of hydropower plant production forecasting applications. Full article

Global water pollution driven by industrial and agricultural expansion has resulted in the widespread occurrence of persistent contaminants, particularly pharmaceuticals and heavy metals, in groundwater systems. Conventional treatment methods often prove inefficient, costly, and environmentally unsustainable, highlighting the need for innovative in situ remediation technologies. Permeable Reactive Barriers (PRBs) have emerged as a promising and energy-efficient solution for the long-term purification of contaminated aquifers. Their efficiency arises from passive operation, relying on natural groundwater flow to promote pollutant removal through adsorption, ion exchange, precipitation, and redox-driven transformations. This review emphasizes the superior performance of materials such as Activated Carbon, Biochar, Zeolites, and Zero-Valent Iron (ZVI) in the immobilization and reduction in pharmaceuticals and metal ions. Key challenges to PRB longevity include permeability loss and reactive media depletion due to mineral precipitation and biofouling. Advances in hybrid PRB configurations, coupled with electrokinetic (EK) and bioreactor systems, and predictive modeling, particularly Artificial Neural Networks (ANNs), offer pathways to enhance performance, optimize design, and ensure sustainable operation. Overall, PRBs represent a scalable and environmentally sound approach to groundwater remediation, with future progress relying on the development of multifunctional, regenerable materials and integrated design strategies. Full article

Hybrid polymeric woven composites (HPWCs) are increasingly important in automotive, aerospace, and renewable energy structures where low weight, impact tolerance, damage containment, and superior mechanical properties are required. By combining dissimilar fibres within woven architectures, HPWCs can achieve a more favourable balance of stiffness, strength, and energy absorption than single-fibre woven systems; however, experimental evidence and predictive modelling remain insufficiently integrated, particularly under dynamic and post-impact loading. This systematically searched critical review provides an HPWC-focused synthesis that links architecture-driven mechanical behaviour, damage development, and multiscale numerical simulation within a single framework. The effects of reinforcement architecture, fibre pairing, and matrix selection on tensile, flexural, compressive, interlaminar, strain rate-dependent, and impact responses are examined, with particular emphasis on barely visible impact damage and post-impact residual strength. Macroscale, mesoscale, and microscale finite element strategies are critically compared in terms of predictive fidelity, computational cost, and suitability for design-orientated assessment. The main contribution of this review lies in integrating experimental characterisation with modelling limitations, validation requirements, and industrial relevance, thereby clarifying where current approaches are effective and where critical gaps remain. Practical implications for lightweight structural design, impact-resistant components, and future validation-driven research are highlighted. Full article

In response to the increasing operational complexity of inland waterway systems, this study develops a multi-objective optimization framework for ship lock scheduling under energy-consumption and resource constraints. The model evaluates five operational dimensions, namely average waiting time, lock utilization, total energy consumption, arrival rescheduling rate, and berth-overcapacity penalty. Based on historical lockage records from the Da Teng Gorge Ship Lock Hub, four representative multi-objective algorithms—NSGA-II, NSGA-III, MOEA/D, and SPEA-II—are comparatively examined. The revised analysis emphasizes trade-off performance rather than unsupported absolute dominance claims: NSGA-III shows the most balanced overall behavior on the preserved empirical instance, MOEA/D remains competitive in time-sensitive scenarios, and SPEA-II performs well in some overcapacity-control settings. To improve methodological transparency, the paper clarifies the physical meaning and source of major parameters, distinguishes measured quantities from scenario settings, and reports carbon impact as a derived indicator linked to energy consumption. These revisions provide a more transparent and practically interpretable basis for intelligent ship lock scheduling under congestion, energy, and resource constraints. Full article

Heating, ventilation, and air conditioning (HVAC) systems struggle to maintain optimal thermal comfort because perception is subjective and varies significantly across individuals. Traditional uniform cooling strategies often overlook demographic diversity, leading to inequitable comfort outcomes and inefficient building operations. To address this limitation, this study analyzed a web-based survey of 366 university occupants using a partial proportional odds model with multiple imputation and inverse-frequency weighting. Interaction terms, specifically Age–Activity, Gender–Clothing, and Age–Clothing, were included to assess combined effects that reflect demographic disparities in adaptive capacity. The results show that clothing insulation, activity, age, gender, race/ethnicity, and space type significantly influence thermal responses. Notably, male occupants were more than three times as likely to report feeling too warm (odds ratio [OR] = 3.24), whereas older adults exhibited significantly lower odds of reporting feeling too warm (OR = 0.42). Substantial variation was observed across racial and ethnic groups (ORs ranging from 2.4 to 6.5). These findings highlight the limitations of traditional population-average comfort approaches and provide valuable scientific insights for demand-response-ready HVAC strategies that adjust temperature setpoints dynamically without sacrificing comfort. By offering accurate, real-time estimates across diverse thermal ranges, these occupant-centric models reduce HVAC energy use and associated emissions at the building scale while supporting ancillary services for flexible load shifting and smarter coordination within low-carbon electric grids. Ultimately, incorporating demographic and contextual diversity into building controls reduces unnecessary cooling waste while promoting thermal equity, establishing a human-centric foundation for sustainable built environments. Full article

In persistent patrol and online task discovery in environments with obstacles, unmanned aerial vehicle (UAV) swarms are constrained by limited battery capacity and frequent recharging disrupts patrol continuity. In comparison, unmanned ground vehicle (UGV) fleets have higher endurance and payload capacity and can serve as mobile charging platforms while executing ground-service tasks. In such collaborative scenarios, UAVs patrol along a coverage path and discover tasks online, whereas UGVs execute discovered ground tasks and provide mobile charging support. To cope with rendezvous uncertainty due to obstacle-induced detours and inefficient usage of UGV time during charging, this study proposes an energy-constrained UAV-UGV coordination framework based on adaptive anticipatory rendezvous and time-window scheduling. In particular, the adaptive anticipatory rendezvous module handles anticipatory rendezvous planning, while the time-window scheduling module models the post-rendezvous charging stage as a schedulable time window for opportunistic ground-task insertion. Simulations demonstrate that the proposed framework consistently reduces system energy consumption, completion time, and the number of emergency landings compared with three representative baselines. Moreover, a UAV-UGV prototype with AprilTag-based visual landing and post-landing mechanical correction is developed to validate the engineering feasibility of the key closed-loop process. Full article

Wind energy is a cornerstone of the global transition to renewable and sustainable energy systems. However, the same meteorological processes that generate this clean energy can also produce extreme wind events that threaten the structural integrity and operational reliability of wind turbines and power grids. Therefore, accurately predicting extreme wind speeds is a critical link between promoting clean energy and ensuring infrastructure resilience. Traditional models often struggle to capture the multimodal characteristics of extreme wind speeds under complex meteorological conditions due to fixed distribution assumptions or unstable training of mixture models, leading to estimation biases that undermine planning reliability and may result in catastrophic turbine failures or overly conservative designs. To address these issues—particularly weight imbalance and overfitting–this study proposes an enhanced regularized extreme value mixture model (ERDC-EVMM). This method integrates elastic network regularization and Kullback–Leibler divergence constraints within a Mixture of Experts framework, and employs K-means initialization and momentum-based training to enhance convergence stability. Validated using daily extreme wind speed sequences from coastal and inland wind farms, the model outperforms standard GEV and mixture models in terms of goodness-of-fit, percentile accuracy, and return period estimates, while achieving a convergence speed that is more than 30% faster (82 iterations). By balancing accuracy and training stability, the ERDC-EVMM model provides a reliable statistical tool for extreme wind speed forecasting, supporting the safe expansion of wind energy infrastructure and the design of climate-resilient communities. Full article

In this work, we analyze the arithmetic building blocks of DNA basecalling to motivate runtime approximate computing in bio systems-on-chip (BioSoCs). We propose and characterize a reconfigurable compressor-tree multiplier whose operating mode can be selected at runtime to trade energy for controlled arithmetic error. Using a 45 nm CMOS evaluation flow, the proposed design demonstrates a clear power–accuracy trade-off across 64 operating modes, achieving about a 58–61% reduction in multiplier power (per multiply under fixed V/f) relative to an accurate Wallace baseline, with mean relative error distance (MRED) in the 1.05–2.88% range. At the application level, we outline a first-order noise-propagation model and, consistent with prior approximate-inference studies, note that task-level quality loss is often within a few percent (up to 5%), motivating end-to-end basecalling evaluation. Application-level evaluation on a TinyX3 DNA basecaller—a compact Bonito-based model—shows that the proposed multiplier with measured REV = 0.012 and MRED = 1.98% preserves near-baseline performance, with negligible degradation in sequence identity and relative length at low perturbation levels and only gradual accuracy decline (confirming ≤ 5% accuracy drop) emerging as perturbations increase into the moderate regime. Finally, a processor-level case study using convolution microbenchmarks (kernel footprints 9–49 weights per output) shows an 11% improvement in energy per instruction and a 12% reduction in energy per MAC when integrating the proposed multiplier into an embedded RISC-V execution engine. Full article

Varistors are critical overvoltage protection components in modern power electronic systems. They effectively absorb and dissipate surge energy to ensure the safe and stable operation of electrical equipment. However, surface defects can lead to substandard performance or even trigger equipment failure, compromising overall system stability. Therefore, high-precision surface defect detection is essential for quality assurance. To address these challenges, we propose a lightweight model termed Defect-Aware and Edge-Reconstruction Enhanced YOLO (DAER-YOLO) for efficient varistor inspection. First, we construct a C3k2-based defect-aware enhancement module (C3k2-iEMA). This module tackles the difficulty of extracting features from small or morphologically complex defects. By integrating multi-scale feature extraction, an attention mechanism, and efficient nonlinear mapping, it strengthens the perception of defect details. Second, to enhance the reconstruction capability for edge damage and small-object defects, we introduce the Efficient Up-Convolution Block (EUCB). This block improves multi-level feature fusion and generates clearer enhanced feature maps. Based on these improvements, DAER-YOLO outperforms the YOLOv11n baseline on a custom varistor dataset, with mAP@50 and mAP@50:95 increasing by 1.6% and 2.3%, respectively. Experimental results demonstrate that the model effectively improves detection accuracy while exhibiting significant potential for real-time industrial applications. Full article

Photovoltaic (PV) power generation is inherently intermittent due to unpredictable irradiance variations, posing significant challenges for grid integration. While conventional power smoothing strategies mitigate short-term fluctuations, they do not explicitly enforce the tracking of a scheduled power trajectory. This paper proposes a dispatchable PV framework that integrates a hybrid convolutional neural network-long short-term memory (CNN-LSTM) model for precise day-ahead power forecasting with a real-time supercapacitor (SC) compensation strategy. The CNN-LSTM network captures complex spatiotemporal meteorological dependencies to generate a robust day-ahead reference trajectory. Concurrently, a supercapacitor energy storage system (SC-ESS) integrated at the DC-link level via a bidirectional buck–boost converter actively balances the instantaneous mismatch between this forecast trajectory and the actual PV generation. Unlike filter-based hybrid methods, the SC-ESS is employed as a direct forecast error actuator in a closed-loop control scheme. This strategy strictly enforces real-time forecast tracking while preserving maximum power point tracking (MPPT) and DC-link voltage stability. Simulations and laboratory experiments under rapidly varying irradiance confirm that the proposed method significantly reduces power deviations from the forecast reference and improves short-term power predictability without imposing excessive stress on the SC. This forecast-aware strategy effectively enhances the dispatchability of PV systems, providing a practical solution for grid-supportive operation. Full article

Parameter estimation of photovoltaic (PV) models is crucial for the theoretical analysis and performance evaluation of PV cells and modules, with the objective of enhancing their efficiency and reliability, thereby supporting the long-term sustainability of solar energy systems. Nevertheless, the nonlinear and multimodal characteristics of PV models make the task of accurate parameter estimation challenging. This paper proposes an improved differential evolution algorithm, named opposition-based parent selection differential evolution (OBPSDE), to enhance the reliability and robustness of PV parameter estimation. The method integrates a parent-selection mechanism with an opposition-based learning strategy to exploit both solution quality and population diversity during the search process. The proposed method is evaluated using measured data from several PV cells and modules (RTC France, PVM752GaAs, PWP201, and STP6-120/36) for parameter estimation of the double-diode model (DDM). Its performance is compared with standard DE, DE variants, and four metaheuristic algorithms using statistical metrics including root mean square error (RMSE), individual absolute error (IAE), and mean absolute error (MAE). The results indicate that OBPSDE achieves stable performance, competitive computational cost, and improved convergence behavior, with RMSE values of 6.93726 × 10^–4 for RTC France, 5.89070 × 10^–5 for PVM752GaAs, 1.93772 × 10^–3 for PWP201, and 1.39519 × 10^–2 for STP6-120/36. Additionally, the improved parameter estimation accuracy may support more reliable performance prediction and analysis of PV systems, contributing to effective PV system modeling and diagnostic applications. Full article

As the global response to climate change and energy crises accelerates, the large-scale integration of heterogeneous distributed energy resources (DERs) is rapidly transforming traditional passive distribution networks into active distribution networks. However, the massive quantity and high stochasticity of these underlying devices trigger a severe “curse of dimensionality,” creating significant computational and communication bottlenecks for coordinated system dispatch. To overcome these challenges, the “clustering followed by equivalence” aggregation modeling paradigm has emerged as a critical technical pathway. This paper reviews the state-of-the-art clustering and aggregation methodologies for distribution networks with high DER penetration. The review begins by synthesizing multi-dimensional feature extraction techniques and cutting-edge clustering algorithms that establish the foundation for dimensionality reduction. It then delves into refined aggregation models tailored to heterogeneous resources, including dynamic data-driven equivalence for renewable generation, Minkowski sum-based boundary approximations for energy storage, and thermodynamic alongside Markov chain mapping methods for flexible loads. Building upon these models, the paper comprehensively discusses the practical applications of generalized aggregators, such as microgrids and virtual power plants, in feasible region error evaluation, coordinated network control, multi-agent market games, and privacy-preserving architectures. Finally, the review outlines future research trajectories, emphasizing hybrid data-model-driven architectures for real-time dispatch, distributionally robust optimization (DRO) for enhancing grid resilience and self-healing, and decentralized trading ecosystems to ensure equitable system-level surplus allocation. This review aims to provide a systematic theoretical reference for the coordinated management and aggregated trading of flexibility resources in novel power systems. Full article