The increased usage of renewable energy sources (RESs) and the intermittent nature of the power they provide lead to several issues related to stability, reliability, and power quality. In such instances, energy storage systems (ESSs) offer a promising solution to such related RES issues. Hence, several ESS techniques were proposed in the literature to solve these issues; however, a single ESS does not fulfill all the requirements for certain operations and has different tradeoffs for overall system performance. This is mainly due to the limited capability of a single ESS and the potency concerning cost, lifespan, power and energy density, and dynamic response. In order to overcome the tradeoff issue resulting from using a single ESS system, a hybrid energy storage system (HESS) consisting of two or more ESSs appears as an effective solution. Many studies have been considered lately to develop and propose different HESSs for different applications showing the great advantages of using multiple ESSs in one combined system. Although these individual methods have been well documented, a comprehensive review of HESS-integrated RE has not been fully investigated in the literature before. Thus, as a novel contribution to the literature, this study aims to review and analyze the importance and impact of HESSs in the presence of renewable energy towards sustainable development that will facilitate this newly emerging topic to researchers in this field. In this regard, the present scenario and recent trend of HESSs in RESs at the global level, including a comparison with main ESS features, are discussed and analyzed along with the concept, design, classifications, and a detailed comparison of HESSs. The emerging role of HESSs in terms of their benefits and applications has been analyzed. Recent control and optimization methods of HESSs associated with RESs and their advantages and disadvantages have been reviewed. Finally, open issues and new challenges toward more efficient, sustainable, and green energy have also been highlighted herein. All the highlighted insights of this review will hopefully lead to increased efforts toward the development of an advanced HESS for future renewable energy optimal operation.

Soiling of solar panels has a considerable impact on the performance of photo voltaic (PV) systems, emphasizing the importance of developing reliable decision support tools for solar panel cleaning. Although recent convolutional neural network (CNN)-based models, including lightweight architectures such as SolPowNet, have demonstrated high classification accuracy, their performance can be sensitive to dataset variability and domain shifts encountered in real-world PV environments. Motivated by the lightweight design philosophy of SolPowNet, this paper proposes a hybrid and ensemble-based intelligent cleaning decision framework that integrates classical image processing, machine learning, and deep learning techniques. The proposed approach combines physically interpretable handcrafted texture and sharpness features classified using a Random Forest model with a pretrained MobileNetV3-Small CNN through a conservative OR-based ensemble fusion strategy. In addition, a probability-driven Soiling Index (SI) is introduced to translate classification confidence into actionable cleaning decisions, including no cleaning, light cleaning, and full cleaning. Experimental results on multiple PV image datasets demonstrate that, under domain-shift conditions where individual models may experience performance degradation, the proposed ensemble framework achieves an accuracy of up to 85.93% and attains a dusty-panel detection rate of 0.90 on the unseen dataset. On the in-distribution evaluation, the proposed OR-ensemble achieves an average accuracy of 0.9663 ± 0.0177 with dusty recall of 0.9896 ± 0.0104 over repeated stratified runs. Importantly, the conservative fusion strategy minimizes high-risk false negative cases while avoiding excessive misclassification of clean panels. Overall, the proposed framework offers a robust, scalable, and deployment-ready solution for intelligent PV cleaning decision support, advancing CNN-based soiling detection toward practical and risk-aware operation and maintenance systems.

Predictive maintenance has increased usage and shows a significant advantage over preventive maintenance. Though, conventional approaches of predictive maintenance have noteworthy restrictions in maintenance optimization and reliability enhancement. In the previous two decades, machine learning (ML) has been successful and overwhelmed many vital defects of conventional maintenance prediction methods. In the meantime, ML exhibitions unique predictive power in maintenance prediction and optimization. In this study, a multiple classifier ML methodology for Predictive Maintenance is presented. This study compares the features of corrective, protective, and predictive maintenance studies to the conventional approaches to predictive maintenance and investigates their problems. This study yields that Neural network (NN) was the best model. Finally, the findings of this study provide future investigations concerning how to utilize ML to enhance maintenance prediction and planning, expand equipment reliability, and achieve the best possible benefit.

Newly hired workers in construction, industrialized building production, and other built-environment operations often face elevated safety and ergonomic risk while learning manual tasks. At the same time, many onboarding programs still rely on observation, verbal coaching, and checklist-based sign-off, which can be difficult to standardize across supervisors and sites. This study presents the development and field evaluation of a data-driven training system that integrates markerless motion capture, machine-learning-assisted ergonomic risk scoring, and Lean/continuous-improvement (CI) routines to provide structured coaching during onboarding. A single-site, non-randomized quasi-experimental sequential-cohort design compared a traditional onboarding cohort with a subsequent app-supported cohort (n = 20 each). Primary outcomes were time to qualification, training cost, and task accuracy. Secondary site-level indicators were safety compliance and musculoskeletal (MSK) injury outcomes. Compared with the traditional cohort, the app-supported cohort reached qualification sooner (5.85 ± 1.50 vs. 18.60 ± 3.50 calendar months), at lower cost (SR 29,250 ± 7602 vs. SR 93,000 ± 17,348 per employee), and with higher task accuracy (88.60 ± 5.70% vs. 60.65 ± 10.60%). Welch’s t-tests showed statistically significant differences across all primary outcomes (all p < 0.001), although the standardized effect sizes were very large and should be interpreted cautiously given the modest sample and non-randomized design. Safety compliance (+68%) and MSK injuries (−25%) are reported only as descriptive site-level indicators because denominator and exposure data were not available for inferential analysis. The study contributes a practical intervention model linking ergonomic sensing to coaching cues, auditable training logs, A3 problem solving, and standard work refinement. The findings suggest that this integrated approach is promising for built-environment onboarding, but multi-site studies with stronger comparative designs, individual-level reporting, and fuller algorithmic documentation are needed.

The increasing integration of renewable energy sources (RESs) and the nonlinear nature of economic load dispatch (ELD) have significantly intensified the complexity of modern power systems, demanding efficient optimization techniques. This study proposes a Fractional Marine Predators Algorithm (FMPA) to address the ELD problem under uncertain and stochastic operating conditions. The proposed approach enhances the conventional MPA by incorporating fractional calculus (FC), which introduces memory-dependent search dynamics and improves the balance between exploration and exploitation. This modification enables the algorithm to utilize historical search information, resulting in smoother convergence behavior and reduced susceptibility to premature convergence. The performance of the proposed method is assessed using multiple benchmark systems, including 3-unit, 13-unit, and 40-unit configurations with integrated wind power. The optimization framework considers realistic system constraints such as generator limits, valve-point loading effects, and stochastic wind power modeled using probabilistic functions. Simulation results demonstrate that FMPA provides competitive and near-optimal generation costs with stable convergence behavior compared to several existing metaheuristic techniques. Statistical analysis over multiple independent runs confirms the robustness, stability, and reliability of the proposed method. The results indicate that FMPA provides a scalable and efficient solution for solving complex, nonlinear, and constrained ELD problems in modern power systems.