Microgrids play a crucial role in optimizing renewable energy by integrating various sources to generate and store electricity. Effective resource management within microgrids is essential for improving efficiency and reducing operational costs. This study employs bibliometric analysis to explore key trends and emerging technologies in microgrid energy management. The analysis begins with a co-occurrence network to visualize relationships between key terms and their interconnected research areas, helping to identify thematic clusters. A trend topic analysis follows, highlighting the evolution of significant themes from 2010 to 2024. A dendrogram analysis further organizes subtopics hierarchically, while a word map analysis provides an overview of dominant themes and their practical applications. Thematic map analysis categorizes keywords into distinct clusters, offering a comprehensive understanding of technology trends and connections in the field. Key findings highlight four clusters: energy management systems, smart power grid optimization, battery management systems, and electric frequency control. These clusters underscore critical research gaps and innovative approaches in optimizing microgrid performance. By identifying future research directions, this study aims to contribute to the development of sustainable and efficient energy systems.

In power grids that integrate renewable energy sources, the virtual synchronous machine (VSM) or synchronverter offers a viable solution to the challenge posed by reduced inertia. This technology employs inverters to transfer power to the electrical network that relies on a control algorithm emulating the dynamic behavior of synchronous machines. Over the past decade, it has been applied in various contexts, leveraging its control structure based on the fundamental equations of synchronous machines. Although several review articles have been published on control strategies for grid-forming inverters, they often lack a specific focus on recent developments related to the synchronverter. Therefore, this paper aims to fill that gap by presenting a detailed review on high-quality research databases to retrieve recent documents published in recent years. These documents were classified according to journals, conferences, and books. A keyword bibliographic analysis was performed to identify the attractive topics related to the synchronverter control strategy. The paper reviews recent improvements in and applications of the synchronverter, identifying emerging trends and new potential use cases to provide a workflow guide for readers and researchers, as the documents are presented in comprehensive tables, streamlining the process of locating specific references. In addition, some advantages and disadvantages of synchronverters are reported.

This study explores the relationship between the volumetric fraction of jute fiber and the mechanical properties of epoxy matrix composites for motorcycle fairings, highlighting the feasibility of jute fibers due to their strength and sustainability. Through a methodology that includes meticulous fiber cleaning, precise mold fabrication, and rigorous evaluation of tensile and impact properties, the study provides an innovative perspective in composite materials engineering. The results demonstrate that composites with jute fibers are viable for manufacturing fairings with excellent thermal and mechanical performance, opening new avenues for motorcycle manufacturing that prioritize both performance and environmental responsibility.

This study presents a deep learning-based framework for forecasting energy demand in a quicklime production company, aiming to enhance operational efficiency and enable data-driven decision-making for industrial scalability. Using one year of real electricity consumption data, the methodology integrates temporal and operational variables—such as load profile, active power, shift indicators, and production-related proxies—to capture the dynamics of energy usage throughout the manufacturing process. Several neural network architectures, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Conv1D models, were trained and compared to predict short-term power demand with 10-min resolution. Among these, the GRU model achieved the highest predictive accuracy, with a best performance of RMSE = 2.18 kW, MAE = 0.49 kW, and SMAPE = 3.64% on the test set. The resulting forecasts support cost-efficient scheduling under time-of-use tariffs and provide valuable insights for infrastructure planning, capacity management, and sustainability optimization in energy-intensive industries.

En este artículo se describe el procedimiento que permite diseñar un controlador para un convertidor Sepic de 2 kilovatios con elementos no ideales que puede ser usado en aplicaciones industriales. Se obtiene el modelo conmutado del convertidor a partir de los modos de operación, con este se construye el modelo en pequeña señal que permite obtener la función de transferencia necesaria para el diseño del controlador análogo, garantizado un tiempo de establecimiento de 25 ms. Se muestran los resultados de simulación usando MATLAB®/SIMULINK® alrededor del punto de operación realizando perturbaciones a la salida del convertidor y variaciones en el voltaje DC de entrada, evaluando el desempeño del sistema en el dominio temporal. Con el controlador diseñado se satisface la especificación de diseño para las resistencias internas de los inductores hasta 0.3 Ohmnios.

While hardware selection is widely recognized as a key factor in machine learning performance, systematic and reproducible evaluation across heterogeneous and accessible platforms remains limited, particularly when jointly considering execution time, energy consumption, stability, and cost-efficiency. This work presents a unified and fully reproducible benchmarking framework for supervised learning, designed to enable controlled and comparable evaluation across diverse hardware environments. The proposed methodology enforces consistent training pipelines, fixed hyperparameter configurations, and repeated executions to ensure statistical reliability, while incorporating performance metrics such as execution time, power consumption, and energy usage, as well as performance-per-dollar. The framework is validated on a representative set of platforms, including CUDA-enabled GPUs, Apple Silicon (CPU/GPU), x86 processors, ARM-based embedded systems, and cloud-based environments, using convolutional, recurrent (RNN, LSTM, BiLSTM), and tree-based (XGBoost) models. The results reveal that hardware efficiency is strongly model-dependent. GPUs provide the highest computational performance and stability for parallel workloads, whereas Apple Silicon achieves superior energy efficiency with competitive execution times, particularly for recurrent architectures. The batch size analysis shows that performance can vary significantly depending on workload configuration, especially on CPU-based platforms, while epoch-based evaluation confirms that the measured performance reflects steady-state behavior rather than initialization overhead. In contrast, conventional CPUs and embedded systems exhibit significant scalability limitations for deep learning training, although they remain competitive for tree-based methods such as XGBoost, which demonstrates near hardware-independent predictive performance. These findings highlight the limitations of generalized hardware selection criteria and emphasize the need for model-aware and hardware-aware benchmarking. The proposed framework offers a practical and extensible foundation for reproducible, hardware-aware evaluation of machine learning systems, supporting informed decision-making in research, deployment, and cost-constrained scenarios.