Search Results (6,182)

The increasing adoption of lithium-ion (Li-ion) batteries in electric vehicles (EVs) and renewable energy systems has heightened the demand for efficient Battery Thermal Management Systems (BTMS). Effective thermal regulation is critical to prevent performance degradation, extend battery lifespan, and mitigate safety risks such as thermal runaway. Liquid cooling has become the dominant strategy in commercial EVs due to its superior thermal performance over air cooling. However, optimizing liquid cooling systems remains challenging due to the trade-off between heat transfer efficiency and pressure drop. Recent studies have explored various coolant selection, nanofluid enhancements, and complex channel geometries, an ideal balance remains difficult to achieve. While advanced methods such as topology optimization offer promising performance gains, they often introduce significant modeling and manufacturing complexity. In this study, we propose a practical alternative: an interconnected straight-channel cooling plate that introduces lateral passages to disrupt the thermal boundary layer and enhance mixing. Comparative analysis shows that the design improves temperature uniformity and reduces peak battery temperature, all while maintaining a moderate pressure drop. The proposed configuration offers a scalable and effective solution for next-generation BTMS, particularly in EV applications where thermal performance and manufacturability are both critical. Full article

Wireless sensor networks (WSNs) consist of distributed nodes to monitor various physical and environmental parameters. The sensor nodes (SNs) are usually resource constrained such as power source, communication, and computation capacity. In WSN, energy consumption varies depending on the distance between sender and receiver SNs. Communication among SNs having long distance requires significantly additional energy that negatively affects network longevity. To address these issues, WSNs are deployed using multi-hop routing. Using multi-hop routing solves various problems like reduced communication and communication cost but finding an optimal cluster head (CH) and route remain an issue. An optimal CH reduces energy consumption and maintains reliable data transmission throughout the network. To improve the performance of multi-hop routing in WSN, we propose a model that combines Multi-Objective Particle Swarm Optimization (MOPSO) and a Decision Tree for dynamic CH selection. The proposed model consists of two phases, namely, the offline phase and the online phase. In the offline phase, various network scenarios with node densities, initial energy levels, and BS positions are simulated, required features are collected, and MOPSO is applied to the collected features to generate a Pareto front of optimal CH nodes to optimize energy efficiency, coverage, and load balancing. Each node is labeled as selected CH or not by the MOPSO, and the labelled dataset is then used to train a Decision Tree classifier, which generates a lightweight and interpretable model for CH prediction. In the online phase, the trained model is used in the deployed network to quickly and adaptively select CHs using features of each node and classifying them as a CH or non-CH. The predicted nodes broadcast the information and manage the intra-cluster communication, data aggregation, and routing to the base station. CH selection is re-initiated based on residual energy drop below a threshold, load saturation, and coverage degradation. The simulation results demonstrate that the proposed model outperforms protocols such as LEACH, HEED, and standard PSO regarding energy efficiency and network lifetime, making it highly suitable for applications in green computing, environmental monitoring, precision agriculture, healthcare, and industrial IoT. Full article

This Special Issue, “Symmetry/Asymmetry Studies in Modern Power Systems,” presents a curated collection of research addressing the critical and evolving role of symmetry in the context of energy transition. The contributions, selected through a rigorous review process, collectively advance the understanding and management of power system balance, stability, and resilience amidst the increasing integration of renewables and power electronics. The published papers offer innovative solutions across several interconnected areas, including advanced control for active power symmetry, optimized renewable integration and inertia support, intelligent equipment operation, system-wide dynamic analysis, scheduling under uncertainty, and enhanced protection and power quality. By synthesizing advanced computational techniques with core power engineering challenges, this issue provides both theoretical insights and practical methodologies. It underscores a paradigm shift towards actively orchestrating system stability within inherently asymmetric conditions, laying a foundation for the design of more resilient, efficient, and sustainable future grids. Finally, key future research directions are outlined to further integrate adaptive control, physics-informed machine learning, and standardized metrics for holistic system design. Full article

Against the backdrop of electricity market liberalization and deregulation, accurate electricity price forecasting is critical for optimizing power dispatch and promoting the low-carbon transition of energy structures. However, electricity prices exhibit inherent complexities such as seasonality, high volatility, and non-stationarity, which undermine the efficacy of traditional forecasting methodologies. To address these challenges, this study proposes a four-stage Decomposition-Reconstruction-Optimization-Integration (DROI) framework, coupled with an econometric breakpoint test, to evaluate forecasting performance across distinct time segments of Spanish electricity price data. The framework employs CEEMDAN for signal decomposition, decomposing complex price sequences into intrinsic mode functions to retain essential features while mitigating noise, followed by frequency-based data reconstruction; integrates the Improved Sparrow Search Algorithm (ISSA) to optimize initial model parameters, minimizing errors induced by subjective factors; and leverages Convolutional Neural Networks (CNN) for frequency-domain feature extraction, enhanced by an attention mechanism to weight channels and prioritize critical attributes, paired with Long Short-Term Memory (LSTMs) for temporal sequence forecasting. Experimental results validate the method’s robustness in both interval forecasting (IPCP = 100% and IPNAW is the smallest, Experiment 1.3) and point forecasting tasks (MAPE = 1.3758%, Experiment 1.1), outperforming naive approaches in processing stationary sequence clusters and demonstrating substantial economic utility to inform sustainable power system management. Full article

The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a key economic region in China facing increasing pressure to balance socioeconomic development with environmental protection and energy conservation. This study examines the interrelationships among energy consumption, air pollutants (PM_2.5, NO₂, and SO₂), and carbon dioxide (CO₂) emissions in the GBA from 2000 to 2020. Using spatial correlation matrices and temporo-spatial decoupling analysis, we assess spatial patterns, temporal dynamics, and interactions among these factors. Results show that the GBA has made significant progress in reducing air pollution and carbon emissions. Notably, since 2013, concentrations of PM_2.5, NO₂, and SO₂ have decoupled markedly from energy consumption, reflecting effective pollution control measures. Although CO₂ emissions have decreased more gradually, the trend remains positive, indicating steady advances in carbon management. These findings underscore the need for continued optimization of the energy structure to achieve coordinated control of energy use, air quality, and carbon emissions—essential for promoting sustainable, high-quality development in the region. Full article

The development of electric quarry transport puts a significant strain on local power grids, leading to sharp peaks in consumption and degradation of power quality. Existing methods of peak smoothing, such as generation control, virtual power plants, or intelligent load management, have limited efficiency under the conditions of stochastic and high-power load profiles of industrial charging stations. A new strategy for direct charge and discharge management of a system for integrated battery energy storage (IBES) is based on dynamic iterative adjustment of load boundaries. The mathematical apparatus of the method includes the formalization of an optimization problem with constraints, which is solved using a nonlinear iterative filter with feedback. The key elements are adaptive algorithms that minimize the network power dispersion functionality (i.e., the variance of $P_{grid}\left(t\right)$ over the considered time interval) while respecting the constraints on the state of charge (SOC) and battery power. Numerical simulations and experimental studies demonstrate a 15 to 30% reduction in power dispersion compared to traditional constant power control methods. The results confirm the effectiveness of the proposed approach for optimizing energy consumption and increasing the stability of local power grids of quarry enterprises. Full article

Agriculture faces growing pressure to optimize water use, particularly in woody perennial crops where irrigation systems are installed once and seldom redesigned despite changes in canopy structure, soil conditions, or plant mortality. Such static layouts may accumulate inefficiencies over time. This study introduces a decision support system (DSS) that evaluates the hydraulic adequacy of existing irrigation systems using two new concepts: the Resource Overutilization Ratio (ROR) and the Irrigation Ecolabel. The ROR quantifies the deviation between the actual discharge of an installed irrigation network and the theoretical discharge required from crop water needs and user-defined scheduling assumptions, while the ecolabel translates this value into an intuitive A+++–D scale inspired by EU energy labels. Crop water demand was estimated using the FAO-56 Penman–Monteith method and adjusted using canopy cover derived from UAV-based canopy height models. A vineyard case study in Galicia (Spain) serves an example to illustrate the potential of the DSS. Firstly, using a fixed canopy cover, the FAO-based workflow indicated moderate oversizing, whereas secondly, UAV-derived canopy measurements revealed substantially higher oversizing, highlighting the limitations of non-spatial or user-estimated canopy inputs. This contrast (A+ vs. D rating) illustrates the diagnostic value of integrating high-resolution geospatial information when canopy variability is present. The DSS, released as open-source software, provides a transparent and reproducible framework to help farmers, irrigation managers, and policymakers assess whether existing drip systems are hydraulically oversized and to benchmark system performance across fields or management scenarios. Rather than serving as an irrigation scheduler, the DSS functions as a standardized diagnostic tool for identifying oversizing and supporting more efficient use of water, energy, and materials in perennial cropping systems. Full article

The escalating global demand for energy-efficient and sustainable built environments has catalyzed the advancement of Building Energy Management Systems (BEMSs), particularly through their integration with cutting-edge technologies. This review presents a comprehensive and critical synthesis of the convergence between BEMSs and enabling tools such as the Internet of Things (IoT), wireless sensor networks (WSNs), and artificial intelligence (AI)-based decision-making architectures. Drawing upon 89 peer-reviewed publications spanning from 2019 to 2025, the study systematically categorizes recent developments in HVAC optimization, occupancy-driven lighting control, predictive maintenance, and fault detection systems. It further investigates the role of communication protocols (e.g., ZigBee, LoRaWAN), machine learning-based energy forecasting, and multi-agent control mechanisms within residential, commercial, and institutional building contexts. Findings across multiple case studies indicate that hybrid AI–IoT systems have achieved energy efficiency improvements ranging from 20% to 40%, depending on building typology and control granularity. Nevertheless, the widespread adoption of such intelligent BEMSs is hindered by critical challenges, including data security vulnerabilities, lack of standardized interoperability frameworks, and the complexity of integrating heterogeneous legacy infrastructure. Additionally, there remain pronounced gaps in the literature related to real-time adaptive control strategies, trust-aware federated learning, and seamless interoperability with smart grid platforms. By offering a rigorous and forward-looking review of current technologies and implementation barriers, this paper aims to serve as a strategic roadmap for researchers, system designers, and policymakers seeking to deploy the next generation of intelligent, sustainable, and scalable building energy management solutions. Full article

The huge impact of construction on the environment is becoming increasingly apparent, and it is unacceptable to many engineers and designers. A growing interest in sustainable construction has been observed for several years. This is especially true for commercial buildings, where achieving an appropriate standard is often the main criterion for investment. Many current publications deal with the topic of energy related to building use. In contrast, knowledge of the so-called embodied carbon footprint is not yet widespread but increasingly important in the context of low-carbon construction. The study created six different building types by juxtaposing different construction variants with different facade variants. The analysis was given to the “cradle to grave” phases, i.e., A1–A4, B4–B5 and C1–C4. Module D (material recycling) is omitted, as well as phases B1–B3 and B6–B7 related to use, maintenance, repair and energy and water consumption. Phases B1–B3 refer to maintenance repair and use activities that are the responsibility of the building manager, so they are taken as estimates at the concept stage. Phase B6 and B7 were excluded from the study, due to the fact that they are not responsible for the embodied carbon footprint, but the operational one. It was assumed that the values for B6 would be shown independently in the building’s energy performance and the final values would be comparable. The purpose of the study was to verify the factors that have the greatest impact on the results of the embodied environmental footprint. The study showed that changes in the building’s design and facade have the greatest impact on the embodied carbon footprint. Furthermore, not only the quantity of materials used but also their durability is crucial, so using durable finishes to minimize the need for repair and replacement can play a key role in reducing the building’s embodied carbon footprint. Differences between the variants reached approximately 107 kg CO₂e/m² (about 15%). The comparison of impact categories further indicates that solutions optimized for global warming potential are not necessarily favorable in other environmental dimensions. Finally, the relatively moderate spread between the most and least favorable variants within the analyzed scope indicates that material substitution alone is insufficient to achieve deep decarbonization of office buildings. Comprehensive strategies addressing material selection, durability, service life and design for disassembly and reuse are therefore required. Full article

Accurate wind power forecasting is crucial for the secure and efficient integration of renewable energy into the power grid. However, the inherent intermittency and non-stationary nature of wind power pose significant challenges to prediction models. To address these issues, this paper proposes a novel hybrid forecasting framework named VMD-IPCA-IHSO-FSRVFL. This model synergistically combines variational mode decomposition (VMD), incremental principal component analysis (IPCA) for feature selection, an improved holistic swarm optimization (IHSO) algorithm, and a feature space-regularized random vector functional link (FSRVFL) network. The VMD first decomposes the complex original wind power signal into several stable sub-sequences to simplify the prediction task. The IPCA then identifies and selects the most relevant features, reducing data redundancy and noise. Subsequently, the IHSO algorithm is employed to automatically optimize the hyperparameters of the FSRVFL model, enhancing its performance and convergence speed. Finally, the optimized FSRVFL, a computationally efficient semi-supervised learning model, performs the final prediction. The proposed model was validated using four seasonal datasets from a Chinese offshore wind farm. Experimental results demonstrate that our VMD-IPCA-IHSO-FSRVFL model significantly outperforms other benchmark models, including BP, ELM, RVFL, and their variants, across all evaluation metrics (MSE, RMSE, MAE, and R²). The findings confirm that the integration of signal decomposition, effective feature selection, and intelligent parameter optimization substantially improves forecasting accuracy and stability under different seasonal conditions. This study provides a robust and effective solution for wind power prediction, offering valuable insights for wind farm operation and grid management. Full article

Waste generated from agricultural activities is anticipated to increase in the future, especially in less developed countries, and this could cause environmental health risks if these wastes are not well managed. The anaerobic digestion (AD) by co-digesting organic waste is a technology used to produce biogas while utilizing biodigestate as a biofertilizer; however, AD requires a lot of water to be efficient, which could pose water challenges to arid areas. This study evaluated biogas production under semi-dry conditions by augmenting the process with a high-water content wild melon and determined the nutrient composition of the resultant biodigestate. Batch studies of AD were performed to evaluate methane potential of the different animal waste using an online and standardized Automatic Methane Potential Test System (AMPTS) II light for approximately 506 h (21 days) at 38 °C. The highest biomethane potential (BMP) determined for mono and co-substrate digestion was 29.5 NmL CH₄/g VS (CD) and 63.3 NmL CH₄/g VS (CMWM), respectively, which was calculated from AMPTS biomethane yield of 3166.2 NmL (CD) and 1480.6 NmL (CMWM). Water-displacement method was also used to compare biogas yield in wet and semi-dry AD. The results showed high biogas yield of 8480 mL for CM (mono-substrate) and 10,975 mL for CMCC in wet AD. Semi-dry AD was investigated by replacing water with a wild melon (WM), and the highest biogas production was 8000 mL from the CMCC combination augmented with WM. Generally, in wet AD, co-digestion was more effective in biogas production than mono-substrate AD. The biodigestate from different substrate combinations were also evaluated for nutrient composition using X-ray Fluorescence (XRF) analysis, and all the samples contained fair amount of essential nutrients such as calcium (Ca), phosphorus (P), potassium (K) and microelements such as chloride (Cl), magnesium (Mn), iron (Fe), zinc (Zn). This study successfully implemented semi-dry AD from co-digested animal wastes to produce biogas as an energy solution and biofertilizer for crop production, thereby creating a closed-loop system that supports a circular bioeconomy. In addition, the study confirmed that lowering the water content in the AD process is feasible without compromising substantial biogas production. This technology, when optimized and well implemented, could provide sustainable biogas production in areas with water scarcity, therefore making the biogas production process accessible to rural communities. Full article

Traditional oilfields face challenges such as high energy consumption, imprecise control, and lax management in mid-to-late development stages, leading to increased costs and reduced efficiency. To address these issues, this work aims to develop an intelligent optimization framework for intermittent pumping by explicitly integrating physical mechanisms with data-driven modeling. Specifically, we propose a data–physics dual-driven method that combines physics-based parameters derived from seepage mechanics with data-driven feature selection using Pearson correlation analysis to identify nine key production factors. An improved CatBoost regression framework is developed through systematic preprocessing, including data cleaning, cubic polynomial feature expansion, F-value screening, and Z-score normalization. The model is further enhanced using Bayesian hyperparameter optimization, a weight adaptation mechanism, and an attention-based multi-level architecture. The novelty of this work lies in the unified dual-driven optimization strategy and the enhanced CatBoost framework that jointly improve prediction accuracy and model generalization. Experimental results demonstrate that the proposed method can accurately predict pumping operation times. Compared with the original CatBoost model, the MAE of the large-interval model decreases by 56.94%, while that of the small-interval model decreases by 16.23%. In addition, the accuracy of the large-interval model increases by 4.1%, and that of the small-interval model increases by 1.22%. These improvements show that the enhanced CatBoost model significantly strengthens predictive performance. This approach provides a reliable basis for optimizing pumping schedules, reducing energy consumption, and promoting intelligent and refined oilfield management. Full article

The factors restricting the large-scale deployment of smart vehicular networks include application service placement/migration, mobility management, network congestion, and latency. Current vehicular networks are striving to optimize network performance through decentralized framework deployments. Specifically, the urban-level execution of current network deployments often fails to achieve the quality of service required by smart cities. To address these issues, we have proposed a vehicular edge–fog computing (VEFC)-enabled adaptive area-based traffic management (AABTM) architecture. Our design divides the urban area into multiple microzones for distributed control. These microzones are equipped with roadside units for real-time collection of vehicular information. We also propose (1) a vehicle mobility management (VMM) scheme to facilitate seamless service migration during vehicular movement; (2) a dynamic vehicular clustering (DVC) approach for the dynamic clustering of distributed network nodes to enhance service delivery; and (3) a dynamic microservice assignment (DMA) algorithm to ensure efficient resource-aware microservice placement/migration. We have evaluated the proposed schemes on different scales. The proposed schemes provide a significant improvement in vital network parameters. AABTM achieves reductions of 86.4% in latency, 53.3% in network consumption, 6.2% in energy usage, and 48.3% in execution cost, while DMA-clustering reduces network consumption by 59.2%, energy usage by 5%, and execution cost by 38.4% compared to traditional cloud-based urban traffic management frameworks. This research highlights the potential of utilizing distributed frameworks for real-time traffic management in next-generation smart vehicular networks. Full article

This paper presents a comprehensive framework for real-time energy management in microgrids integrating distributed renewable energy sources and demand response (DR) programs. To address the inherent uncertainties in key operational variables—such as load demand, wind speed, solar irradiance, and electricity market prices—this study employs a probabilistic modeling approach. A two-stage stochastic optimization method, combining mixed-integer linear programming and optimal power flow (OPF), is developed to minimize operational costs while ensuring efficient system operation. Real-time dynamic pricing mechanisms are incorporated to incentivize consumer load shifting and promote energy-efficient consumption patterns. Three microgrid scenarios are analyzed using one year of real historical data: (i) a grid-connected microgrid without DR, (ii) a grid-connected microgrid with 10% and 20% DR-based load shifting, and (iii) an islanded microgrid operating under incentive-based DR contracts. Results demonstrate that incorporating DR strategies significantly reduces both operating costs and reliance on grid imports, especially during peak demand periods. The islanded scenario, while autonomous, incurs higher costs and highlights the challenges of self-sufficiency under uncertainty. Overall, the proposed model illustrates how the integration of real-time pricing with stochastic optimization enhances the flexibility, resilience, and cost-effectiveness of smart microgrid operations, offering actionable insights for the development of future grid-interactive energy systems. Full article

The building sector accounts for a significant portion of the global energy consumption and carbon dioxide (CO₂) emissions, making it a critical area for improving energy efficiency. In Africa, the rapid energy demand and costs have further emphasized the urgency of developing effective solutions for reducing building energy use. This paper presents a comprehensive review of data-driven and physics-based modeling approaches for forecasting and optimizing energy performance in non-domestic buildings. The review highlights the evolution of statistical models, classical machine learning methods, deep learning, and hybrid approaches across various application scenarios. Emphasis is placed on the role of data pre-processing techniques, including data fusion and transfer learning, as strategies to address data limitations and improve model generalization. Furthermore, the study evaluates the strengths and limitations of different modeling methods in terms of accuracy, scalability, and applicability in real-world contexts. By integrating insights from recent literature, this paper identifies key research gaps such as the need for standard datasets, physics-informed hybrid modeling, and policy-oriented frameworks. The findings aim to guide building managers, policymakers, and researchers toward adopting robust data-driven solutions that enhance energy resilience, reduce operational costs, and support environmental sustainability in the built environment. The review also justifies the importance of these models for practical applications like energy benchmarking, retrofit planning, and CO₂ reduction, providing a clear link between research and industry implementation. Full article