Search Results (8,164)

Electricity theft is a major source of non-technical losses in distribution networks, threatening both economic revenues and power supply reliability. This study addresses the identification of nodes exhibiting anomalous load behavior (anomalous nodes) in 10 kV distribution feeders. Based on the IEEE-33 bus benchmark system, the disturbance patterns induced by abnormal consumption are analyzed. The results show that voltage and current fluctuations intensify with increasing electrical distance from the power source, while branch loss peaks localize at the affected terminals and propagate unidirectionally along the power flow path. Building on these findings, an improved density-based spatial clustering of applications with noise (DBSCAN) method is proposed, integrating five spatial network features and sixteen temporal electrical features extracted from voltage, current, and power series. Prior to clustering, the features are standardized and reduced via principal component analysis (PCA), retaining over 90% of the cumulative variance. Validation on a hybrid dataset demonstrates that the proposed method achieves 90.7% accuracy, 87.5% recall, and an F1-score of 0.895, outperforming traditional K-means and approaching supervised CNN models without requiring labeled data. These results confirm the method’s robustness and suitability for practical deployment in distribution networks. Full article

The rapid advancement of technologies such as artificial intelligence, big data, and cloud computing has driven continuous expansion of global data centers, resulting in increasingly severe energy consumption and carbon emission challenges. According to projections by the International Energy Agency (IEA), the global electricity demand of data centers is expected to double by 2030. The construction of green data centers has emerged as a critical pathway for achieving carbon neutrality goals and facilitating energy structure transition. This paper presents a systematic review of the role of waste heat recovery technologies in data centers for achieving low-carbon development. Categorized by aspects of waste heat recovery technologies, power production and district heating, it focuses on assessing the applicability of heat collection technologies, such as heat pumps, thermal energy storage and absorption cooling, in different scenarios. This study examines multiple electricity generation pathways, specifically the Organic Rankine Cycle (ORC), Kalina Cycle (KC), and thermoelectric generators (TEG), with comprehensive analysis of their technical performance and economic viability. The study also assesses the feasibility and environmental advantages of using data center waste heat for district heating. This application, supported by heat pumps and thermal energy storage, could serve both residential and industrial areas. The study shows that waste heat recovery technologies can not only significantly reduce the Power Usage Effectiveness (PUE) of data centers, but also deliver substantial economic returns and emission reduction potential. In the future, the integration of green computing power with renewable energy will emerge as the cornerstone of sustainable data center development. Through intelligent energy management systems, cascaded energy utilization and regional energy synergy, data centers are poised to transition from traditional “energy-intensive facilities” to proactive “clean energy collaborators” within the smart grid ecosystem. Full article

The sun tracker plays a major role in improving the energy efficiency of a solar power system. To address this role, this study experimentally explores the energy efficiency of three sun-tracking systems with three types of degrees of freedom (DOFs)—namely, single-axis for both elevation (1DOF_Elev) and azimuth (1DOF_Azim), and dual-axis (2DOF)—integrated in photovoltaic (PV) panels. The three sun-tracking configurations are assessed and compared with the fixed system (0DOF), considering both the net electricity output of the studied photovoltaic system and the energy consumption of each configuration during operation. To accomplish this objective, hardware and software tools were deployed to create a prototype. The sun-tracking techniques are based on the sun position algorithm (astronomical calculations). The different data (time, voltage, current, power, azimuth, and elevation) are stored in real time within a locally developed database which represents crucial data within SCADA systems embedded in smart grids. The results revealed that the 2DOF system exhibits the highest energy efficiency (37.23%), followed by 1DOF_Azim (12.86%), and then by 1DOF_Elev (10.05%), when compared to 0DOF. Overall, this study provides solutions for optimizing photovoltaic energy production and could be integrated into battery-powered devices to accelerate battery recharging, achieving time savings of over 30%. Full article

The electro-hydraulic hybrid system has emerged as a critical technology in new energy vehicles, owing to the remarkable power density and efficient energy regeneration capabilities of hydraulic technology, coupled with the high energy density of electric power. This system effectively enhances vehicle range and battery life. We developed an energy management strategy (EMS) for the electro-hydraulic hybrid system (EHHS) to ensure smooth energy conversion, while ensuring the full utilization of electrical and hydraulic energy within a reasonable and efficient range. To enhance the system’s overall performance, it is imperative to address pivotal technologies, including power coupling and energy management. In this research, the structure of an electro-hydraulic hybrid vehicle (EHHV) is classified, compared and discussed. The application of existing EHHVs is studied. Subsequently, an analysis and summary are conducted on the current status and development trends of EMSs and collaborative operation control strategies (COCSs), and a novel mechanical-electro-hydraulic power-coupled system (MEHPCS) is put forward that successfully converts mechanical, electrical, and hydraulic energy in performance. Simultaneously, other applications of the system are forecasted. Finally, some suggestions for the electro-hydraulic hybrid systems’ future development are made. This study can promote the development of sustainable transportation technologies. The system integrates mechanical engineering, control theory, and environmental science, enabling interdisciplinary methodological innovation. In addition, relevant studies provide data support for policy makers by quantifying energy consumption indicators. Full article

Energy consumption forecasting offers a foundation for governmental agencies to establish energy consumption benchmarks for public institutions. Meanwhile, correlation analysis of institutional energy use provides clear guidance for building energy-efficient retrofits. This study employed five machine learning models to train and predict monthly energy consumption intensity data from 2020 to 2022 for three types of public institutions in China’s eastern coastal regions. A novel ensemble model was proposed and applied for energy consumption prediction. Additionally, the SHAP model was utilized to analyze the correlation between influencing factors and energy consumption data. Finally, the relationship between climatic factors and monthly energy consumption intensity was investigated. Results indicate that the ensemble model achieves higher predictive accuracy compared to other models, with regression metrics on the training set generally exceeding 0.9. Although XGBoost also demonstrated strong performance, it was less stable than the ensemble model. Energy intensity across different building types exhibited strong correlations with the number of energy users, floor area, electricity use, and water consumption. Linear analysis of temperature and energy consumption intensity revealed a directional linear relationship between the two for both medical and administrative buildings. Full article

The recently adopted EU Nature Restoration law emphasises the urgent need to address the ecological impacts of river barriers, which fragment habitats and disrupt natural flows. However, efforts to remove barriers are often constrained by prohibitive costs, regulatory hurdles, and public opposition. In Ireland, barrier removal costs range between EUR 200,000 and EUR 500,000 per structure, representing a substantial financial burden given that more than 73,000 barriers are identified nationwide. Although removal would restore ecological function, it would also eliminate the potential to repurpose these structures for hydropower, thereby reducing opportunities to contribute to the national target of 80% renewable electricity generation by 2030. This study outlines the development of a river barrier modification system to serve the dual purposes of upstream and downstream fish lift over barriers and generation of electricity for local consumption using a fish-friendly pump-as-turbine unit. Under normal flows, the unit generates electricity while during low flows it operates in pumping mode to enable fish passage. A prototype was fabricated and tested at a fish farm using both artificial and live fish. An assessment of the regional potential was also extrapolated from preliminary results suggesting that the BMS offers a cost-effective alternative to full barrier removal, potentially offsetting costs by 50–85% while contributing to both EU restoration targets and national renewable energy goals. Full article

Polyethylene terephthalate (PET) is one of the most widely used plastics globally, and its poor post-consumer management poses serious risks to the environment and human health. Tackling this issue requires innovative strategies that combine recycling and sustainable manufacturing with the principles of the circular economy. This study addresses this challenge by investigating the use of recycled PET, along with reverse logistics, to produce a cell phone holder through additive manufacturing (AM). Characterization was performed using differential scanning calorimetry, thermogravimetric analysis, intrinsic viscosity measurements, and mechanical tensile tests. Environmental and circular performance were evaluated using Life Cycle Assessment (LCA) and the Material Circularity Indicator (MCI), comparing production with 100% virgin PET resin and 100% recycled PET resin. The results showed that the recycled route achieved a tensile strength of 37.7 MPa, with 7.6% strain before rupture, and thermal analysis confirmed its stability during processing. The LCA revealed a 12% reduction in overall environmental impacts when recycled PET replaced virgin resin, with electricity consumption identified as the main critical point. The circularity assessment suggested potential savings of up to 70% if recycled PET products are reprocessed at the end of their life cycles. These findings demonstrate that combining open-loop recycling with additive manufacturing (AM) can effectively turn waste into high-quality, value-added products, advancing circularity and sustainable material innovation. Full article

Railway braking efficiency hinges on the thermomechanical conditions at the wheel-rail interface. Frictional heating during operation causes significant temperature fluctuations, directly impacting braking performance in rail vehicles. Evaluating these effects is important for developing infrastructure and components adapted to environmental conditions. Several studies have explored the influence of temperature on components such as the brake disc or the wheel; little attention has been paid to the thermal conditions of the rail itself. This paper examines the effect of rail temperature on the braking behavior and energy consumption of a railway vehicle. Using a 1:20 railway track, rail segments were subjected to four temperatures (28.5 °C, 40.0 °C, 49.9 °C, 71.0 °C) by heating with Nichrome wire, and tests were performed at three speeds (0.75, 1.00, and 1.30 m/s). The results show that higher rail temperatures improve wheel-rail adhesion up to an optimum point (40.0 °C), beyond which performance deteriorates. In contrast, tests at 71.0 °C showed reduced braking efficiency, despite lower electrical current peaks, indicating a non-linear thermal response. Full article

Coal constitutes China’s most significant resource endowment at present. Utilizing coal resources for hydrogen production represents an early-stage pathway for China’s hydrogen production industry. The analysis of energy quality and carbon emissions in coal gasification-based hydrogen production holds practical significance. This paper integrates the exergy analysis methodology into the traditional LCA framework to evaluate the exergy and carbon emission scales of coal gasification-based hydrogen production in China, considering the technical conditions of CCUS. This paper found that the life cycle exergic efficiency of the whole chain of gasification-based hydrogen production in China is accounted to be 38.8%. By analyzing the causes of exergic loss and energy varieties, it was found that the temperature difference between the reaction of coal gasification and CO conversion unit and the pressure difference due to the compressor driven by the electricity consumption of the compression process in the variable pressure adsorption unit are the main causes of exergic loss. Corresponding countermeasures were suggested. Regarding decarbonization strategies, the CCUS process can reduce CO₂ emissions across the life cycle of coal gasification-based hydrogen production by 48%. This study provides an academic basis for medium-to-long-term forecasting and roadmap design of China’s hydrogen production structure. Full article

This paper introduces a control-oriented Linear Parameter Varying (LPV) model of an energy-efficient electric vehicle, enhanced to account for wind-induced disturbances. The proposed model structure is designed to support model-based control strategies focused on minimizing energy consumption. In addition to core control inputs—such as torque reference and cornering radius—the model integrates a simulated representation of wind effects on the vehicle’s longitudinal dynamics. To manage the underlying nonlinearities of the vehicle dynamics, a trajectory-based linearization approach was employed to construct the baseline LPV model without wind effects. The accuracy of the extended model was validated using real-world speed profile data. Owing to its modular and control-compatible design, the model provides a solid foundation for testing and developing energy-saving control strategies, making it especially applicable to the design and operation of energy-efficient electric vehicles. The proposed model holds significant potential for further reducing energy consumption, particularly in urban transportation scenarios. Full article

This study explores the relationship between electricity consumption and financial development in 20 emerging market economies (EMEs) from 2000 to 2020. Employing the panel ARDL–PMG estimator and a two-step system GMM to address endogeneity, we identify a significant positive long-run cointegrating relationship, where electricity consumption fosters financial development. The estimated error correction term suggests a stable equilibrium, with deviations corrected at a 29% annual rate, in the short-run adjustment. These results underscore the significance of targeted energy investments in driving financial market growth. Policies promoting grid action, renewable integration, and innovative financing tools, such as green bonds, can align electricity expansion with financial stability objectives. By incorporating recent global disruptions and applying advanced econometric methods, this study provides updated empirical evidence and actionable policy insights on the electricity–finance nexus in EMEs. Full article

This paper presents an evaluation of the use of deep learning architectures for forecasting electrical energy consumption in residential environments. The main contribution of this study lies in the development and assessment of a hybrid forecasting framework that integrates multiscale temporal analysis and weather data, enabling evaluation of predictive performance across different temporal granularities, forecast horizons, and aggregation levels. Single and hybrid models were compared, trained with high-resolution data from a single residence, both considering only endogenous variables and including exogenous variables (weather data). The results showed that, among all models tested in this study, the hybrid LSTM + GRU model achieved the highest predictive performance, with R² values of 94.62% using energy data and 95.25% when weather variables were included. Intermediary granularities, particularly the 6 steps, offered the best balance between temporal detail and predictive robustness for the tests performed. Furthermore, short-time windows aggregation (1 to 5 min) showed better accuracy, while the inclusion of weather data in scenarios with larger aggregation windows and longer horizons provided additional gains. The results reinforce the potential of hybrid deep learning models as effective tools for forecasting residential electricity consumption, with possible practical applications in energy management, automation, and integration of distributed energy resources. Full article

Freshwater scarcity and saline groundwater are major constraints for maintaining green roofs in coastal areas. This study evaluated the response of Jacobaea maritima subsp. sicula, (Sicilian silver ragwort) a drought-tolerant coastal ornamental plant, to tap water and seawater irrigation under Mediterranean summer conditions. Plants were grown in 10 cm-deep green-roof modules and subjected to six irrigation regimes: tap water, seawater, or alternating tap water and seawater, each applied at 4- or 8-day intervals, with irrigation volumes equal to 60% of cumulative reference evapotranspiration (ET_o). Growth, relative water content (RWC), chlorophyll index (SPAD), and leachate electrical conductivity were monitored to assess plant performance and salinity responses. Seawater irrigation caused rapid substrate salinization, leaf dehydration, and plant death within one month, while alternating seawater with tap water also failed to sustain survival. In contrast, tap water–irrigated plants maintained high RWC, chlorophyll content, and stable visual quality throughout the experimental period, even with deficit irrigation at 60% ET_o every eight days. These findings demonstrate that J. maritima subsp. sicula is well suited for freshwater-irrigated extensive green roofs in semi-arid regions, providing reliable performance under infrequent irrigation and limited water supply. However, seawater or high-salinity irrigation should be avoided. Future research should explore mixed freshwater–seawater irrigation regimes with a higher freshwater proportion, aiming to reduce total freshwater consumption while sustaining plant survival and esthetic performance. Full article

In this paper, we analyze the possibilities of optimizing the driving strategy for energy-efficient electric vehicles competing in the Shell Eco-marathon race. The base method we already developed and successfully applied for several years—winning the Urban Concept Battery Electric competition of the 2022, 2023, and 2024 Shell Eco-marathon races—was further tested, with small modifications to our optimization method. We only used an optimizer tool based on a genetic algorithm. We were interested in determining how a modification to the minimalization problem could help our optimizer find the best driving cycle to reach the minimum energy consumption. We successfully applied the modification to our method at the 2025 competition, where we beat our own record and proved its practical applicability. Full article

The purpose of this study is to develop a multiphysical model of agglomeration of low-grade phosphorites with the addition of phosphate-siliceous shales and oil sludge. To achieve these tasks, a numerical approach was used in the COMSOL Multiphysics environment, based on solving the related problems of heat transfer and hydrodynamics during heat treatment of the material. A laboratory vertical tubular furnace made of heat-resistant quartz glass with electric heating was used to study the effect of the temperature field and the velocity of gases on the degree of sintering and the dynamics of phosphorous agglomerate formation under various technological conditions. It has been established that the optimal temperature for the agglomeration process is a layer temperature of 950–1000 °C at a gas flow rate of 1.5–2 m/s, which ensures the formation of durable granules and minimizes sintering heterogeneity. The maximum sintering layer height of the test charge reaches 210–230 mm at pressures of 0.015–0.027 MPa. A comparison of the numerical simulation results with experimental data showed a good agreement, which confirms the practical significance of the proposed model for the design and optimization of industrial processes of agglomeration of phosphorous raw materials. Modern physical and chemical analyses have established the phase, microstructural, and element-by-element characteristics of the studied phosphate-siliceous shale and the product of agglomeration firing. The results of modeling the hydrodynamics of the charge agglomeration process can be recommended to increase the efficiency of processing phosphate-containing waste and reduce energy consumption. Full article