Search Results (3,088)

Port operators must now reduce emissions without weakening the reliability of cargo-handling and logistics services. Two load groups are especially important in this setting: vessels connected to shore-side facilities during berthing and heavy-duty vehicles working inside the terminal area. Their energy-use patterns shape both dispatch stability and the carbon intensity of the port energy system. This paper therefore proposes an integrated port energy management model that jointly schedules wind power, photovoltaic generation, hydrogen production and storage, shore power, conventional purchases, berthed-vessel demand, and low-carbon heavy-duty transport demand. The model combines price-based demand response with a tiered carbon-trading penalty so that flexible electricity consumption and emission costs are reflected in the dispatch decision. Numerical simulations show that the joint use of demand response and the carbon-penalty mechanism lowers total economic dispatch cost by about $11.05\%$ and reduces carbon emissions by $24.52\%$. The results indicate that coordinated renewable-energy and logistics-aware scheduling can improve the economic and environmental performance of port operations. Full article

This study addresses the technical, environmental, economic, and systemic role of multi-apartment residential buildings as hydrogen consumption nodes within urban energy systems. A representative five-story building comprising 30 apartments and 2400–2800 m² of heated floor area, located in a cold European climate, was modelled with an annual heat demand of approximately 185,000 kWh. Four heating configurations were assessed: a conventional natural gas/biomethane boiler (baseline), a hydrogen boiler, a hydrogen-fuel-cell combined heat and power (CHP) system, and a hybrid heat-pump–hydrogen solution. Dynamic simulations indicate that all hydrogen-based systems can fully satisfy space heating and domestic hot water demand without modifications to the internal hydronic distribution network. The fuel cell CHP achieved an overall efficiency of 93%. It generated approximately 54,000 kWh/year of on-site electricity, while the hybrid configuration reached a seasonal efficiency of 108% and the highest primary energy reduction (46%). Operational CO₂ emissions decreased from 37,800 kg/year (gas baseline) to 1900 kg/year (green hydrogen boiler), 1200 kg/year (fuel cell CHP), and 900 kg/year (hybrid system), corresponding to reductions of up to 98%. Peak-load analysis demonstrated improved operational stability in CHP and hybrid systems, characterised by reduced cycling frequency and enhanced thermal resilience through hydrogen storage integration. Capital expenditure (CAPEX) ranged from 41,000 EUR (gas baseline) to 101,000 EUR (fuel cell CHP), reflecting additional storage, safety, and control requirements. Over a 20-year lifecycle (5% discount rate), the hybrid system achieved the lowest levelized cost of heat (0.076 EUR/kWh), followed by fuel cell CHP (0.081 EUR/kWh), compared to 0.087 EUR/kWh for gas. Payback periods ranged between 9 and 13 years, depending on configuration and hydrogen pricing assumptions. Sensitivity analysis identified a break-even hydrogen price of approximately 0.085 EUR/kWh, while carbon pricing above 100 EUR/t CO₂ significantly improves economic competitiveness. District-scale aggregation modelling suggests that hydrogen-equipped multi-apartment buildings can reduce grid electricity imports by 30–40% through on-site generation and seasonal storage. The findings confirm that multi-apartment buildings offer structural and economic advantages for early hydrogen deployment compared to dispersed housing typologies. By combining high demand density, centralised infrastructure, and compatibility with sector-coupling strategies, such buildings can function as distributed energy hubs within decarbonized urban systems. Full article

This data descriptor presents CINES-REC-CITY, an open synthetic dataset providing high-resolution load profiles for energy community research. The dataset represents a typical German urban district with 70 apartments across eight multi-family buildings, including diverse socioeconomic characteristics. Three main components are provided at 15 min resolution for a full year: non-controllable residential electricity consumption for all apartments, charging profiles for 17 battery electric vehicles with trip information, and heat pump operation data for both variable-speed and hysteresis-controlled ground-source systems. All profiles were generated using validated bottom-up stochastic simulation models accounting for realistic user behavior, mobility patterns, and thermal building physics. The modular structure allows for selective combination of components, enabling investigation of different technology penetration scenarios. The dataset serves as a reference benchmark for reproducible research, allowing for direct comparison of optimization approaches, business models, and control strategies using identical underlying consumption patterns. It is suitable for techno-economic analysis, algorithm development for flexible load control, and grid impact assessment. All data is provided in CSV format with weather data for consistent extensions. Full article

Accurate urban building energy modeling (UBEM) is constrained by mismatches between standard climate inputs and actual urban microclimate conditions. This study introduces a scalable, bottom-up, framework that integrates EnergyPlus building energy modeling simulation outputs with Earth observational and geographical-based urban morphology data, which are enhanced through machine learning techniques to improve energy demand predictions in urban settings. Applied to Los Angeles (LA), California, we evaluate the representativeness of typical meteorological year (TMYx) sampling sites against actual urban environmental conditions. We find that while satellite-derived surface temperatures show reasonable alignment with average city conditions, significant discrepancies are observed in urban form metrics such as tree cover, street cover, and building density, suggesting that TMYx stations should be placed in denser urban areas. We augment EnergyPlus simulations for 19 single-family buildings, with remote sensing data using machine learning models, to generate city-wide residential energy consumption heatmaps corrected for microclimate conditions. Models capture substantial intra-urban variation, with predicted energy use differing by approximately 10% between neighborhoods. Feature importance analysis highlights land surface temperature as a key predictor, underscoring its relevance to building energy research. We also find the majority of TMY3 sampling sites to be in low-vulnerability areas, underscoring the structural mismatch that is embedded in urban form and climate. This framework offers a scalable path for integrating urban microclimate effects into energy modeling to enable more precise and equitable energy policy and planning. Full article

Solar power plants are among the most widely used renewable energy sources today. Varying radiation levels from region to region, and similarly varying consumption depending on the user within a given region, make the optimal sizing of these plants challenging. In this study, a machine learning-based surrogate model for the real-time sizing optimization of solar power plants, trained with a completely original dataset, has been developed. In the first stage, 500 different solar power plant installation scenarios were synthetically generated and evaluated in HOMER, and the obtained optimal sizing outputs were used as training targets for the proposed surrogate model rather than real operational data. The results obtained by applying various machine learning methods to the generated dataset are presented comparatively. Among 7 different machine learning models, XGBoost, Gradient Boosting, and LightGBM demonstrated the best performance. The developed model achieved an average R² score of 0.9425 for a total of 3 targets, while target-specific performance showed R² scores of 0.9747 for inverters, 0.9365 for PV panels, and 0.9165 for batteries. This model serves as a computationally efficient surrogate of the HOMER optimization process, enabling high-accuracy real-time predictions while significantly reducing the computational burden associated with intensive mathematical calculations, iterative procedures, and complex search spaces. Full article

Against the backdrop of the dual-carbon strategy, China’s civil aviation industry, as a high-energy-consumption and high-carbon-emission sector, faces mounting pressure for low-carbon transformation. As the dominant airlines within China’s civil aviation system, Air China, China Eastern Airlines, and China Southern Airlines play a pivotal role in guiding the industry’s high-quality development. Employing the Global Malmquist–Luenberger (GML) index model, this study constructs a global production frontier incorporating undesirable outputs to systematically measure the dynamic evolution of total factor productivity (TFP) for the three major airlines in the period 2005–2023, and further applies a combined static-dynamic regression framework to identify the firm-level heterogeneous mechanisms through which explanatory factors operate. The results reveal significant heterogeneity in TFP trajectories: China Southern Airlines exhibits the most stable efficiency with the lowest volatility; China Eastern Airlines displays the greatest volatility but the strongest post-crisis rebound; and Air China occupies an intermediate position in both efficiency level and volatility. This differentiation stems from fundamental differences in market positioning, strategic orientation, and resource allocation patterns. Market competitiveness exerts a significantly positive effect on TFP for both Air China and China Eastern Airlines. Technological innovation investment generates short-run negative effects across all three airlines, albeit with divergent magnitudes. Human capital accumulation acts as a positive driver for Air China but produces a negative effect for China Southern Airlines, attributable to a structural mismatch between aggressive talent upgrading and organizational absorptive capacity. Shifting the unit of analysis to the firm level, this study identifies three heterogeneous strategic archetypes—market-led, scale-expansion, and regional-deepening—and constructs a differentiated “one firm, one policy” framework to provide targeted policy guidance for improving airline efficiency and facilitating low-carbon transition under carbon constraints. Full article

Spatially explicit socioeconomic data are critical for regional analysis, yet they remain scarce at subnational scales in many African contexts. This study presents a transparent and reproducible open-data framework to generate high-resolution gridded Gross Domestic Product (GDP) and derived socioeconomic and energy indicators. The approach combines gridded population and Night-Time Light (NTL) through the LitPop method to downscale provincial GDP to 1 km resolution for the Inkomati-Usuthu Water Management Area (IUWMA) in South Africa. The resulting GDP dataset is subsequently used as a spatial proxy to disaggregate compensation of employees, gross capital formation, fixed capital stock, net exports, gross operational surplus and sectoral Total Final Energy Consumption (TFEC). Results show strong consistency with official provincial GDP totals, with deviations ±0.4% after 2017. In 2024, LitPop allocated 4.26 billion constant 2015 USD to the IUWMA, equivalent to 16% of Mpumalanga’s GDP, compared with 47.3% under area-based allocation and 51.3% under population-based allocation. These differences reveal the strong influence of spatially concentrated industrial and energy-intensive activity. The workflow provides a scalable and replicable solution to generate coherent gridded socioeconomic datasets for WEF Nexus modelling, although estimates remain proxy-based and sensitive to NTL-related biases, particularly the overrepresentation of highly illuminated industrial assets and the underrepresentation of less luminous activities. Full article

The study presents the results of research aimed at developing digital models for determining the operating parameters of railway power supply systems equipped with distributed generation plants based on renewable energy sources (RESs). RESs can be used in railway transport to increase the reliability of power supply to facilities located in areas with insufficiently developed power grids. This primarily applies to consumers, for whom a power failure can lead to significant damage, accidents, and a threat to human life. RES can serve as independent power sources for special-group consumers and can increase energy conversion efficiency. Furthermore, large-scale implementation of renewable energy sources can significantly reduce energy supply costs and improve power quality. The study employs phase-coordinate modeling, which is characterized by the following features: a systems approach, which implies determining operating conditions while considering the properties and characteristics of complex traction and supply networks; versatility, which enables modeling of power supply systems of various structures and designs; and comprehensiveness, which involves calculating normal, emergency, and special operating parameters—crucial for scenarios such as ice melting on catenary wires. The modeling results obtained using the Fazonord AC-DC software (ver. 5.3.5.2) show that RES-based distributed generation plants provide a variety of beneficial effects: reduction in electricity consumption from power system networks; decrease in voltage unbalance and harmonic distortion on the busbars of regional windings of traction substations; and stabilization of voltage levels on current collectors of electric locomotives. Full article

The rapid development of wind and solar energy necessitates a solution to the problem of the optimal spatial placement of wind farms (WFs) and solar farms (SFs) within electric power systems. The non-stationary generation schedules of WFs and SFs place increased demands on the flexibility of conventional generation, determined by the intensity of net load fluctuations. This paper proposes a methodology for the spatial optimization of WF and SF location, in which the optimization criteria include net load indicators (rate of net load change and net load increment), the base power of the RES system, and the economic criterion of maximum electricity generation. Unlike existing approaches, in which the geographical smoothing effect on power fluctuations is treated as an incidental outcome, the proposed methodology employs it as an explicit optimization criterion for RES placement. The algorithm provides for the preliminary ranking of candidate sites based on the maximum electricity generation criterion, followed by the redistribution of generating capacities among sites with an acceptable capacity factor in accordance with the selected optimization criterion. The methodology was tested on a model comprising six potential wind farm sites and two solar farm sites with a total installed capacity of 600 MW and a maximum power system load of 3000 MW. The obtained results show that the optimal redistribution of installed capacities among sites allows a 31.5% reduction in net load variability intensity to be achieved with an 11.6% reduction in electricity generation relative to the maximum possible. The study is based on idealized daily generation and consumption profiles and is theoretical in nature, proposing a pre-screening tool for RES siting that complements rather than replaces subsequent network-constrained planning studies, including power-flow analysis and grid verification, and establishes a methodological foundation for further development using real multi-year retrospective data. Full article

This study examines how the Internet of Things (IoT) acts as a key enabler of sustainability in industrial production systems within the Industry 4.0 paradigm, addressing the fragmented understanding of the mechanisms linking digital technologies to environmental, operational, and emerging human-centric outcomes. A systematic literature review was conducted following PRISMA 2020 guidelines using the Web of Science Core Collection. After applying explicit inclusion and exclusion criteria, 69 peer-reviewed studies published between 2016 and 2026 were analyzed through qualitative thematic synthesis and comparative analysis. The findings reveal that IoT functions as a foundational digital infrastructure enabling real-time monitoring, operational transparency, and data-driven decision-making in production environments. Four dominant application domains are identified: (i) energy and resource efficiency, (ii) production monitoring and control, (iii) predictive maintenance and asset management, and (iv) emerging human-centric production systems aligned with Industry 5.0. While IoT consistently improves operational reliability and resource efficiency, its contribution to the social dimension of sustainability remains comparatively underdeveloped. This study advances the existing literature by providing a mechanism-oriented synthesis that explains how IoT-enabled infrastructures generate sustainability outcomes across production systems. Furthermore, it establishes a conceptual bridge between Industry 4.0 digitalization and the transition toward human-centric and resilient manufacturing models associated with Industry 5.0. From a practical perspective, the results highlight that IoT adoption contributes to reducing energy consumption, optimizing resource utilization, and enhancing operational performance, while also supporting safer and more adaptive working environments. However, challenges related to data integration, workforce adaptation, and digital capability gaps persist, underscoring the need for inclusive and strategically aligned digital transformation processes. Full article

In milling operations, cutters entering and exiting workpiece boundaries cause varying radial immersions and chip thicknesses. This generates aperiodic cutting forces that often induce vibrations and degrade surface quality. To address this, this study aims to accurately predict milling forces and surface profiles during these critical engagement and disengagement phases. An analytical approach was developed to estimate the changing distances between the cutting teeth and workpiece boundaries, enabling the precise calculation of the dynamic chip thickness as the cutter transitions through the material. Based on these dynamic calculations, milling forces and system responses were simulated. Experimental validation demonstrated a strong agreement between the simulated cutting forces, machined surface profiles, and real-world results. Notably, findings revealed that even cutting parameters deemed stable by traditional stability lobes can still trigger vibrations during these boundary transitions. Consequently, a novel parameter selection strategy is proposed to effectively prevent these transient vibrations, significantly enhancing the final surface finish. Ultimately, this comprehensive modelling framework provides a deeper understanding of the system dynamics throughout the entire milling process, offering high relevance for broader applications, such as optimising energy consumption, predicting tool wear, and improving machining parameter optimisation. Full article

This paper investigates the potential of localized air-curtain microclimate control to reduce HVAC energy consumption in electric vehicles while maintaining occupant thermal comfort. The study compares conventional full-cabin cooling with driver-focused and passenger-focused air-curtain configurations under controlled ambient conditions of 32 °C. The experimental framework combines analytical airflow and heat-transfer modeling with comparative HVAC performance evaluation using power consumption, time to reach thermal comfort, and Predicted Mean Vote (PMV) analysis. The results show that the air-curtain configurations reduce HVAC power consumption from 3.2 kW for conventional cooling to 2.3 kW and 2.5 kW for the driver- and passenger-focused configurations, corresponding to energy savings of approximately 22–28%. In addition, localized airflow significantly accelerates thermal comfort attainment, reducing stabilization time from 8 min to 4–5 min while maintaining PMV values within acceptable comfort limits. The findings demonstrate that occupant-centered air-curtain microclimate strategies can improve HVAC energy efficiency, reduce auxiliary energy demand, and support more sustainable and range-efficient operation of next-generation electric vehicles. Full article

Accurate forecasting of hourly electricity consumption is critical for smart grid management, energy market operations, national policy planning, and—particularly for import-dependent economies such as Türkiye—energy security. This study presents, to the best of the authors’ knowledge, the first systematic benchmark of four state-of-the-art time series architectures—TimesNet, PatchTST, iTransformer, and Temporal Fusion Transformer (TFT)—conducted specifically on a national-scale Turkish multivariate energy dataset from the Energy Exchange Istanbul (EPİAŞ), covering 72,322 hourly observations across 15 generation, consumption, and market-clearing price variables from January 2018 to April 2026. While benchmark studies of Transformer-based architectures exist on general time-series datasets, no prior work has applied this specific combination of architectures to the EPİAŞ dataset under unified experimental conditions with an explicit energy-security interpretation. All models were trained under standardized preprocessing (StandardScaler), a 24 h lookback window, and systematic hyperparameter optimization. Experimental results demonstrate that iTransformer achieves the best predictive performance (MAE = 521.34 MWh, RMSE = 748.12 MWh, R² = 0.9881, MAPE = 1.34%), followed by TFT (R² = 0.9863) and PatchTST (R² = 0.9844). TimesNet, while the most computationally efficient, achieves an R² of 0.9791. Beyond predictive benchmarking, this study situates the findings within Türkiye’s energy security agenda: the dataset captures fossil fuel dependency, the growing share of domestic renewables, and market-clearing price dynamics shaped by geopolitical shocks, including the Russo–Ukrainian war and evolving EU–Türkiye energy relations. Comprehensive analysis of model architectures, attention mechanisms, temporal feature importance, and computational efficiency is provided. These findings establish a rigorous baseline for deploying modern sequence models in large-scale, real-time national energy forecasting systems that serve both market-efficiency and strategic-energy-autonomy objectives. The results specifically highlight how high-fidelity forecasting can serve as a risk-mitigation tool against geopolitical supply disruptions by quantifying the impact of domestic renewable integration. Full article

This study investigates the use of interpretable machine learning techniques to identify behavioral and demographic patterns associated with diabetes, based on structured population survey data from the Canadian Community Health Survey (CCHS). A decision tree classifier was applied to a dataset comprising $16,824$ respondents and 38 preprocessed features covering lifestyle, well-being, and sociodemographic factors. The model was optimized through grid search with five-fold stratified cross-validation, achieving a test accuracy of $61.3$% (mean $62.6$% ±$0.6$% across a $10\times 5$ repeated stratified cross-validation). Feature importance analysis revealed that age, alcohol consumption patterns, daily energy expenditure, and physical activity were the most influential factors associated with diabetes status, with the top three features exhibiting stable importance across all cross-validation folds. The model produced a set of 32 human-readable decision rules; a sensitivity analysis confirmed that these rules are stable across encoding choices and cross-validation folds. Several model variants were evaluated: a class-weighted decision tree, a logistic regression baseline, an age-only decision tree, and an age and sex logistic regression. The class-weighted model improved minority-class recall (from $0.25$ to $0.53$) at the cost of overall accuracy. A one-hot encoding sensitivity analysis showed that replacing ordinal label encoding of nominal variables with one-hot encoding produces virtually identical results (accuracy: $61.4$% vs. $61.3$%), confirming that the main rules are not artifacts of the encoding choice. Although the classification accuracy is moderate and not significantly better than a majority-class baseline (McNemar’s test, $p=0.455$), the extracted rules confirmed several known associations and revealed interactions between social and lifestyle variables. These rules are intended as hypothesis-generating population-level descriptors rather than validated clinical decision tools, and no causal inference is claimed. This approach demonstrates the value of rule-based models for exploratory public health research. Full article

Sixth-generation (6G) networks are expected to enable a new level of connectivity, with peak data rates reaching 1 Tbps and latencies below 0.1 ms, especially in large-scale Internet of Things (IoT) environments. Despite these advantages, the rapid increase in device density poses multiple challenges, most notably the growth in control plane signaling and the associated increase in energy consumption. These issues might significantly affect the scalability and efficiency of future networks if left unaddressed. We propose EASE-6G, an energy-aware Software-Defined Networking (SDN) framework that moves network operation from reactive to proactive and predictive, supporting ultra-dense conditions, where the number of connected devices may reach ${10}^6$ devices per square kilometer. EASE-6G uses Proactive Flow Installation to reduce the need for instant decisions. Traffic is predicted using a Long Short-Term Memory (LSTM) model, while a signaling-aware Deep Q-Network (DQN) streamlines control, reducing unnecessary signaling while maintaining performance. Simulations in OMNeT++/Simu5G were performed to compare EASE-6G with Smart Fog Radio Access Network (SF-RAN) and Deep Q-Network-based Open Radio Access Network (DQN-ORAN). EASE-6G was found to reduce energy consumption by 36.8%, signaling overhead by 36.7%, and latency by 35.6%. The LSTM model achieved a Mean Absolute Percentage Error (MAPE) of 4.2%. The DQN agent showed improved stability, with 22% lower variance than the baseline. These results demonstrate that the proposed predictive SDN control mechanisms improve energy efficiency and reduce overhead, delivering a practical solution for the implementation of scalable, sustainable IoT in future 6G networks. Full article