Energy Science & Engineering最新文献

The integration of Digital Twin (DT) technology into building energy management systems has garnered significant attention for its potential to enhance energy efficiency. This study conducts a systematic literature review to critically evaluate the role of DTs in optimizing energy use, reducing operational costs, and improving the sustainability of building environments. Through a comprehensive analysis of existing research, this review highlights how DTs facilitate continuous monitoring, predictive maintenance, and operational optimization, thereby contributing to more energy-efficient building operations. The findings reveal that while DTs offer substantial benefits, challenges such as data integration, high initial costs, and the need for specialized expertise hinder widespread adoption. To address these barriers, this study proposes a framework for the successful implementation of DT technology in building energy management, emphasizing the importance of standardized protocols, cross-disciplinary collaboration, and incremental scaling. This study provides valuable insights for both practitioners and researchers, offering a strategic roadmap to leverage DT technology for achieving energy sustainability and operational excellence in the built environment.

Predicting energy consumption helps countries make strategic decisions in many critical areas such as energy management, economic development, energy security, environmental sustainability and infrastructure investments. Therefore, accurate and reliable energy consumption predictions are vital to ensure the sustainability and prosperity of countries. This study aims to contribute to the proper planning of transportation policies and energy management by successfully predicting Türkiye's railway energy consumption. In this direction, energy prediction values were obtained from 18 different machine learning methods using the country's railway line length, number of passengers, freight amount and energy consumption values from 1977 to 2024. To further strengthen the results obtained with these methods, bagging, boosting, stacking and blending ensemble learning methods were utilized. With the improvements, the R-squared value was increased up to 0.9667 and energy predicting was achieved with very high accuracy. Based on the results obtained from this study, it is possible to provide investment planning more efficiently. In addition, the implementation of energy management strategies, infrastructure planning and sustainable energy policies will be provided more efficiently as a result of obtaining more successful results by using ensemble machine learning methods instead of traditional machine learning methods for energy consumption predictions in different sectors.

The conventional direct field-oriented control (DFOC) strategy using proportional–integral (PI) regulators for managing the energy of a doubly fed induction generator (DFIG) in wind turbine systems often proves inadequate due to the PI controller's sensitivity to parameter variations. Additionally, it tends to produce lower-quality energy output. To address these shortcomings, this study proposes a novel control strategy that combines two fractional-order controllers: a fractional-order proportional-derivative (FOPD) regulator and a fractional-order integral dual-derivative (FOIDD) regulator. These regulators are valued for their simplicity, low cost, and ease of implementation. The hybrid FOPD–FOIDD approach aims to enhance the performance and robustness of the traditional DFOC-PI control applied to DFIG-based wind turbine systems, enabling improved power regulation and dynamic response. To further optimize the designed control system, Particle Swarm Optimization is used to fine-tune the controller parameters, ensuring efficient and stable power generation under varying and dynamic wind conditions. The new regulator replaces the classical PI in the DFOC scheme for the rotor-side converter of the DFIG. The design and simulations were realized in MATLAB, and results were rigorously compared with those of the DFOC-PI system under diverse operating conditions, including variations in active power reference, rapid wind speed changes, and parameter uncertainties. The comparative analysis demonstrates that the proposed FOPD–FOIDD controller significantly outperforms the DFOC-PI. Simulation results show major improvements in dynamic performance, including reductions in current harmonic distortion by up to 87.55% and 14.14%, and substantial decreases in active power, torque, and reactive power ripples—by 93.18%, 92.42%, and 74.99%, respectively. Overall, the new control strategy exhibits superior robustness and stability, maintaining high-quality power generation despite unpredictable variations in generator parameters.

Factors like pricing, transmission expansion, and capacity planning rely on accurate power demand forecasts. This paper intends to utilize time-series models to forecast the peak electricity demand of Jordan's power grid amidst its energy transition, offering insights into necessary expansion and system adjustments over the next decade It explores the relationship between the country's peak load fluctuations over the last three decades and examining factors including the Gross Domestic Product (GDP) and population growth. Autoregressive Integrated Moving Average (ARIMA), and Autoregressive Integrated Moving Average with Explanatory Variable (ARIMA-X), are employed to forecast yearly peak loads, which are also compared with linear regression, providing an enhanced understanding of power generation and network expansion needs for the coming decade. The results show strong correlations between peak load, population growth, and GDP, with the models proving effective in forecasting future peak loads, albeit with caution regarding ARIMA-X. Projections suggest a potential 41% increase in peak load by 2035, reaching around 5300 MW in 14 years. Assuming consistent growth rates in population and GDP, the projections of the peak load also indicate that the peak load might reach twice its current level in the next 2 to 2.5 decades.

The worldwide transition to renewable energy is gaining momentum, highlighting the importance of identifying and utilizing local wind resources as a viable approach to decrease dependence on fossil fuels and promote sustainable energy security. This investigation examined 15 years of wind speed data (2010–2024) from Khaf, Iran, utilizing machine-learning methods alongside statistical modeling and economic evaluation to deliver an in-depth assessment of the area's wind energy potential. The Weibull probability distribution was utilized to define the statistical characteristics of wind speeds and to determine the parameters necessary for calculating wind power density. The forecast for monthly mean wind speeds in 2025 was conducted using SARIMAX and Prophet models, resulting in a high level of predictive accuracy (R² = 0.85–0.98), which facilitates accurate estimation of wind energy capacity. This study combines machine-learning forecasting, statistical methods, and economic analysis to offer a practical framework for evaluating the regional energy profile and enhancing turbine selection, thereby contributing to the sustainable development of wind farms in Khaf.

Deep coal and rock gas, as an important component of unconventional natural gas, has attracted much attention due to its abundant reserves. However, in the current process of coal and rock gas development, underground accidents of burying drilling tools due to sand bridge stuck drill accidents often occur, which is one of the important difficulties faced by coal and rock gas wells at present. This paper systematically studies the mechanism of stuck drill caused by sand Bridges in deep coal-rock-gas horizontal Wells for the problem of stuck drill in sand Bridges. Studies show that collapse and block shedding caused by wellbore instability a key factors in the formation of sand bridges. Traditional models have limited applicability in deep coal-rock gas reservoirs because they ignore this factor. Based on the drilling conditions, considering the “bulldozer effect” and the influence of coal seam collapse and block drop, the calculation models of the stuck pipe resistance of permeable and impermeable sand bridges were established, respectively. The calculation methods of the sand bridge volume and the additional axial force and torque were derived. Through sensitivity analysis, the results show that the length of the sand bridge, the annular clearance, and the internal and external pressure difference significantly affect the stuck pipe resistance. Among them, the resistance effect of the impermeable sand bridge is particularly prominent. The engineering applicability of the model was verified through the example of the horizontal well for coal and rock gas in Changqing. The research proposes control measures such as optimizing the performance of drilling fluid, staged drilling, and controlling the drilling speed, providing theoretical support and technical reference for the drilling operation of deep coal, rock, and gas horizontal Wells.

This paper presents a state-of-the-art literature review on noise, vibration, and harshness (NVH) in hydrogen-fuelled internal combustion engines. Studies published between 2011 and 2025 were screened, covering fundamental flame physics, test-bench work, and recent prototype vehicles. The review links hydrogen's core properties—high flame speed, wide flammability, low ignition energy, strong diffusivity—to specific NVH outcomes such as rapid pressure rise, knock, back-fire, and block resonance. For each pathway we summarise measured noise levels, vibration signatures, and psycho-acoustic findings. Mitigation methods are then grouped: lean premixing, direct injection, adaptive ignition timing, exhaust tuning, and structural damping. Results show that, with these measures, hydrogen engines can approach the NVH envelope of modern gasoline units. Remaining gaps lie in long-term durability under high-frequency loading and in full-vehicle sound quality. Overall, the review clarifies current knowledge, highlights consistent trends, and points to research still needed for quiet, smooth hydrogen mobility.

Increasing global energy demand and renewable energy expansion have heightened the importance of accurate solar irradiance forecasting for effective grid management and capacity planning. Atmospheric pollution significantly affects solar irradiance measurements, requiring air quality integration for precise forecasting in polluted urban environments. This study develops a comprehensive multi-city data set spanning eight geographically diverse locations with systematically categorized pollution levels, from pristine environments (Copenhagen, Sydney) to heavily polluted urban centers (Beijing, New Delhi, Lahore). A pollution-aware neural network training methodology is introduced, representing the first systematic investigation of ensemble model performance across explicitly categorized atmospheric quality levels. The study presents a novel ensemble architecture integrating multi-layer perceptrons, recurrent neural networks, and nonlinear autoregressive with exogenous inputs, specifically designed for forecasting under varying atmospheric pollution conditions. The proposed ensemble model achieves superior performance with R² of 0.8702, RMSE of 1.0809, and MAE of 0.8137, consistently outperforming individual models across all pollution categories and geographical locations. Validation using the HI-SEAS data set confirms superiority over three contemporary state-of-the-art methodologies. The framework incorporates SHapley Additive exPlanations (SHAP) analysis for model interpretability and comprehensive cross-validation procedures. This study establishes a foundational framework for pollution-aware solar forecasting, addressing critical gaps regarding atmospheric variability's impact on prediction accuracy.

The global transition toward clean energy has intensified interest in solar power, especially in regions with favorable geographical conditions. Despite the rapid development and deployment of solar plants, operational challenges remain, particularly in optimizing energy conversion in real time. This paper proposes a practical real-time solar radiation prediction model designed to enhance the performance of solar plants by forecasting available energy, thereby improving control during the energy conversion process. To this aim, an autonomous nonlinear dynamical model with an unknown drift function is considered. A Group Method of Data Handling (GMDH)-based identification approach, supported by a comprehensive experimental dataset, is employed to estimate the drift function and confirm the feasibility of the model. Once the nonlinear model is validated, a theoretical framework is developed to enable adaptive estimation of the model's states and parameters, eliminating the need for offline identification. Experimental results across multiple scenarios demonstrate the model's effectiveness in accurately identifying unknown parameters and state variables under different environmental conditions, geographic locations, and challenging cases such as partial shading. These results highlight the practical potential of the proposed method for improving real-time control and energy efficiency in solar plant operations.

This comprehensive review evaluates Iraq's energy landscape, examining the spatial distribution of renewable and conventional resources, carbon emissions from power generation, and the technoeconomic viability of energy projects. Iraq's electricity generation is overwhelmingly dominated by thermal power plants, accounting for 96.6% of total production, while hydropower contributes 3.39% and solar only 0.059% of Iraq's overall electricity. Despite vast oil and gas reserves, the country faces chronic electricity shortages due to aging infrastructure, reliance on imports, and limited renewable adoption. Iraq possesses significant but underexploited renewable potential across hydropower, solar, wind, biomass, geothermal, wave, and blue energy. Hydropower remains dominant but is constrained by water scarcity and outdated infrastructure. On the other hand, solar and wind demonstrate strong technical and economic feasibility but face grid and financial barriers, while biomass and geothermal resources remain largely untapped. The energy transition is uneven, with CO₂ reductions in governorates such as Al-Muthanna and Kirkuk achieved through partial fuel switching, whereas others continue to experience rising emissions from high-carbon generation. Technoeconomic assessments underscore the competitiveness of renewables, with solar photovoltaic in Al-Nasiriyah and Al-Rutba yielding low-levelized cost of energy values of 0.033–0.035 $/kWh and high-capacity factors, and wind projects in Al-Qaim and Rawa achieving 0.025–0.05 $/kWh. By integrating Iraq's energy challenges, renewable potential, environmental trends, and technoeconomic insights, this review provides policymakers, researchers, and investors with evidence-based guidance to support strategic planning, targeted investments, and the adoption of technologies for a resilient, low-carbon, and economically sustainable energy future.