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Solar photovoltaic (PV) systems, integral for sustainable energy, face challenges in forecasting due to the unpredictable nature of environmental factors influencing energy output. This study explores five distinct machine learning (ML) models which are built and compared to predict energy production based on four independent weather variables: wind speed, relative humidity, ambient temperature, and solar irradiation. The evaluated models include multiple linear regression (MLR), decision tree regression (DTR), random forest regression (RFR), support vector regression (SVR), and multi-layer perceptron (MLP). These models were hyperparameter tuned using chimp optimization algorithm (ChOA) for a performance appraisal. The models are subsequently validated on the data from a 264 kWp PV system, installed at the Applied Science University (ASU) in Amman, Jordan. Of all 5 models, MLP shows best root mean square error ( RMSE ), with the corresponding value of 0.503, followed by mean absolute error ( MAE ) of 0.397 and a coefficient of determination ( R 2 ) value of 0.99 in predicting energy from the observed environmental parameters. Finally, the process highlights the fact that fine-tuning of ML models for improved prediction accuracy in energy production domain still involves the use of advanced optimization techniques like ChOA, compared with other widely used optimization algorithms from the literature.

The growing demand for reliable and sustainable energy sources, coupled with concerns over greenhouse gas emissions and fossil fuel depletion, necessitates the development of hybrid renewable energy systems that can ensure energy security, improve efficiency, and reduce environmental impact. This study addresses the need for integrated renewable solutions by investigating the energy performance and economic feasibility of a hybrid system that combines photovoltaic (PV) panels, parabolic trough collectors (PTC), and a lab-scale biomass-fired steam power plant. The primary objective is to optimize system performance while minimizing fuel consumption and operational costs. The proposed system includes a PTC unit, a 4.6 kW PV array, a 6.4 kW biomass-powered DC generator, three 3 kWh batteries, and a 3 kW converter. Energy assessment was conducted through experimental measurements supported by simulation and optimization using HOMER and PVsyst software. Results show that the integration of PV panels reduced biomass fuel consumption by approximately 70%, leading to a 50% reduction in operational costs over a 10-year period. The system achieved a favorable payback period of just 2.2 years. These findings highlight the viability of hybrid PV–PTC–biomass systems as a sustainable and cost-effective solution for clean energy generation in decentralized or off-grid applications.

This study develops and solves a micropolar-fluid model for heat storage in a rectangular duct subject to constant wall heat flux. The dimensionless mass, linear and angular momentum, and energy equations are treated with a Runge-Kutta (for the coupled momentum ODEs) and finite-difference scheme (for the energy PDE). Increasing the coupling parameter raises the dimensionless axial velocity while initially reducing and then increasing microrotation; the net effect is a decrease in dimensionless temperature, indicating diminished storage effectiveness. In contrast, higher spin-gradient viscosity reverses these trends and enhances thermal storage. Reynolds and Prandtl numbers exhibit inverse relationships with the temperature profile. Quantitatively, the mean Nusselt number is ≈ 2.53 at an entrance length of x * ≈ 5.5 , while the local Nusselt number decays approximately exponentially downstream and approaches zero. For a low-inertia case ( Re = 5 ), the peak fluid temperature reached ∼ 95 ∘ C under the imposed heat flux. These results clarify how micropolar parameters govern heat transfer and storage performance, offering guidance for tuning ducts that use micropolar working fluids.

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling and grid management. This paper presents a comprehensive review conducted with reference to a pioneering, comprehensive, and data-driven framework proposed for solar Photovoltaic (PV) power generation prediction. The systematic and integrating framework comprises three main phases carried out by seven main comprehensive modules for addressing numerous practical difficulties of the prediction task: phase I handles the aspects related to data acquisition (module 1) and manipulation (module 2) in preparation for the development of the prediction scheme; phase II tackles the aspects associated with the development of the prediction model (module 3) and the assessment of its accuracy (module 4), including the quantification of the uncertainty (module 5); and phase III evolves towards enhancing the prediction accuracy by incorporating aspects of context change detection (module 6) and incremental learning when new data become available (module 7). This framework adeptly addresses all facets of solar PV power production prediction, bridging existing gaps and offering a comprehensive solution to inherent challenges. By seamlessly integrating these elements, our approach stands as a robust and versatile tool for enhancing the precision of solar PV power prediction in real-world applications.

This study introduces a novel comparative analysis of thermal management systems for lithium-ion battery packs using four LiFePO4 batteries. The research evaluates advanced configurations, including a passive system with a phase change material enhanced with extended graphite, and a semipassive system with forced water cooling. A key innovation lies in replacing water with a nanofluid in a single cold plate surrounded by a phase change composite, leveraging the superior thermal properties of nanoparticles. Further advancements are presented in a three-plate system and a complex-plate system, which employ modified cold plate designs and two-dimensional flow dynamics for enhanced cooling. Among these, the complex-plate system with nanofluid proved most effective, reducing the maximum temperature by 17.17% from 88.17 °C in the passive system to 73.03 °C, while extending the operational temperature threshold by 7.41%. Nanoparticles contributed to a 9.46% temperature reduction, highlighting their impact on thermal performance. Notably, the complex-plate system surpassed the three-plate configuration in efficiency, achieving superior cooling with lower pumping power requirements. This study emphasizes the novelty and practicality of integrating nanofluids and advanced cooling designs, setting a benchmark for optimizing lithium-ion battery thermal management systems.

The unpredictability of intermittent renewable energy (RE) sources (solar and wind) constitutes reliability challenges for utilities whose goal is to match electricity supply to consumer demands across centralized grid networks. Thus, balancing the variable and increasing power inputs from plants with intermittent energy sources becomes a fundamental issue for transmission system operators. As a result, forecasting techniques have obtained paramount importance. This work aims at exploiting the simplicity, fast computational and good generalization capability of Extreme Learning Machines (ELMs) in providing accurate 24 h-ahead solar photovoltaic (PV) power production predictions. The ELM architecture is firstly optimized, e.g., in terms of number of hidden neurons, and number of historical solar radiations and ambient temperatures (embedding dimension) required for training the ELM model, then it is used online to predict the solar PV power productions. The investigated ELM model is applied to a real case study of 264 kWp solar PV system installed on the roof of the Faculty of Engineering at the Applied Science Private University (ASU), Amman, Jordan. Results showed the capability of the ELM model in providing predictions that are slightly more accurate with negligible computational efforts compared to a Back Propagation Artificial Neural Network (BP-ANN) model, which is currently adopted by the PV system owners for the prediction task.

Water-in-diesel (W/D) emulsion fuel is a potentially viable diesel fuel that can simultaneously enhance engine performance and reduce exhaust emissions in a current diesel engine without requiring engine modifications or incurring additional costs. In a consistent manner, the current study examines the impact of adding water, in the range of 5–30% wt. (5% increment) and 2% surfactant of polysorbate 20, on the performance in terms of brake torque (BT) and exhaust emissions of a four-cylinder four-stroke diesel engine. The relationship between independent factors, including water addition and engine speed, and dependent factors, including different exhaust released emissions and BT, was initially generated using machine learning support vector regression (SVR). Subsequently, a robust and modern optimization of the sea-horse optimizer (SHO) was run through the SVR model to find the optimal water addition and engine speed for improving the BT and lowering exhaust emissions. Furthermore, the SVR model was compared to the artificial neural network (ANN) model in terms of R-squared and mean square error (MSE). According to the experimental results, the BT was boosted by 3.34% compared to pure diesel at 5% water addition. The highest reduction in carbon monoxide (CO) and unburned hydrocarbon (UHC) was 9.57% and 15.63%, respectively, at 15% of water addition compared to diesel fuel. The nitrogen oxides (NOx) emissions from emulsified fuel were significantly lower than those from pure diesel, with a maximum decrease of 67.14% at 30% water addition. The suggested SVR-SHO model demonstrated superior prediction reliability, with a significant R-Squared of more than 0.98 and a low MSE of less than 0.003. The SHO revealed that adding 15% water to the W/D emulsion fuel at an engine speed of 1848 rpm yielded the optimum BT, CO, UHC, and NOx values of 49.5 N.m, 0.5%, 57 ppm, and 369 ppm, respectively. Finally, these outcomes have important implications for the potential of the SVR-SHO approach to minimize engine exhaust emissions while maximizing engine performance.

In this work, a hybrid system consisting of a parabolic trough collector and a steam power plant is proposed. The effect of utilizing the parabolic trough collector on improving the performance of the plant and reducing fuel consumption has been studied experimentally. This study was implemented on a lab scale hybrid energy system consisting of a parabolic trough collector unit incorporated into a biomass-oil shale fired steam power plant during startup conditions. To determine the performance of this lab-scale hybrid system, the efficiency of the parabolic trough collector standalone system has been measured and the flow rate of the system has been tuned to 0.31 L/min to obtain an efficiency of 10.2%. The biomass-oil shale fired power plant worked with superheated steam at 377 °C temperature and 0.6 MPa pressure. The thermal efficiency of the power plant was 12.6% with net output power of 6.3 kW without using the parabolic trough collector unit. It was found that the performance of the hybrid system has shown better efficiency than the standalone biomass fired power plant with the same fuel mixture ratio and steam flowrate. The fuel mixture consumed in the hybrid system decreased by 62.0% at starting up condition. This result may be extended to steady-state operating conditions by increasing the number of parabolic trough collector units utilized. Furthermore, the overall thermal efficiency of the hybrid parabolic trough collector power plant system may reach 33.3% during steady-state operation if 48 parabolic trough collector similar units were used. These parabolic trough collector units should be arranged in three parallel rows, each row of 16 units in series.

Tidal energy is a promising renewable resource capable of supporting global strategies to reduce carbon emissions. This study develops a numerical framework to predict tidal turbine performance under corrugated wavy surface hydrodynamics, capturing the combined influence of turbulent flow and wave–structure interaction. The model integrates the conservation of momentum and continuity equations with a corrugated wavy surface function to represent near-surface velocity fluctuations. The coupled equations were solved in MATLAB® to evaluate the effects of turbine radius, hub depth, wave amplitude, and wavelength under unsteady and incompressible turbulent flow conditions. The results show that increasing the turbine radius from 2 to 4 m raises the power ratio from approximately 0.46 to 0.97 due to the larger swept area. Increasing the hub depth from 5 to 8 m decreases the power ratio from 0.81 to 0.68 because of reduced near-bed velocities. Shorter wavelengths (λ = 0.55 m) and higher wave amplitudes (a = 0.65 m) significantly enhance power output, while longer wavelengths (λ = 0.65 m) produce negligible power (<0.1). The findings indicate that optimal performance occurs with maximum wave amplitude and minimal wavelength and hub depth. The developed model offers a practical theoretical tool for designing and optimizing tidal turbines under realistic marine wave conditions.

This study presents a comprehensive analysis of an organic Rankine cycle (ORC) integrated with a hybrid solar-geothermal energy system. The research aims to evaluate the thermodynamic performance and exergy efficiency of the hybrid system and compare it with a standalone solar ORC. The problem is approached by analyzing exergy destruction within key components, using the entropy method, and evaluating the impact of different working fluids. For the standalone solar ORC with a 1 MW turbine, the turbine and condenser were identified as the main sources of exergy destruction, contributing 40% and 25%, respectively. In the hybrid solar-geothermal ORC, the solar collectors were the dominant source of exergy destruction, contributing 58%, while the turbine's share was reduced to 10%. The use of R245fa improved the overall efficiency by up to 3%. A validated Simulink model was developed, confirming the reliability of the simulation results. The findings highlight the potential of hybrid systems in improving energy efficiency for small- to medium-scale combined heat and power applications, with future work focusing on optimizing operational parameters and exploring additional working fluids.