Applied Solar Energy最新文献

This paper examines modern approaches to the design and modeling of thermal energy storage (TES) systems for solar greenhouses. It is shown that integrating TES with solar collectors helps stabilize the indoor microclimate and reduce fossil fuel consumption. Particular attention is given to the application of phase change materials (PCMs), which offer high energy storage density and the ability to maintain a nearly constant temperature during phase transition. Key factors affecting TES performance are analyzed, including thermal stratification, PCM selection, tank geometry, and heat transfer enhancement techniques (fins, metal foams, nanoparticles). Durability issues are also discussed, such as PCM degradation resistance, sealing, and corrosion resistance of containers. Special emphasis is placed on the use of numerical methods such as the finite difference method, computational fluid dynamics (CFD), and TRNSYS for analyzing transient charging and discharging processes. It is shown that integrated dynamic models enable consideration of interactions among all system components and allow real-time optimization of control strategies.

The work presents the feasibility and performance study of an autonomous cold room powered by photovoltaic energy, adapted for preserving agricultural products in the Saharan region, particularly in off-grid areas. An experimental test was conducted over two consecutive days to evaluate the system’s performance with a water load of 400 liters. For the first day, the results showed that, with an average evaporation temperature of –1.12°C and an average ambient temperature of 40.2°C, the cooling capacity produced is 5.23 kW, and the compressor consumption is 1.215 kW, resulting in a COP of approximately 4.33. The second day, under operating conditions defined by an average evaporation temperature of 1.12°C and an average ambient temperature of 41.30°C, the cooling capacity produced by the cycle is 5.75 kW, and the compressor consumption is 1.213 kW, corresponding to the COP of approximately 4.47. The temperature of the load (water) decreased from 32 to 10.5°C in 48 h, while the air temperature inside the cold storage decreased from 31°C and stabilized in the range of 6 to 12°C in one hour and 23 min. The average temperature gradient between the outer surface and the inner surface of the cold storage ceiling is significant (14.5°C), indicating that the thermal insulation thickness of the walls was well optimized. Regarding superheating, the results obtained are better because it falls within the superheating ideal range (5–10°C). The results obtained in this study are satisfactory for integrating the solar photovoltaic cooling technology in off-grid areas.

Solar thermal collector performance prediction plays an important role in enhancing system efficiency and developing sustainable energy systems. This paper develops a comprehensive machine learning-based framework for predicting the energy and exergy efficiency of a TiN-water nanofluid charged heat pipe-assisted solar thermal collector. Detailed experimental research was conducted in initial phase to generate a huge dataset containing approximately 500 steady-state operational configurations. This experimentally acquired information was used to train, validate, and test predictive models, laying the groundwork for subsequent machine learning analysis. Before developing the model, Boruta feature selection based on Random Forest significance was used to evaluate the relative relevance of operating parameters while maintaining all physically relevant inputs. Six supervised regression algorithms were implemented using a unified scikit-learn pipeline that included data standardisation and methodical hyperparameter optimisation via GridSearchCV: Multi-Layer Perceptron, Decision Tree, Random Forest, Gradient Boosting, K-Nearest Neighbours, and Extreme Gradient Boosting (XGBoost). Several statistical measures were employed to evaluate the model’s performance, including the coefficient of determination (R²), root mean square error (RMSE), mean absolute error, and mean absolute percentage error. The results clearly reveal that tree-based ensemble models outperform neural network and instance-based techniques. The Gradient Boosting model was the best accurate predictor of energy efficiency, with an R² of 0.9552 and an RMSE of 0.6592. XGBoost performed better at predicting exergy efficiency, with a R² of 0.9583 and an RMSE of 0.2609. When both performance goals were considered simultaneously, XGBoost was proven to be the most stable and reliable common model. Furthermore, heat flux and filling ratio were identified as the key regulating parameters utilising SHAP-based explainable artificial intelligence analysis, which is highly congruent with known thermodynamic principles and experimental data.

Solar drying is an environmentally friendly method for preserving agricultural products; however, limited studies have jointly examined energy–exergy performance and data-driven prediction methods under varying airflow conditions, which restricts performance optimization. This study aims to investigate the effects of inlet airflow rate on energy utilization ratio, energy generation, exergy loss, exergy efficiency, and drying kinetics of mint leaves, and to evaluate the predictive capability of artificial neural network modeling. Thin-layer drying experiments were conducted in a forced convective solar dryer equipped with a newly designed solar air collector at different air mass flow rates. Energy and exergy analyses were conducted using experimental thermodynamic data, and energy utilization and exergy loss were modeled with a feed-forward back-propagation artificial neural network. The energy utilization ratio ranged from 7.45 to 87.1% and increased with decreasing airflow rate. The average exergy losses for air mass flow rates of 0.012, 0.026, and 0.033 kg s^–1 were 16.2, 8.2, and 6.88 W, respectively. Both experimental and artificial neural network results showed that increasing airflow rate reduced energy utilization ratio and exergy loss. The artificial neural network predictions exhibited very close agreement with experimental data. The results show that artificial neural networks can accurately predict energy utilization ratio and exergy loss in a forced convective solar dryer. Airflow rate is a key parameter for thermodynamic performance, and the integrated experimental–ANN approach offers a robust tool for optimizing and designing solar drying systems.

The global imperative for energy transition demands advanced, high-efficiency solar energy systems capable of overcoming both the thermal degradation of photovoltaic (PV) cells and the inherent intermittency of solar irradiance. This comprehensive review systematically analyzes low-concentration photovoltaic/thermal (LCPV/T) systems, focusing specifically on water-cooled devices equipped with flat-plate reflector concentrators (FRCs), and their critical integration with electrical energy storage (EES) and thermal energy storage (TES). The key objective lies in a co-optimization strategy aimed at increasing the capacity factor, ensuring load dispatchability, and enhancing the system’s energy autonomy by balancing competing electrical (EES) and thermal (TES) demands. LCPV/T systems effectively achieve high co-generation performance, with total efficiencies frequently reported exceeding 75%. The review provides a deep dive into the thermal management aspects, including detailed fluid dynamics modeling for the heat exchanger, and highlights the technical suitability of the thermal output for decentralized, low-temperature applications such as pool heating, which enhances system relevance for off-grid consumers. Future research must prioritize the development of robust, predictive control strategies to mitigate forecasting uncertainty and achieve high system autonomy, thereby unlocking the full commercial potential of this hybrid technology.

The freezing phenomenon in the Bohai Sea area of China is relatively severe in winter. To ensure the safety and reliability of the structure of offshore wind turbine generators, it is crucial to study the effective control of their vibration responses under the usage environment. Based on the structural damage and dynamic responses of offshore wind turbine generators under the coupling effect of wind, waves and ice, this paper studies and discusses the response patterns under different working conditions, and conducts research on the effective suppression of vibration responses. The results show that the coupling of ice loads plays an important role in the vibration of offshore wind power towers. The aerodynamic damping generated by turbulent wind can effectively reduce the vibration amplitude of the tower, but the wind load can only reduce the longitudinal bending moment value of the tower base to a certain extent. The ice-induced effect is more obvious at the lateral bending moment of the tower base. Compared with the action of wind load alone, the influence of coupled ice excitation on the peak bending moment of the tower base can reach more than six times that of the wind load alone. Through damping suppression, the vibration amplitude can be effectively reduced, and the operational stability of the tower of offshore wind turbine generators can be improved.

Dust composition governs the spectral response of soiled modules. The present study quantified how composition and morphology propagate to photovoltaic power loss by linking measured elemental mixes to effective optical properties and, in turn, to AM1.5 electrical metrics. Elemental analyses of dust samples were converted to oxides and combined with a Bruggeman effective medium (SiO₂ 14.9%, Al₂O₃ 3.4%, Fe₂O₃ 4.2%, CaCO₃ 4.8%, K₂O 0.8%, graphite 67.8%, CaSO₄ 4.1%) to obtain wavelength-dependent refractive indices from 400–900 nm. A radiative workflow mapped extinction to spectral transmittance, then area-mixes by soiling fraction and convolves with silicon responsivity. Integrated losses rose with shading losses of 0.17% at 0.01 soiling fraction, 8.37% at 0.50, and 16.73% at 0.99 corresponding to 4.992, 4.582, and 4.164 W on a 5 W reference cell. Attenuation was strongest below 550 nm and the penalty appeared primarily as reduced short-circuit current, while open-circuit voltage changes remained at the millivolt level and fill factor was constant. Maximum power therefore was a result of the integrated optical loss. Morphology of dust particles modulated severity and at a fixed loading of 5.0 gm^–2, increasing median radius from 5 to 50 µm reduced loss from 17.3 to 12.1%, and at a fixed size of 10 µm, increasing loading from 0.5 to 5.0 gm^–2 raised losses from 6.6 to 15.9%. Sensitivity analysis identified soiling fraction as the dominant driver with a normalized overall contribution of 1.00, above mass loading (0.39) and particle size (0.19). Numerical evaluation showed mean transmittance deviations less than 0.5% across 1–25 nm wavelength steps. Operationally, output typically fell by 5% at soiling fraction 0.20–0.30, and a 10% loss occurs near 2.0 gm^–2 for 10 µm dust, providing practical triggers for cleaning and yield planning.

The freezing phenomenon in the Bohai Sea area of China in winter is relatively serious. Offshore wind turbines operating in ice areas not only have to bear wind loads but also are subject to the instantaneous impact and continuous action of sea ice loads. Due to the fact that the support structure of the wind turbine is a thin-walled hollow cylindrical steel structure with a large length-to-diameter ratio, it is highly prone to buckling failure under the coupling action of ice load, hydrodynamic load and pneumatic load, which increases the dynamic response of the tower structure, affects the operational stability of the wind turbine, and may even lead to serious accidents such as structural collapse. This paper takes the NREL OC4 5MW offshore wind turbine as the research object. By using the open-source OpenFAST to update and calculate the boundary conditions of aerodynamic load, ice load and hydrodynamic load in real time, the dynamic response of the wind turbine tower under the coupling action of wind, waves and ice can be effectively and accurately calculated.

The results of monitoring a 1.6 kW photovoltaic power plant operating for 14 years in a sharply continental climate are presented. The encapsulant transmittance decreased by 54.0% during this time, and the efficiency by ≈60%. This sharp decline in efficiency is due to a significant decrease in the encapsulant transmittance due to yellowing, as well as (presumably) partial degradation of the semiconductor structure of the photovoltaic module under the influence of ultraviolet radiation and extreme temperature changes characteristic of the climate of Uzbekistan. An approximate formula for predicting changes in the main photovoltaic module parameters over time is proposed.

Access to reliable energy remains a significant challenge for delivering consistent healthcare services in rural and underserved regions of developing countries. Dental healthcare facilities, in particular, depend on a stable and uninterrupted power supply for essential operations such as lighting, sterilization and clinical equipment. Although photovoltaic (PV) systems offer a clean and sustainable energy solution, many solar-powered healthcare installations fail to achieve optimal performance due to poor system design, mismatched load profiles and inefficiencies in energy conversion. This study proposes an optimized PV-based energy system for dental healthcare applications by focusing on device-level improvements in monocrystalline silicon (mono-Si) solar cells. The optimization is achieved through controlled variation of silicon absorber thickness and doping concentration. A simulation-based approach was employed to evaluate key cell and panel-level performance parameters, including conversion efficiency, fill factor, open-circuit voltage, short-circuit current and overall energy output. System performance was modelled using logarithmic regression, with model accuracy validated through R² and Mean Absolute Percentage Error (MAPE) analysis. The results indicate that an optimal trade-off between energy output and cost-effectiveness is achieved at a silicon thickness of (0.6–0.8 µm) and a doping concentration of 10¹⁹ cm^–3. The MAPE values remain below 5%, confirming the robustness and reliability of the proposed empirical models. Under these optimized conditions, the PV system demonstrates increased module capacity, higher energy yield and a reduced number of required panels. This enables a more compact and cost-effective PV system design. Overall, this work bridges the gap between semiconductor-level solar cell optimization and system-level energy performance. The proposed data-driven approach provides a scalable solution for improving the sustainability and reliability of PV-powered dental healthcare facilities, particularly in energy-constrained environments. Hence, this work is expected to offer practical insights for tailored PV system implementation in healthcare applications.