Search Results (1,214)

Internal fins are commonly utilized as a passive technique to enhance natural convection, but their efficiency depends on complex interplay between fin design, material properties, and convective strength. This study presents an extensive numerical analysis of buoyancy-driven flow in square cavities containing a single horizontal fin on the hot wall. Over 9000 simulations were conducted, methodically varying the Rayleigh number (Ra = 10 to 10⁵), Prandtl number (Pr = 0.1 to 10), and fin characteristics, such as length, vertical position, thickness, and the thermal conductivity ratio (up to 1000), to assess their overall impact on thermal efficiency. Thermal enhancements compared to scenarios without fins are quantified using local and average Nusselt numbers, as well as a Nusselt number ratio (NNR). The results reveal that, contrary to conventional beliefs, long fins positioned centrally can actually decrease heat transfer by up to 11.8% at high Ra and Pr due to the disruption of thermal plumes and diminished circulation. Conversely, shorter fins located near the cavity’s top and bottom wall edges can enhance the Nusselt numbers for the hot wall by up to 8.4%, thereby positively affecting the development of thermal boundary layers. A U-shaped Nusselt number distribution related to fin placement appears at Ra ≥ 10³, where edge-aligned fins consistently outperform those positioned mid-height. The benefits of high-conductivity fins become increasingly nonlinear at larger Ra, with advantages limited to designs that minimally disrupt core convective patterns. These findings challenge established notions regarding passive thermal enhancement and provide a predictive thermogeometric framework for designing enclosures. The results can be directly applied to passive cooling systems in electronics, battery packs, solar thermal collectors, and energy-efficient buildings, where optimizing heat transfer is vital without employing active control methods. Full article

This work proposes a new method to refill fuel cell electric vehicle hydrogen tanks from a storage system in hydrogen refueling stations. The new method uses the storage tanks in cascade to supply hydrogen to the refueling station dispensers. This method reduces the hydrogen compressor power requirement and the energy consumption for refilling the vehicle tank; therefore, the proposed alternative design for hydrogen refueling stations is feasible and compatible with low-intensity renewable energy sources like solar photovoltaic, wind farms, or micro-hydro plants. Additionally, the cascade method supplies higher pressure to the dispenser throughout the day, thus reducing the refueling time for specific vehicle driving ranges. The simulation shows that the energy saving using the cascade method achieves 9% to 45%, depending on the vehicle attendance. The hydrogen refueling station design supports a daily vehicle attendance of 9 to 36 with a complete refueling process coverage. The carried-out simulation proves that the vehicle tank achieves the maximum attainable pressure of 700 bars with a storage system of six tanks. The data analysis shows that the daily hourly hydrogen demand follows a sinusoidal function, providing a practical tool to predict the hydrogen demand for any vehicle attendance, allowing the planners and station designers to resize the elements to fulfill the new requirements. The proposed system is also applicable to hydrogen ICE vehicles. Full article

As the global energy transition accelerates, the integration of solar power and artificial intelligence (AI) technologies offers new pathways for sustainable local development. This study examines four Serbian municipalities—Šabac, Sombor, Pirot, and Čačak—to assess how AI-enabled solar power systems can enhance energy resilience, reduce emissions, and support community-level sustainability goals. Using a mixed-method approach combining spatial analysis, predictive modeling, and stakeholder interviews, this research study evaluates the performance and institutional readiness of local governments in terms of implementing intelligent solar infrastructure. Key AI applications included solar potential mapping, demand-side management, and predictive maintenance of photovoltaic (PV) systems. Quantitative results show an improvement >60% in forecasting accuracy, a 64% reduction in system downtime, and a 9.7% increase in energy cost savings. These technical gains were accompanied by positive trends in SDG-aligned indicators, such as improved electricity access and local job creation in the green economy. Despite challenges related to data infrastructure, regulatory gaps, and limited AI literacy, this study finds that institutional coordination and leadership commitment are decisive for successful implementation. The proposed AI–Solar Integration for Local Sustainability (AISILS) framework offers a replicable model for emerging economies. Policy recommendations include investing in foundational digital infrastructure, promoting low-code AI platforms, and aligning AI–solar projects with SDG targets to attract EU and national funding. This study contributes new empirical evidence on the digital–renewable energy nexus in Southeast Europe and underscores the strategic role of AI in accelerating inclusive, data-driven energy transitions at the municipal level. Full article

Evaporative cooling (EC) offers an energy-efficient alternative to direct expansion (DX) cooling but suffers from high water consumption. This limitation can be mitigated by pre-cooling incoming fresh air using cooler exhaust air via energy recovery. This study presents and optimizes a solar-driven EC system integrated with underfloor air distribution (UFAD) to enhance thermal comfort and minimize water use in a temporary office in Riyadh’s arid climate. A 3D CFD model was developed and validated against published data to simulate indoor airflow, providing data for thermal comfort evaluation using the predicted mean vote model in cases with and without energy recovery. A year-round hourly energy analysis revealed that the solar-driven EC-UFAD system reduces grid power consumption by 93.5% compared to DX-based UFAD under identical conditions. Energy recovery further cuts annual EC water usage by up to 31.3%. Operational costs decreased by 84% without recovery and 87% with recovery versus DX-UFAD. Full article

Photovoltaic panels have become a promising solution for generating renewable energy and reducing our reliance on fossil fuels by capturing solar energy and converting it into electricity. The effectiveness of this conversion depends on several factors, such as the quality of the solar panels and the amount of solar radiation received in a specific region. This makes accurate solar irradiance forecasting essential for planning and managing efficient solar power systems. This study examines the application of machine learning (ML) models for accurately predicting global horizontal irradiance (GHI) using a three-year dataset from six distinct photovoltaic stations: NELHA, ULL, HSU, RaZON+, UNLV, and NWTC. The primary aim is to identify optimal shared features for GHI prediction across multiple sites using a 30 min time shift based on autocorrelation analysis. Key features identified for accurate GHI prediction include direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and solar panel temperatures. The predictions were performed using tree-based algorithms and ensemble learners, achieving $R^2$ values exceeding 95% at most stations, with NWTC reaching 99%. Gradient Boosting Regression (GBR) performed best at NELHA, NWTC, and RaZON, while Multi-Layer Perceptron (MLP) excelled at ULL and UNLV. CatBoost was optimal for HSU. The impact of time-shifting values on performance was also examined, revealing that larger shifts led to performance deterioration, though MLP performed well under these conditions. The study further proposes a stacking ensemble approach to enhance model generalizability, integrating the strengths of various models for more robust GHI prediction. Full article

The growing demand for energy-efficient heating, ventilation, and air conditioning (HVAC) systems has increased interest in multi-source heat pumps as a sustainable solution. While extensive research has been conducted on heat pump performance prediction, there is still a lack of practical tools for early-stage system evaluation. This study addresses that gap by developing regression-based models to estimate the performance of various heat pump configurations, including air-source, ground-source, and dual-source systems. A simplified performance estimation model was created, capable of delivering results with accuracy levels comparable to TRNSYS simulation outputs, making it a valuable and accessible tool for system evaluation. The analysis was conducted across nine climatic zones in Italy, considering key environmental factors such as air temperature, ground temperature, and solar irradiance. Among the tested configurations, hybrid systems like Solar-Assisted Ground-Source Heat Pumps (SAGSHP) achieved the highest performance, with SCOP values up to 4.68 in Palermo and SEER values up to 5.33 in Milan. Regression analysis confirmed strong predictive accuracy (R² = 0.80–0.95) and statistical significance (p < 0.05), emphasizing the models’ reliability across different configurations and climatic conditions. By offering easy-to-use regression formulas, this study enables engineers and policymakers to estimate heat pump performance without relying on complex simulations. Full article

This study experimentally validates a solar-thermal desalination system equipped with predictive feedwater control guided by real-time solar forecasting. Unlike conventional systems that react to temperature changes, the proposed approach proactively adjusts feedwater flow in anticipation of solar variability. To assess environmental and financial sustainability, the study integrates this control logic with a full Life Cycle Assessment (LCA) and Techno-Economic Analysis (TEA). Field testing in a high-temperature, arid region demonstrated strong performance, achieving a Global Warming Potential (GWP) of 1.80 kg CO₂-eq/m³ and a Levelized Cost of Water (LCOW) of $0.88/m³. Environmental impacts were quantified using OpenLCA and ecoinvent datasets, covering climate change, acidification, and eutrophication categories. The TEA confirmed economic feasibility, reporting a positive Net Present Value (NPV) and an Internal Rate of Return (IRR) exceeding 11.5% over a 20-year lifespan. Sensitivity analysis showed that forecast precision and TES design strongly influence both environmental and economic outcomes. The integration of intelligent control with simplified thermal storage offers a scalable, cost-effective solution for off-grid freshwater production in solar-rich regions. Full article

Accurate forecasting of electricity production is crucial for the stability of the entire energy sector. However, predicting future renewable energy production and its value is difficult due to the complex processes that affect production using renewable energy sources. In this article, we examine the performance of basic deep learning models for electricity forecasting. We designed deep learning models, including recursive neural networks (RNNs), which are mainly based on long short-term memory (LSTM) networks; gated recurrent units (GRUs), convolutional neural networks (CNNs), temporal fusion transforms (TFTs), and combined architectures. In order to achieve this goal, we have created our benchmarks and used tools that automatically select network architectures and parameters. Data were obtained as part of the NCBR grant (the National Center for Research and Development, Poland). These data contain daily records of all the recorded parameters from individual solar and wind farms over the past three years. The experimental results indicate that the LSTM models significantly outperformed the other models in terms of forecasting. In this paper, multilayer deep neural network (DNN) architectures are described, and the results are provided for all the methods. This publication is based on the results obtained within the framework of the research and development project “POIR.01.01.01-00-0506/21”, realized in the years 2022–2023. The project was co-financed by the European Union under the Smart Growth Operational Programme 2014–2020. Full article

Energy Performance Contracts (EPC) are performance-based financing mechanisms designed to improve energy efficiency and support renewable energy adoption in the public sector. This study examines the economic efficiency of a 1710.72 kWp solar power plant (SPP), implemented under an EPC at Alanya Alaaddin Keykubat University, using a regression-based analysis. The model evaluates the effects of solar radiation, investment cost, and electricity sales price on unit production cost, and its predictions were compared with actual production data. Results show the system exceeded the EPC contract target by 16.2%, producing 2,423,472.28 kWh in its first year and preventing 1168.64 tons of CO₂ emissions. The developed multiple linear regression model achieved a predictive error margin of 14.7%, confirming its validity. This study highlights the technical, economic, and environmental benefits of EPC applications in Türkiye’s public institutions and offers a practical decision-support framework for policymakers. The novelty lies in integrating a regression model with operational data and providing a comparative assessment of planned, predicted, and actual outcomes. Full article

IoT devices with sensors and actuators are frequently deployed in environments without access to the power grid. These devices are battery powered and might make use of energy harvesting if battery lifetime is too limited. This article focuses on automatically adapting the duty cycle frequency to the predicted available solar energy so that a continuous operation of IoT applications is guaranteed. The implementation is based on a low-cost solar control board that is integrated with the Serverless IoT Framework (SIF), which provides an event-based programming paradigm for microcontroller-based IoT devices. The paper presents a case study where the IoT device sleep time is pro-actively adapted to a predicted sequence of cloudy days to guarantee continuous operation. Full article

As global electricity demand increases and concerns over fossil fuel usage intensify, renewable energy sources have gained significant attention. Solar energy stands out due to its low installation costs and suitability for deployment. However, solar power generation remains difficult to predict because of its dependence on weather conditions and decentralized infrastructure. To address this challenge, this study proposes a flexible hybrid ensemble (FHE) framework that dynamically selects the most appropriate base model based on prediction error patterns. Unlike traditional ensemble methods that aggregate all base model outputs, the FHE employs a meta-model to leverage the strengths of individual models while mitigating their weaknesses. The FHE is evaluated using data from four solar power plants and is benchmarked against several state-of-the-art models and conventional hybrid ensemble techniques. Experimental results demonstrate that the FHE framework achieves superior predictive performance, improving the Mean Absolute Percentage Error by 30% compared to the SVR model. Moreover, the FHE model maintains high accuracy across diverse weather conditions and eliminates the need for preliminary validation of base and ensemble models, streamlining the deployment process. These findings highlight the FHE framework’s potential as a robust and scalable solution for forecasting in small-scale distributed solar power systems. Full article

A novel multi-scale copula-based model predictive control (MPC) method is proposed to address the core regulation challenges of runoff hydropower and distributed photovoltaic systems within high-penetration renewable energy grids. Complex spatio-temporal complementarity under ecological constraints and the limitations of conventional methods were critically analyzed. The core innovation lies in integrating copula theory with MPC, enabling adaptive spatio-temporal optimization and weight adjustment to significantly enhance the efficiency of complementary regulation and overcome traditional performance bottlenecks. Key nonlinear dependencies of water–solar resources were investigated, and mainstream techniques (copula analysis, MPC, rolling optimization, adaptive weighting) were evaluated for their applicability. Future directions for improving modeling precision and intelligent adaptive control are outlined. Full article

A solar air heater (SAH) converts solar energy into heated air without causing environmental pollution. It features a low initial cost and easy maintenance due to its simple design. However, owing to air’s poor thermal conductivity, its thermal efficiency is relatively low compared to that of other solar systems. To improve its thermal performance, previous studies have aimed at either enlarging the heat transfer surface or increasing the convective heat transfer coefficient. In this study, a novel SAH with fins and sequentially placed obstacles on the fin surface—designed to achieve both surface extension through a finned channel and enhancement of the heat transfer coefficient via the obstacles—was investigated using computational fluid dynamics analysis. The results confirmed that the obstacles enhanced heat transfer performance by up to 2.602 times in the finned channel. However, the obstacles also caused a pressure loss. Therefore, the thermo-hydraulic performance was discussed, and it was concluded that the obstacles with a relative height of 0.12 and a relative pitch of 10 yielded the maximum THP values among the investigated conditions. Additionally, correlations for the Nusselt number and friction factor were derived and predicted the simulation values with good agreement. Full article

This paper focuses on the triple-junction gallium arsenide solar array of a MEO (Medium Earth Orbit) satellite that has been in orbit for 7 years. Through a combination of theoretical and data-driven methods, it conducts research on anti-radiation design verification and life prediction. This study integrates the Long Short-Term Memory (LSTM) algorithm into the full life cycle management of MEO satellite solar arrays, providing a solution that combines theory and engineering for the design of high-reliability energy systems. Based on semiconductor physics theory, this paper establishes an output current calculation model. By combining radiation attenuation factors obtained from ground experiments, it derives the theoretical current values for the initial orbit insertion and the end of life. Aiming at the limitations of traditional physical models in addressing solar performance degradation under complex radiation environments, this paper introduces an LSTM algorithm to deeply mine the high-density current telemetry data (approximately 30 min per point) accumulated over 7 years in orbit. By comparing the prediction accuracy of LSTM with traditional models such as Recurrent Neural Network (RNN) and Feedforward Neural Network (FNN), the significant advantage of LSTM in capturing the long-term attenuation trend of solar arrays is verified. This study integrates deep learning technology into the full life cycle management of solar arrays, constructs a closed-loop verification system of “theoretical modeling–data-driven intelligent prediction”, and provides a solution for the long-life and high-reliability operation of the energy system of MEO orbit satellites. Full article

Solar power generation is rapidly emerging within renewable energy due to its cost-effectiveness and ease of deployment. However, improper inspection and maintenance lead to significant damage from unnoticed solar hotspots. Even with inspections, factors like shadows, dust, and shading cause localized heat, mimicking hotspot behavior. This study emphasizes interpretability and efficiency, identifying key predictive features through feature-level and What-if Analysis. It evaluates model training and inference times to assess effectiveness in resource-limited environments, aiming to balance accuracy, generalization, and efficiency. Using Unmanned Aerial Vehicle (UAV)-acquired thermal images from five datasets, the study compares five Machine Learning (ML) models and five Deep Learning (DL) models. Explainable AI (XAI) techniques guide the analysis, with a particular focus on MPEG (Moving Picture Experts Group)-7 features for hotspot discrimination, supported by statistical validation. Medium Gaussian SVM achieved the best trade-off, with 99.3% accuracy and 18 s inference time. Feature analysis revealed blue chrominance as a strong early indicator of hotspot detection. Statistical validation across datasets confirmed the discriminative strength of MPEG-7 features. This study revisits the assumption that DL models are inherently superior, presenting an interpretable alternative for hotspot detection; highlighting the potential impact of domain mismatch. Model-level insight shows that both absolute and relative temperature variations are important in solar panel inspections. The relative decrease in “blueness” provides a crucial early indication of faults, especially in low-contrast thermal images where distinguishing normal warm areas from actual hotspot is difficult. Feature-level insight highlights how subtle changes in color composition, particularly reductions in blue components, serve as early indicators of developing anomalies. Full article