Search Results (93)

Solar thermal collectors remain a fundamental component of renewable heat generation in the building sector. Recent progress in solar tracking technologies has led to the emergence of adaptive and stepwise tracking systems that enhance radiation capture while maintaining low mechanical and energy demands. This review comprehensively synthesizes current knowledge on the design, modeling, and performance evaluation of such systems, with emphasis on their role in building decarbonization and techno-economic feasibility. The classification of collectors is revisited to highlight the relationship between optical concentration, tracking precision, and thermal output. Comparative studies indicate that adaptive and stepwise tracking strategies improve annual energy yield by 20–35% compared to fixed systems, while reducing the levelized cost of heat (LCOH) by up to 15%. Modeling approaches integrating optical and thermal domains are discussed alongside emerging applications of artificial intelligence, predictive control, and IoT-based monitoring. The paper concludes with an outlook on future research directions, focusing on durability, standardization, and digital integration of solar thermal systems in smart buildings. Overall, adaptive tracking technologies represent a promising pathway toward efficient and sustainable solar heat utilization in the context of global energy transition. Full article

Solar flares are among the most powerful and dynamic events in the solar system, resulting from the sudden release of magnetic energy stored in the Sun’s atmosphere. These energetic bursts of electromagnetic radiation can release up to ${10}^{32}$ erg of energy, impacting space weather and posing risks to technological infrastructure and therefore require accurate forecasting of solar flare occurrences and intensities. This study evaluates the predictive performance of three machine learning algorithms—Random Forest (RF), k-Nearest Neighbors (kNN), and Extreme Gradient Boosting (XGBoost)—for classifying solar flares into four categories (B, C, M, X). Using 13 parameters of the SHARP dataset, the effectiveness of the models was evaluated in binary and multiclass classification tasks. The analysis utilized 8 principal components (PCs), capturing 95% of data variance, and 100 PCs, capturing 97.5% of variance. Our approach uniquely combines binary and multiclass classification with different levels of dimensionality reduction, an innovative methodology not previously explored in the context of solar flare prediction. Employing a 10-fold stratified cross-validation and grid search for hyperparameter tuning ensured robust model evaluation. Our findings indicate that RF and XGBoost consistently demonstrate strong performance across all metrics, benefiting significantly from increased dimensionality. The insights of this study enhance future research by optimizing dimensionality reduction techniques and informing model selection for astrophysical tasks. By integrating this newly acquired knowledge into future research, more accurate space weather forecasting systems can be developed, along with a deeper understanding of solar physics. Full article

In recent years, the widespread adoption of photovoltaic (PV) panels for electricity generation has provided significant momentum toward sustainable energy goals. However, it has been observed that the accumulation of dust and contaminants on panel surfaces markedly reduces efficiency by blocking solar radiation from reaching the surface. Consequently, dust detection has become a critical area of research into the energy efficiency of PV systems. This study proposes SolPowNet, a novel Convolutional Neural Network (CNN) model based on deep learning with a lightweight architecture that is capable of reliably distinguishing between images of clean and dusty panels. The performance of the proposed model was evaluated by testing it on a dataset containing images of 502 clean panels and 340 dusty panels and comprehensively comparing it with state-of-the-art CNN-based approaches. The experimental results demonstrate that SolPowNet achieves an accuracy of 98.82%, providing 5.88%, 3.57%, 4.7%, 18.82%, and 0.02% higher accuracy than the AlexNet, VGG16, VGG19, ResNet50, and Inception V3 models, respectively. These experimental results reveal that the proposed architecture exhibits more effective classification performance than other CNN models. In conclusion, SolPowNet, with its low computational cost and lightweight structure, enables integration into embedded and real-time applications. Thus, it offers a practical solution for optimizing maintenance planning in photovoltaic systems, managing panel cleaning intervals based on data, and minimizing energy production losses. Full article

Advances in machine learning have improved the ability to predict critical environmental conditions, including solar radiation levels that, while essential for life, can pose serious risks to human health. In Ecuador, due to its geographical location and altitude, UV radiation reaches extreme levels. This study presents the development of a chatbot system driven by a hybrid artificial intelligence model, combining Random Forest, CatBoost, Gradient Boosting, and a 1D Convolutional Neural Network. The model was trained with meteorological data, optimized using hyperparameters (iterations: 500–1500, depth: 4–8, learning rate: 0.01–0.3), and evaluated through MAE, MSE, R², and F1-Score. The hybrid model achieved superior accuracy (MAE = 13.77 W/m², MSE = 849.96, R² = 0.98), outperforming traditional methods. A 15% error margin was observed without significantly affecting classification. The chatbot, implemented via Telegram and hosted on Heroku, provided real-time personalized alerts, demonstrating an effective, accessible, and scalable solution for health safety and environmental awareness. Furthermore, it facilitates decision-making in the efficient generation of renewable energy and supports a more sustainable energy transition. It offers a tool that strengthens the relationship between artificial intelligence and sustainability by providing a practical instrument for integrating clean energy and mitigating climate change. Full article

Rapid urbanization across Southern Asia’s coastal regions has significantly increased electricity demand, driving India’s solar sector expansion under the National Solar Mission and positioning the country as the world’s fourth-largest solar market. Nonetheless, methodological limitations remain in applying GIS-based multi-criteria decision analysis (MCDA) frameworks to coastal urban microclimates, which involve intricate land-use dynamics and resilience constraints. To address this gap, this study proposes a multi-criteria GIS- based Analytical Hierarchy Process (AHP) framework, incorporating remote sensing and geospatial data, to assess Solar Farm Sites (SFSs) suitability, supplemented by sensitivity analysis in Thoothukudi coastal city, India. Ten parameters—covering photovoltaic, climatic, topographic, environmental, and accessibility factors—were used, with Global Horizontal Irradiance (18%), temperature (11%), and slope (11%) identified as key drivers. Results show that 9.99% (13.61 km²) of the area has excellent suitability, mainly in the southwest, while 28.15% (38.33 km²) exhibits very high potential along the southeast coast. Additional classifications include good (22.29%), moderate (32.41%), and low (7.16%) suitability zones. Sensitivity analysis confirmed photovoltaic variables as dominant, with GHI (0.25) and diffuse radiation (0.23) showing the highest impact. The largest excellent zone could support approximately 390 MW, with excellent and very high zones combined offering up to 2080 MW capacity. The findings also underscore opportunities for dual-use solar deployment, particularly on salt pans (17.1%), as well as elevated solar installations in flood-prone areas. Overall, the proposed framework provides robust, spatially explicit insights to support sustainable energy planning and climate-resilient infrastructure development in coastal urban settings. Full article

Under the backdrop of global climate warming, both forest vegetation greening and resilience decline coexist, and the consistency of these trends at the regional scale remains controversial. This study uses the kNDVI (Kernel Normalized Difference Vegetation Index) and TAC (Temporal Autocorrelation) index framework, combined with BEAST and Random Forest methods, to quantify and analyze the spatiotemporal evolution of forest resilience and its driving factors in Southwest China from 2000 to 2022. The results show the following: (1) Forest resilience exhibits a “high in the northwest and low in the southeast” spatial distribution, with a temporal pattern of “increase-decrease-increase.” The years 2010 and 2015 are key turning points. Trend shift analysis divides resilience into six types. (2) Although forest vegetation shows a clear greening trend, resilience does not necessarily increase with greening, and in some areas, an “increase in greening—decline in resilience” asynchronous pattern appears. (3) The annual average temperature, precipitation, and solar radiation are the main climate factors and their influence on resilience follows a nonlinear relationship. Higher temperatures and increased radiation may suppress resilience, while increased precipitation can enhance it. This study suggests incorporating the TAC indicator into ecological monitoring and early warning systems, along with applying trend classification results for region-specific management to improve the scientific basis and adaptability of forest governance under climate change. Full article

The efficiency of solar water heating systems is strongly influenced by variable weather conditions, making the optimization of control strategies essential for maximizing energy performance. This study presents the development and evaluation of a rule-based adaptive control strategy that dynamically selects one of three predefined control modes—ON–OFF, proportional, or indirect proportional control (IPC)—based on real-time weather classification. The classification algorithm assigns each day to one of four solar irradiance categories, enabling the controller to respond appropriately to current environmental conditions. The proposed adaptive controller was implemented and tested under real operating conditions and compared with a conventional commercial solar controller. Over a 40-day testing period, the adaptive system achieved a 12.7% increase in thermal energy storage efficiency. Specifically, despite receiving 4.8% less solar radiation (719 kWh vs. 755 kWh), the adaptive controller stored 453 kWh of heat in the water tank compared to 416 kWh with the traditional system. This corresponds to an efficiency improvement from 0.55 to 0.63. These results demonstrate the adaptive controller’s superior ability to utilize available solar energy across all weather scenarios. The findings confirm that intelligent control strategies not only enhance technical performance but also improve the economic and environmental value of solar thermal systems. Full article

Clouds modulate the net radiative flux that interacts with both shortwave (SW) and longwave (LW) radiation, but the uncertainties regarding their effect in polar regions are especially high because ground observations are lacking and evaluation through satellites is made difficult by high surface reflectance. In this work, sky conditions for six different polar stations, two in the Arctic (Ny-Ålesund and Utqiagvik [formerly Barrow]) and four in Antarctica (Neumayer, Syowa, South Pole, and Dome C) will be presented, considering the decade between 2010 and 2020. Measurements of broadband SW and LW radiation components (both downwelling and upwelling) are collected within the frame of the Baseline Surface Radiation Network (BSRN). Sky conditions—categorized as clear sky, cloudy, or overcast—were determined using cloud fraction estimates obtained through the RADFLUX method, which integrates shortwave (SW) and longwave (LW) radiative fluxes. RADFLUX was applied with daily fitting for all BSRN stations, producing two cloud fraction values: one derived from shortwave downward (SWD) measurements and the other from longwave downward (LWD) measurements. The variation in cloud fraction used to classify conditions from clear sky to overcast appeared consistent and reasonable when compared to seasonal changes in shortwave downward (SWD) and diffuse radiation (DIF), as well as longwave downward (LWD) and longwave upward (LWU) fluxes. These classifications served as labels for a machine learning-based classification task. Three algorithms were evaluated: Random Forest, K-Nearest Neighbors (KNN), and XGBoost. Input features include downward LW radiation, solar zenith angle, surface air temperature ($T_a$), relative humidity, and the ratio of water vapor pressure to $T_a$. Among these models, XGBoost achieved the highest balanced accuracy, with the best scores of 0.78 at Ny-Ålesund (Arctic) and 0.78 at Syowa (Antarctica). The evaluation employed a leave-one-year-out approach to ensure robust temporal validation. Finally, the results from cross-station models highlighted the need for deeper investigation, particularly through clustering stations with similar environmental and climatic characteristics to improve generalization and transferability across locations. Additionally, the use of feature normalization strategies proved effective in reducing inter-station variability and promoting more stable model performance across diverse settings. Full article

Machine learning (ML) models are widely used to analyze the spatiotemporal impacts of agricultural practices on environmental sustainability, including the contribution to global greenhouse gas (GHG) emissions. Management practices, such as organic amendment applications, are critical pillars of Climate-smart agriculture (CSA) strategies that mitigate GHG emissions while maintaining adequate crop yields. This study investigated the critical threshold of soil moisture level associated with soil CO₂ emissions from organically amended plots using the classification and regression tree (CART) algorithm. Also, the study predicted the short-term soil CO₂ emissions from organically amended systems using soil moisture and weather variables (i.e., air temperature, relative humidity, and solar radiation) using multilinear regression (MLR) and generalized additive models (GAMs). The different organic amendments considered in this study are biochar (2268 and 4536 kg ha⁻¹) and chicken and dairy manure (0, 224, and 448 kg N/ha) under a sweet corn crop in the greater Houston area, Texas. The results of the CART analysis indicated a direct link between soil moisture level and the magnitude of CO₂ flux emission from the amended plots. A threshold of 0.103 m³m⁻³ was calculated for treatment amended by biochar level I (2268 kg ha⁻¹) and chicken manure at the N recommended rate (CXBX), indicating that if the soil moisture is less than the 0.103 m³m⁻³ threshold, then the median soil CO₂ emission is 142 kg ha⁻¹ d⁻¹. Furthermore, applying biochar at a rate of 4536 kg ha⁻¹ reduced the soil CO₂ emissions by 14.5% compared to the control plots. Additionally, the results demonstrate that GAMs outperformed MLR, exhibiting the highest performance under the combined effect of chicken and biochar. We conclude that quantifying soil moisture thresholds will provide valuable information for the sustainable mitigation of soil CO₂ emissions. Full article

Urban forests are very important for the environment and for people, especially in semi-arid cities where there is not much greenery. This makes heat stress worse and makes the city less livable. This paper presents a comprehensive geospatial methodology for selecting afforestation sites in the expanding semi-arid urban area of Şanlıurfa, Turkey, characterized by minimal forest cover, rapid urbanization, and extreme weather conditions. We identified nine ecological and infrastructure criteria using high-resolution Sentinel-2 images and features from the terrain. These criteria include slope, aspect, topography, land surface temperature (LST), solar radiation, flow accumulation, land cover, and proximity to roads and homes. After being normalized to make sure they were ecologically relevant and consistent, all of the datasets were put together into a GIS-based Multi-Criteria Decision Analysis (MCDA) tool. The Analytic Hierarchy Process (AHP) was then used to weight the criteria. A deep learning-based semantic segmentation model was used to create a thorough classification of land cover, primarily to exclude unsuitable areas such as dense urban fabric and water bodies. The final afforestation suitability map showed that 151.33 km² was very suitable and 192.06 km² was suitable, mostly in the northeastern and southeastern urban fringes. This was because the terrain and subclimatic conditions were good. The proposed methodology illustrates that urban green infrastructure planning can be effectively directed within climate adaptation frameworks through the integration of remote sensing and spatial decision-support tools, especially in ecologically sensitive and rapidly urbanizing areas. Full article

Artificial intelligence (AI) has emerged as a transformative tool for optimizing photovoltaic (PV) systems, enhancing energy efficiency, predictive maintenance, and fault detection. This study presents a systematic literature review and bibliometric analysis to identify the most commonly used AI techniques and their applications in PV systems. The review provides details on the advantages, limitations, and optimal use cases of various review techniques, such as Artificial Neural Networks, Fuzzy Logic, Convolutional Neural Networks, Long-Short Term Memory, Support Vector Machines, Decision Trees, Random Forest, k-Nearest Neighbors, and Particle Swarm Optimization. The findings highlight that maximum power point tracking (MPPT) optimization is the most widely researched AI application, followed by solar power forecasting, parameter estimation, fault detection and classification, and solar radiation forecasting. The bibliometric analysis reveals a growing trend in AI-PV research from 2018 to 2024, with China, the United States, and European countries leading in contributions. Furthermore, a type-2 fuzzy logic system is developed in MATLAB R2023b for automating AI technique selection based on the problem type, offering a practical tool for researchers, industry professionals, and policymakers. The study also discusses the practical implications of adopting AI in PV systems and provides future directions for research. This work serves as a comprehensive reference for advancing AI-driven solar PV technologies, contributing to a more efficient, reliable, and sustainable energy future. Full article

With the intensification of global climate change, buildings in hot climate zones face increasing challenges related to high energy consumption and thermal comfort. Building integrated photovoltaic (BIPV) façades, which combine power generation and energy saving potential, require further optimization in their climate-adaptive design. Most existing studies primarily focus on the photoelectric conversion efficiency of PV modules, yet there is a lack of systematic analysis of the coupled effects of temperature, humidity, and solar radiation intensity on PV performance. Moreover, the current literature rarely addresses the regional material degradation patterns, integrated cooling solutions, or intelligent control systems suitable for hot and humid climates. There is also a lack of practical, climate specific design guidelines that connect theoretical technologies with real world applications. This paper systematically reviews BIPV façade design strategies following a climate zoning framework, summarizing research progress from 2019 to 2025 in the areas of material innovation, thermal management, light regulation strategies, and parametric design. A climate responsive strategy is proposed to address the distinct challenges of humid hot and dry hot climates. Finally, this study discusses the barriers and challenges of BIPV system applications in hot climates and highlights future research directions. Unlike previous reviews, this paper offers a multi-dimensional synthesis that integrates climatic classification, material suitability, passive and active cooling strategies, and intelligent optimization technologies. It further provides regionally differentiated recommendations for façade design and outlines a unified framework to guide future research and practical deployment of BIPV systems in hot climates. Full article

This research aims to estimate long-term aerosol radiative effects by combining radiation and Aerosol Optical Depth (AOD) observations in Barcelona, Spain. Aerosol Radiative Forcing and Aerosol Forcing Efficiency (ARF and AFE) were estimated by combining shortwave radiation measurements from a SolRad-Net CM-21 pyranometer (level 1.5) and AERONET AOD (level 2), using the direct method. The shortwave AFE was derived from the slope between net solar radiation and AOD at 440, 675, 879, and 1020 nm, and the ARF was computed by multiplying the AFE by AOD at six solar zenith angles (20°, 30°, 40°, 50°, 60°, and 70°). Clear-sky conditions were selected from all-skies days by a quadratic fitting. The aerosol was classified to investigate the forcing contributions from each aerosol type. The aerosol classification was based on Pace and Toledano’s thresholds from AOD vs. Ångström Exponent (AE). The GRASP inversions were performed by combined AOD, radiation, Degree of Linear Polarization (DoLP) by zenith angles from the polarized sun–sky–lunar photometer and the elastic signal from the UPC-ACTRIS lidar system. The long-term AFE and ARF are both negative, with an increasing tendency (in absolute value) of +24% (AFE) and +40% (ARF) in 14 years. The yearly AFE varied from −331 to −10 Wm⁻²τ⁻¹, and the ARF varied from −64 to −2 Wm⁻², associated with an AOD (440 nm) from 0.016 to 0.690. The three types of aerosols on clear-sky days are mixed aerosols (61%), desert dust (10%), and urban/industrial-biomass burning aerosols (29%). Combined with Gobbi’s method, this classification clustered the aerosols into four groups by AE analysis (two coarse- and two fine-mode aerosols). Then, the contribution of the aerosol types to the ARF showed that the desert dust forcing had the largest cooling effect in Barcelona (−61.5 to −37.4 Wm⁻²), followed by urban/industrial-biomass burning aerosols (−40.4 to −20.4 Wm⁻²) and mixed aerosols (−31.8 and −24.0 Wm⁻²). Regarding the comparison among Generalized Retrieval of Atmosphere and Surface Properties (GRASP) inversions, AERONET inversions, and direct method estimations, the AFE and ARF had some differences owing to their definitions in the algorithms. The DoLP, used as GRASP input, decreased the ARF overestimation for high AOD. Full article

Efforts to address anthropogenic climate change have been focused sensibly on mitigation and adaptation. However, given the difficulties in the implementation of a rapid global mitigation process, increasing attention is being given to geoengineering as a way to countervail some of the climate change impacts. This development has increased the private investment in geoengineering research in the last few years, leading to patent filing. The article examines the recent evolution of patents in the emerging field of geoengineering technologies. Despite the secrecy surrounding the field of geoengineering, especially solar radiation management at the state level, patent databases provide transparency, offering technical details, market insights, and information about the key players. Patents, published 18 months after filing, reveal valuable data about geoengineering technologies, the targeted markets, and involved stakeholders. The databases of the International Patent Classification (IPC) and Cooperative Patent Classification (CPC) are used. The focus of the present analysis is on patents in the sub-domains of carbon dioxide removal and solar radiation management and on those held by the large oil producer corporations. The results highlight the patents filed in the controversial area of SRM. The growing economic significance of geoengineering requires the protection of innovations through patents coupled with the implementation of a global governance system based on climate justice and ethical responsibility. Full article

Vegetation is a vital component of ecosystems and an indicator of global environmental change. It is significantly influenced by climate factors. Previous studies have often overlooked the nonlinear relationships, spatiotemporal variability, and interaction effects of climate factors on vegetation, focusing instead on simplistic trends or regional classifications based on vegetation type, climate zone, or ecosystem. In this study, a factorial-clustered copula covariate analysis model was developed to investigate the effects of climate factors on vegetation cover (NDVI) in China from 2000 to 2023. The results showed that temperature had the strongest correlation with NDVI (0.66), followed by precipitation and solar radiation (both 0.46), and soil moisture (0.14). The NDVI exhibited significant spatial variability, with low values (<0.1) in 17.6% and high values (>0.8) in 12.7% of the areas. Regional variations were observed: precipitation-dominated NDVI changes in arid regions (Cluster 1, 43%), solar radiation in tropical areas (Clusters 4 and 5, >79%), and soil moisture in humid zones (Cluster 2, 29%). Interaction effects, such as Pre:Temp and Pre:Temp:SM, further influenced NDVI dynamics. Joint probability analysis revealed diverse dependency patterns across clusters, highlighting the complex interplay between climatic and non-climatic factors. These findings emphasize the need for tailored management strategies to address region-specific vegetation dynamics under changing climatic conditions. Full article