Yazarlar
- Akkurt, Nevzat(10)
- Gul, Muhammet(7)
- Işık, Erdem(7)
- Yucesan, Melih(6)
- Acar, Ramazan(5)
- Konurhan, Zekeriya(5)
- Bayrakdar, Cihan(4)
- Kahraman, Gökhan(4)
- Keserci, Ferhat(4)
- Tuğan, Volkan(4)
- Utku, Anıl(4)
- Çılğın, Zeynel(4)
- İnallı, Mustafa(4)
- Göçmen, Elifcan(3)
- Korkmaz, Meral(3)
- Utku, Anil(3)
- Akbas, Ergun(2)
- Aksağan, Zülfükar(2)
- Akyol, Sinem(2)
- Bjarnadottir, Brynhildur(2)
- Black, Andrew(2)
- Celik, Erkan(2)
- Demir, Yakup(2)
- Derse, Onur(2)
- Erdem, Necla Seval(2)
- Evans, Ian S.(2)
- Geçkil, Tacettin(2)
- Gunnarsdottir, Gunnhildur E.(2)
- Güçlü, Yavuz Selim(2)
- Hudaverdi, Burcu(2)
- Imak, Andaç(2)
- Kjartansson, Bjarki T.(2)
- Koycegiz, Cihangir(2)
- Oddsdottir, Edda S.(2)
- Oskarsson, Hlynur(2)
- Saplıoğlu, Kemal(2)
- Sarı, Sacit(2)
- Shao, Shuangquan(2)
- Sigurdsson, Bjarni D.(2)
- Susam, Selim Orhun(2)
Yayınlar
- MakaleAçık Erişim2026İklim Krizi Çağında Nükleer Caydırıcılık: Stratejik İstikrar ve Kriz Dinamikleri
Durmuş, İrfan; Özbay, Sadi Özbay; Özek, Nesrin
Bu çalışma, iklim krizi ile nükleer caydırıcılık arasındaki ilişkinin mevcut literatürde hangi mekanizmalar üzerinden kurulduğunu ve hangi analitik boşlukların sürdüğünü incelemektedir. Makale, Web of Science, Scopus, TR Dizin ve DergiPark’ta 2003–2026 dönemini kapsayan Türkçe ve İngilizce yayınları temel alan şeffaf bir yarı-sistematik literatür taramasına dayanmaktadır. Nihai sentez korpusu 33 çekirdek çalışma ile kavramsal omurgayı kuran 10 kurucu/çerçeve metinden oluşmakta ve kaynaklar tür, rol ve kanıt ağırlığına göre sınıflandırılmaktadır. İnceleme, literatürün dört tema etrafında toplandığını göstermektedir: (1) nükleer altyapının iklim kaynaklı kırılganlıkları; (2) kriz yönetimi, komuta-kontrol ve yanlış hesaplama riskleri; (3) bütçe öncelikleri, jeopolitik rekabet ve yayılma dinamikleri; (4) nükleer savaşın iklimsel sonuçları ile adalet tartışmaları. Çalışma, iklim krizinin nükleer caydırıcılığı yalnızca üsler ve tesisler üzerinde yarattığı doğrudan etkilerle değil, stratejik çevreyi daha oynak, daha maliyetli ve daha siyasal hale getirerek dönüştürdüğü sonucuna ulaşmaktadır. Bulgular ayrıca literatürün altyapı odaklı, Batı merkezli ve ikinci dereceden siyasal etkiler bakımından sınırlı kaldığını göstermektedir.
- MakaleAçık Erişim2026Scopus SJRQ1Upcycling Agricultural Biomass into Functional Bio-Oils through Pyrolysis: Toward Sustainable Rejuvenation of Asphalt Binder
Furtana Yalcin, Beyza; Yilmaz, Mehmet; Yalçin, Erkut; Vural Kok, Baha
This study investigates the feasibility of utilizing bio-oils derived from agricultural waste materials (such as straw, olive pomace, and pine cones) as rejuvenating additives in reclaimed asphalt pavement (RAP) containing bituminous mixtures, offering a sustainable and environmentally friendly approach in asphalt technology. The primary objective of the study is to enhance the physical and mechanical properties of aged binders using ecofriendly alternatives, thereby developing high-performance asphalt mixtures without harming the environment. The bio-oils, produced through pyrolysis, were evaluated in comparison with two commercial rejuvenators. Key performance criteria, including Marshall stability, moisture damage resistance (TSR), indirect tensile stiffness modulus (ITSM), and fatigue life, were tested. The results indicate that natural rejuvenators not only soften the aged binder but also effectively balance the flexibility, strength, and deformation resistance of the mixture. Notably, straw oil exhibited superior performance in terms of both high stability and extended fatigue life, while olive pomace oil demonstrated remarkable success in moisture resistance. Although commercial additives were effective to a certain extent, they failed to achieve the same level of structural compatibility and sustainability provided by natural bio-oils. In this regard, the study provides strong evidence that agricultural biomass waste can serve as a value-added solution in asphalt pavements, in alignment with the principles of the circular economy. In conclusion revealed that mixtures containing 75% RAP rejuvenated with straw oil achieved 11% higher Marshall stability and up to 14.5 times longer fatigue life compared to virgin mixtures, while olive pomace oil-based mixtures reached a TSR value of 81.5%, demonstrating superior moisture resistance.
- MakaleYalnız künye2026Scopus SJRQ2An Investigation of the Variability of Short-Term Drought with SPEI-Based Severity, Duration and Magnitude Parameters in a Transboundary Basin
Akbas, Ergun; Acar, Ramazan; Eşit, Musa; Koycegiz, Cihangir
Climate change-induced variability in the water budget leads to significant drought events. In this study, severity, duration, and magnitude parameters obtained from 1-month and 3-month series of the Standardized Precipitation Evapotranspiration Index (SPEI) were used to investigate short-term drought from different perspectives. The lower part of the Euphrates-Tigris Basin, which is a transboundary basin, was preferred as the study area within the borders of Türkiye. In this study, monthly total precipitation and monthly mean temperature data for the period 1963–2021 were used. Both graphical (Şen Innovative Trend Analysis) and statistical (Mann–Kendall, Spearman’s Rho, Sen’s Slope Estimator) trend analysis methods were applied to detect, categorize, and analyze the variability of drought. The Combination of Wilcoxon Test and Scatter Diagram (CWTSD) method, which offers both graphical and statistical trend analysis capabilities, was preferred to enhance consistency and facilitate comparison of results. According to the findings, more severe droughts are observed in the eastern part of the basin compared to the western part. Across the basin, severity and magnitude are decreasing, while duration is increasing. The increase in duration indicates that droughts are shifting from short-term to long-term periods. This situation is of great importance in terms of planning agricultural strategies.
AtıfOpenAlex3
İncele - MakaleYalnız künye2026Scopus SJRQ1From trash to technology: Ai-driven waste classification with hybrid CNN-transformer approach for sustainable recycling
Utku, Anil
The increasing amount of waste globally requires more efficient recycling processes and increases the importance of intelligent, automatic classification systems. Since traditional waste separation methods are time-consuming and have high error rates, artificial intelligence-based solutions have great potential in this area. In this study, the effectiveness of deep learning-based waste classification models was investigated, and nine different waste types were classified using DenseNet121, EfficientNetB0, ResNet50, ViT, DenseNet121-ViT, EfficientNetB0-ViT, ResNet50-ViT. Experimental results show that hybrid CNN-ViT models achieved the highest accuracy rates. Mainly, the EfficientNetB0-ViT model exhibited the best performance with a 95% accuracy rate and made a balanced classification for all waste types. The model can optimize recycling processes by ensuring the correct separation of wastes and contribute to environmental sustainability by reducing misclassification. Artificial intelligence-supported waste management systems offer an effective solution in critical areas such as reducing greenhouse gas emissions, saving energy, and supporting environmentally friendly urban planning. The findings of this study reveal that deep learning-based artificial intelligence models are an important tool for sustainable environmental management.
AtıfScopus1WoS1OpenAlex3İncele - MakaleAçık Erişim2026Scopus SJRQ2The Relationship Between Agricultural Carbon Dioxide Emission and Agriculture Subsectors Production: Static Panel Data Approach
Peker, Ayşe Esra; Akdağ, Nuran; Veysikarani, Dilek; Kaya, Mustafa Göktuğ; Kaya, Perihan Hazel
Human activities contribute to carbon dioxide (CO₂) emissions, which have serious environmental, economic, and social effects of climate change. This has sparked efforts to find large-scale solutions to eliminate the causes of climate change. While these solutions mainly involve reducing carbon emissions through global agreements, individual nations have also taken steps to cut emissions by changing production methods, such as irrigation and fertilisation, to ensure the efficient and effective use of resources and the proper storage and transportation of food. As a result, the agricultural sector is both a source and a victim of this process. The sector’s environmental impact varies due to differing production techniques and energy needs across various sub-sectors like vegetable, grain, and fruit production. Increases in carbon dioxide emissions in this sector are connected not only to production activities but also to demographic and social factors, such as the rural population proportion. This study aims to explore the effect of vegetable, grain, and fruit production, as well as the rural population ratio, on carbon dioxide emissions. To achieve this, a static panel data analysis was conducted across 21 European countries from 1970 to 2023. According to the research findings, vegetable production increases carbon emissions from agriculture. However, it was found to have a significant, albeit borderline, impact on fruit production and to reduce carbon emissions. These results demonstrate that agriculture's contribution to carbon dioxide emissions varies by sub-sector and provide valuable information for developing sustainable agricultural policies.
- MakaleAçık Erişim2026Scopus SJRQ1Sustainable material pathways: Utilizing biomass-derived bio-oils from agricultural wastes to restore aged asphalt binder properties
Yalcin, Beyza Furtana; Yilmaz, Mehmet; Yalcin, Erkut; Kok, Baha Vural
This study explores the valorization of agricultural biomass wastes into high-value bio-based materials by evaluating the performance of pyrolysis-derived bio-oils obtained from straw, pine cone, and olive pomace for the rejuvenation of aged asphalt binders. The effectiveness of these biomass-derived bio-oils was comparatively assessed against two commercial rejuvenators to enhance binder reusability using renewable resources. The optimum rejuvenator dosages were determined through penetration, viscosity, and dynamic shear rheometer (DSR) tests, while the associated physical and chemical modifications were investigated using FTIR, SEM-EDS, and elemental analyses. Penetration and viscosity results indicated that all rejuvenators significantly reduced binder stiffness and improved fluidity. DSR analysis indicated that increasing rejuvenator content reduced G∗/sin δ values, reflecting a decrease in binder stiffness and a softening effect of the rejuvenators, which contributes to restoring the viscoelastic balance of the aged binder. FTIR results demonstrated that biomass-derived bio-oils, particularly those from pine cone and straw, effectively reduced carbonyl (C=O) and sulfoxide (S=O) functional groups, indicating strong chemical rejuvenation. SEM-EDS analyses showed that the heterogeneous and porous morphology of bio-oils promoted improved interaction with the asphalt binder. Based on the combined results of the three experimental approaches, the optimum rejuvenator dosages were identified as 25% for commercial products, 35% for pine cone and olive pomace bio-oils, and 45% for straw bio-oil. Overall, the findings highlight that biomass-derived bio-oils produced via pyrolysis offer a sustainable and performance-effective pathway for converting agricultural residues into value-added materials, supporting circular bioenergy and biomass utilization strategies.
AtıfScopus2WoS3OpenAlex3İncele - MakaleAçık Erişim2026Scopus SJRQ2Synergistic Effect of Cylindrical Fins and Phase Change Material on the Thermal and Economic Performance of a Double-Slope Solar Distillation Device Under Tunceli Climatic Conditions
Tuğan, Volkan; Işık, Erdem; İnallı, Mustafa
Global water scarcity driven by population growth, climate variability, and rising freshwater demand increases the need for more efficient solar desalination systems. In this study, a double-slope solar distillation device supported by a phase change material pool was developed to improve freshwater production. The phase change material layer was placed directly beneath the absorber plate to store excess thermal energy during peak solar radiation and release it during low-radiation periods. To strengthen thermal interaction between the absorber surface and the storage medium, cylindrical fins were embedded within the phase change material pool. The development of a double-slope solar distillation device integrated with a finned phase change material pool, and the simultaneous comparison of the thermal efficiency and economic analyses of single-slope and double-slope solar distillation devices under the same climatic conditions, are the prominent features of this study. Additionally, the performance of solar distillation devices has not previously been investigated under the climatic conditions of Tunceli, which further motivates this research. The developed system reached a peak freshwater productivity of 2459.2 g/m2/day, providing a 21.4% enhancement over the conventional configuration. In addition, the thermal efficiency increased from 17.7% in the conventional unit to 21.4% in the phase change material-assisted design. The economic analysis showed that the cost of distilled water was $0.097/kg for the conventional system and $0.105/kg for the proposed system, with the corresponding payback periods being approximately 11 and 12 months, respectively.
AtıfScopus2WoS2OpenAlex2İncele - MakaleYalnız künye2026Scopus SJRQ1Modeling stack voltage and remaining useful life of proton exchange membrane fuel cells using deep neural networks
Dogan, Gurkan; Imak, Andaç
Currently, various energy conversion systems are being developed to reduce environmental impacts. Proton exchange membrane fuel cells (PEMFCs), which stand out with their high efficiency characteristics, have emerged as an important alternative. However, these advantages, along with high costs and uncertainties regarding remaining useful life (RUL) estimation, restrict their further commercialization, especially in the automotive industry. This study presents a forecasting framework using a long short-term memory (LSTM) architecture enhanced with the Bahdanau attention mechanism for predicting RUL and long-term degradation trends. The model thus exhibits a performance that can more effectively capture dependencies in time series and adaptively weight significant time steps. Furthermore, the tree-structured parzen estimator (TPE) was chosen for the appropriate selection of hyperparameters in the developed forecasting framework. The validity, stability, and performance of the proposed model under different operating conditions were examined on the FC1 and FC2 datasets. Experimental studies have shown that the proposed method is the most successful among the best methods, achieving RE metric values of 1.628 × 10−3, 0.003 × 10−1, 99.16 × 10−2, and 0.044 × 10−1 respectively in estimating the best RMSE, MAPE, R2, and RUL.
- MakaleAçık Erişim2026Scopus SJRQ1Quantifying agricultural resilience under climate variability: a data-driven climate resilience index for European cereal systems
Utku, Anil; Radulescu, Magdalena; Barut, Abdulkadir; Alofaysan, Hind
Climate variability and extreme weather events pose an increasingly serious threat to agricultural productivity in Europe. Although many studies have focused on the mean yield response to climate change, relatively few have attempted to offer a comprehensive, multi-dimensional view of agricultural resilience, separating long-term productivity trends from short-term climate-induced fluctuations. In this paper, a data-driven Climate Resilience Index is proposed to assess the resilience of European cereal systems under climate stress. Annual yield time series are decomposed into trend and anomaly components to identify interannual deviations driven by climate variability. Machine learning and deep learning algorithms, including RF, CatBoost, CNN, LSTM, TCN, and a TCN-LSTM hybrid model, are used to assess the predictive validity of climate extreme indices and persistence patterns. Among the evaluated models, the TCN-LSTM architecture achieved the highest predictive performance with 0.8347 R2, outperforming standalone temporal and ensemble-based models. The PCA-based exposure framework explained 68.4% of the total climate variability within the first principal component, indicating a highly concentrated climate-stress structure. Feature importance analyses further revealed that lagged yield anomalies and rolling volatility indicators were the dominant predictors of resilience dynamics. The findings show that temporal models perform better than traditional ensemble methods, emphasizing the significance of multi-year recovery mechanisms in anomaly evolution. To provide a consistent measure of exposure, principal component analysis is used to reduce correlated climate indicators into a multi-dimensional climate stress index. The CRI captures these three essential elements: climate exposure, yield variability, and recovery. The results at the country level show substantial variability across Europe, indicating that while exposure intensity is important, recovery dynamics are also critical to understanding resilience. The results also show that there are structurally distinct resilience profiles, and robustness tests indicate that country rankings are not sensitive to different weights and recovery functions. The results clearly show that recovery and adaptive capacity are critical to agricultural sustainability amid growing climate uncertainty. The CRI developed in this paper provides a clear, empirically valid framework for comparing resilience in agricultural systems.
AtıfScopus1OpenAlex2
İncele - MakaleYayıncıda açık erişim2026Scopus SJRQ1Explainable AI-based deep learning architectures for hydrogen prediction in biomass syngas and photocatalysis processes: Towards a sustainable and efficient energy economy
Imak, Andaç
This study, focusing on sustainable energy production and particularly the hydrogen (H2) economy, presents an advanced artificial intelligence (AI) framework for accurately predicting H2 production from sucrose photocatalysis catalyzed by LaFeO3 (GLFO) and the H2 concentration in syngas derived from biomass conversion. The research was conducted along two main directions: (i) deep learning (DL) architectures comprising nine specially designed recurrent neural network-based models and (ii) six conventional machine learning (ML) algorithms. For both H2 production systems, the best-performing models were identified and further interpreted using explainable AI (XAI) based on SHapley Additive exPlanations (SHAP). Among all investigated algorithms, a hybrid One-dimensional Convolutional Neural Network–Bidirectional Long Short-Term Memory–Bidirectional Gated Recurrent Unit (1D-CNN–BiLSTM–BiGRU) model demonstrated the highest predictive performance. Using 10-fold cross-validation, the proposed model achieved outstanding accuracy, with an R2 value of 0.9987 ± 0.0011 for photocatalytic H2 production prediction and 0.9980 ± 0.0006 for syngas H2 concentration prediction. These results highlight the potential of the proposed framework as a precise, reliable, and practical methodological approach for modeling and optimizing complex thermochemical energy systems.
- MakaleYalnız künye2026Scopus SJRQ1Enhancing durability of slag-based alkali-activated composites with dunite sand and carbon fiber: Sulfate, freeze–thaw, and thermal resistance optimization
Benli, Ahmet; Bürlükkara, Sadullah; Öz, Ali; Karataş, Mehmet; Kaplan, Gökhan
The replacement of natural river sand with sustainable alternatives is essential to reduce environmental degradation associated with aggregate extraction. In this context, dunite sand (DS), a magnesium-rich ultramafic mineral, was investigated as an eco-efficient fine aggregate in thermally cured slag-based alkali-activated composites (AACs), in combination with carbon fiber (CF) reinforcement. The study aims to clarify how aggregate substitution and fiber addition interact to control matrix densification, crack-bridging efficiency, and durability under aggressive conditions. Sixteen mixtures incorporating varying DS (0–100%) and CF (0–1.00%) contents were prepared and cured at 80°C to promote rapid slag activation and microstructural development. Mechanical performance, high-temperature resistance, sulfate durability, and freeze–thaw behavior were systematically evaluated. The results demonstrate that moderate DS substitution (~25%) significantly improves matrix compactness and interfacial quality through a micro-filler effect, leading to enhanced strength and durability. CF effectively bridge micro cracks and improve flexural and thermal performance; however, excessive fiber contents impair workability and increase porosity due to fiber clustering. Durability results confirm that optimized DS–CF combinations reduce thermal degradation, limit sulfate-induced deterioration, and enhance resistance to cyclic freezing. Overall, 25% DS replacement combined with limited CF content (≤0.50%) provides the best balance between mechanical performance and durability. The findings highlight the importance of controlling aggregate–fiber synergy and curing strategy to develop durable, thermally stable, and sustainable alkali-activated composites for harsh service environments.
AtıfScopus5WoS4OpenAlex4İncele - MakaleAçık Erişim2026Scopus SJRQ1Analyzing Failures In Wind–Solar Hybrid Energy Systems Using a Fuzzy-Based BWM-MARCOS Approach: Challenges and Solutions
Başhan, Veysi; Yucesan, Melih; Demirel, Hakan; Gul, Muhammet
The reliability of hybrid renewable energy systems (HRES) depends heavily on the identification and management of potential failure modes. This study employs a fuzzy-based BWM-MARCOS approach to systematically analyze and prioritize failure modes within wind–solar hybrid systems. The model aims to prioritize the failures considering four important risk parameters: (1) severity of the failure on system, staff, and failure, (2) failure occurrence chance, (3) effort and ease of detecting the cause of the failure, and (4) economic impact of the failure. In this context, four key risk indicators were evaluated to rank failures, revealing that SP1 (cell damage), ESS1 (battery degradation), and WT11 (battery fire) are the most critical due to their potential impact on system operations. Sensitivity analysis confirmed the stability of these rankings under varying parameter weights. Additionally, cross-method validation using fuzzy TOPSIS, SAW, and MARCOS demonstrated high correlation coefficients, underscoring the reliability of the results. Tailored mitigation strategies, including advanced diagnostics, durable materials, and robust monitoring systems, are proposed to address these critical failures. While the current methodology applies to various HRES configurations, future research should incorporate real-world operational data and machine learning techniques to enhance predictive capabilities and dynamic risk management.
AtıfScopus7WoS7OpenAlex8İncele - MakaleAçık Erişim2026Scopus SJRQ2Risk Assessment via Hazard Scenario in Cereal Production Under Rising CO
Susam, Selim Orhun; Peker, Ayse Esra; Hudaverdi, Burcu
Global food security has been seriously threatened by air pollution and climate change. The impact of CO on crop output is complicated because of its interactions with temperature, nutrient availability, and other pollutants, even though it is necessary for photosynthesis. This study develops a copula-based hazard modeling framework to assess how increasing CO concentrations affect cereal production risk across multiple crops. By integrating hazard scenario analysis with localized dependence measures, we estimate both failure probabilities and dynamic associations between CO emissions and cereal yields. A novel localized version of Kendall’s implemented via a Bernstein copula approximation, enables flexible modeling of heterogeneous and nonlinear dependence structures. As the relationship between CO emissions and crop-specific yields changes in both density and direction, it may indicate asymmetries or nonlinearities that are missed by measures of global dependence. Empirical results reveal distinct crop-specific responses. While CO levels enhance yields for rice, millet, and sugar beet, they reduce or destabilize production for rye and lentil, indicating asymmetric sensitivities. Failure probabilities increase with the time horizon, reflecting the accumulation of climate-related production risk. The findings emphasize the significance of integrating probabilistic hazard modeling into climate-resilient agricultural planning and food security policies, as crop-specific risk exposure has increased in recent time periods.
AtıfWoS1OpenAlex1İncele - Derleme MakaleYalnız künye2026Scopus SJRQ1Assessing the Impact of Climate Change on the Sustainability of Sorghum Production: A Bibliometric Analysis
Zheng, Mingke; Tutar, Halit; Shaghaleh, Hiba; Alhaj Hamoud, Yousef; Er, Hasan; Omac, Basri; Yildirim, Gokhan; Eren, Ömer; Khalifa, Muhammad; Mwamahonje, Andekelile
Sorghum is a crucial crop for sustainable agriculture due to its drought resistance, low carbon footprint, and diverse applications in food, feed, and bioenergy. However, understanding the impact of climate change on sorghum production is essential for ensuring its future sustainability. This study conducts a comprehensive bibliometric analysis of scientific publications related to sorghum and climate change using the Web of Science (WoS) database, covering the period from 1984 to 2024. A total of 1053 relevant papers were analyzed, revealing an average annual growth rate of 12.73% and an average citation count of 32.02 per document. Most of the publications (83.5%) were research articles, while 9.7% were review papers. Among the contributing researchers, Prasad PVV emerged as the most productive author based on an h-index of 13, whereas the United States was identified as the most influential country, contributing 235 documents and 13,750 citations. Keyword clustering analysis identified five major research themes, including sustainable energy and environmental impacts, climate resilience, socioeconomic effects of climate change, and key crops. The findings of this study offer valuable scientific insights to guide future research and policymaking on climate change and sorghum sustainability.
AtıfScopus4OpenAlex3İncele - MakaleAçık Erişim2026Scopus SJRQ2Hybrid Supervised Classification and Deep Embedding–Based Profiling Framework for Electricity Consumption Analysis
Gunay, Mihriban; Yildirim, Ozal; Demir, Yakup; Zhilevski, Marin; Mikhov, Mikho; Yordanov, Nikolay
This study proposes a hybrid deep learning framework that integrates supervised classification and unsupervised profiling for electricity consumption analysis. In the supervised phase, a one-dimensional Convolutional Neural Network combined with Long Short-Term Memory (1D CNN–LSTM) architecture is developed to classify daily load patterns. The performance of the proposed model is compared with traditional machine learning and deep learning approaches, including Support Vector Machine (SVM), k-Nearest Neighbors (KNN), a standalone Long Short-Term Memory (LSTM) model, a Transformer-based model, and a standalone 1D CNN model. Experimental results on the Precon house dataset and the CU-BEMS dataset demonstrate that the proposed hybrid architecture outperforms the benchmark models, achieving classification accuracies of 87.59% and 86.40%, respectively. In the unsupervised phase, the trained CNN–LSTM encoder is utilized as a deep feature extractor. The resulting 32-dimensional latent embeddings are clustered using K-Means, Gaussian Mixture Model (GMM), Agglomerative, Spectral, and Ensemble methods. Clustering robustness is evaluated through bootstrap-based stability analysis using the Adjusted Rand Index (ARI) and the Normalized Mutual Information (NMI). The results demonstrate stable and interpretable electricity consumption profiles, particularly in the residential dataset, where near-perfect clustering stability is observed for K-Means. The proposed framework provides both improved classification performance and robust consumption profiling based on deep embedding, offering a practical tool for energy management.
AtıfScopus1OpenAlex1İncele - MakaleAçık Erişim2026Scopus SJRQ1Numerical analysis of PCM integrated in solar dryers for thermal efficiency
Bakır Gür, Eda; Işık, Erdem; Uçar, Aynur
Solar dryers using phase change materials buffer heat, yet the combined influence of airflow regime and PCM placement on melting remains underexplored. A controlled like-for-like study fixed geometry and PCM properties while varying placement and operating conditions. A 3D transient CFD model simulated a forced-convection dryer with RT28HC over Re = 3000–9000 and inlet temperatures Ti = 50–60 °C. Raising either Re or Ti shortened melt time, and three-wall coverage melted faster and more uniformly than a rear-wall-only layout. For three walls, melt time dropped from 13 h (50 °C, Re = 3000) to 7 h (60 °C, Re = 9000) (−46 %) for the rear wall it fell from 19 h to 11 h (−42 %). A 10 °C increase in Ti reduced overall melt time by 4 h across Re. Practically, three wall PCM with Re ≥ 6000 at Ti = 60 °C achieved reliably 7–9 h melting, enabling late afternoon heat release without auxiliary heating.
AtıfScopus17WoS16OpenAlex12İncele - MakaleAçık Erişim2026Scopus SJRQ2Data-Driven Prediction of Climate Variables in Agricultural Cities of India With Hybrid GA-TCN-LSTM Model
Utku, Anil
Accurate prediction of climate variables is important in reducing the effects of natural disasters and taking preventive measures for agriculture and food security, energy sector, public health, and water resources management. For agricultural production, it is essential for producers to determine strategies for their agricultural activities and to increase agricultural productivity. Issues such as determining planting and harvest times, determining pesticides to be used against agricultural pests, selecting products to be planted, irrigation, and sustainable agriculture are directly related to climate variables. In this study, a GA-TCN-LSTM hybrid prediction model was created to determine climate variables such as relative humidity, temperature, wind speed, and dew point in Bhopal, Indore, Kanpur, Ludhiana, and Patna, which are among the prominent cities of India in terms of agricultural production. The hyper-parameters of the developed model using the temporal convolutional networks (TCN) and long short-term memory (LSTM) were optimized with the genetic algorithm (GA), and the GA-TCN-LSTM hybrid model was created. GA-TCN-LSTM was extensively compared with the base TCN-LSTM, convolutional neural network (CNN), LSTM, TCN, and CNN-LSTM. The compared models were tested using approximately 15 years of hourly, up-to-date, and real-time data of the cities. Experiments showed that GA-TCN-LSTM outperformed the compared models and had above 0.9 R-Squared (R2) for the majority of cities and climate variables.
AtıfScopus2WoS1OpenAlex2İncele - MakaleYalnız künye2026Scopus SJRQ2Experimental ınvestigation of hybrid passive cooling strategies for photovoltaic modules using nano-enhanced PCM and graphene-coated finned structures under hot climate conditions
Özdemir, Osman; Yücel, Halit Lutfi; Kahraman, Gökhan
This study experimentally investigates the use of several passive cooling techniques to enhance the electrical performance of photovoltaic (PV) modules under high ambient summer temperatures in Elazığ, Turkey. Throughout the trial period of July, August, and September, a system comprising four photovoltaic panels, one designated as a reference, was developed. The first passive cooling arrangement utilized a paraffin-based phase change material (PCM) RT42HC. In the second configuration, a nano-enhanced PCM (NePCM) composite was employed, prepared by dispersing 1 mass% of Al2O3 nanoparticles into the paraffin matrix and stabilizing them with 3 mass% of sodium dodecyl sulfate (SDS) to ensure homogeneous distribution. The third, hybrid configuration integrated 14 graphene-coated parabolic pin-tipped fins mounted externally onto the aluminum enclosure housing the NePCM composite. Experimental results demonstrated that the implemented passive cooling techniques markedly decreased the panel surface temperature, thereby enhancing electrical power output and photovoltaic efficiency. The hybrid graphene-coated fin-NePCM system demonstrated superior performance, achieving a maximum surface temperature reduction of 12.65 °C, a sustained electrical efficiency increase of 6–13%, and an average daily electrical energy gain of 26.23 Wh day−1 compared to the reference module. These findings demonstrate that the integrated hybrid design is an effective and reliable passive cooling method for sustaining photovoltaic system performance under hot climate conditions.
- MakaleAçık Erişim2025Investigation of the Benefits of the Floating Solar Power Plant to be Established on Keban Dam Lake using Penman, Linacre and Artificial Neural Networks Methods
Esen, Hikmet; Kurt, Savaş; Toka, UnalT Unal
In this study, it will be calculated how much water evaporation will be prevented if a Floating Solar Power Plant (FSPP) is installed on a certain part of the Keban Dam lake in Elazığ (38.41˚N, 39.14˚E), Türkiye by using the Penman and Linacre methods and the Artificial Neural Networks (ANN) method, which are widely used in evaporation calculation. Thus, it will be calculated how much energy can be produced with this saved water. Keban Hydroelectric Power Plant (HPP), examined in this study, is Türkiye's 3rd largest hydroelectric power plant with an installed power of 1.330 MWe. It is the fourth largest lake in Türkiye and the second largest dam lake after the Atatürk Dam. Keban Dam meets the electrical energy needs of 1.750.733 people in their daily lives with an average electricity production of 5.794.927,279 kWh. It has been calculated that thanks to a floating photovoltaic power plant that will cover 10% of the dam lake, a total of 31.305.282 m3 of water in normal water code will be prevented from evaporating annually, and thanks to this water, energy benefit will be provided with the power plant with an installed power of 6.624,84 MWe. In addition, it has been analyzed that 11.297.018 kWh of electricity can be produced from the dam turbines with the water prevented from evaporating thanks to FSPP. Approximately 72.39 MWe energy is needed for the Ağın, Pertek, Serince, Palu-Kovancılar, Uluova, Kuzova irrigation projects around the Keban Dam lake. It has been determined that the amount of water and energy needed for irrigation of these plains can be obtained thanks to FSPP.
- MakaleAçık Erişim2025Assessing Environmental Policy Impact through the Ecological Footprint: The Case of Türkiye
Sarı, Sacit
This study investigates the long-term effectiveness of environmental policies in Türkiye by examining the stochastic properties of the ecological footprint (EF) and its six subcomponents, carbon footprint, cropland footprint, grazing land footprint, forest products footprint, fishing grounds footprint, and built-up land footprint over the period 1961–2022. Annual per capita data obtained from the Global Footprint Network are analyzed using two-unit root testing methodologies: the conventional Augmented Dickey-Fuller (ADF) test and the more robust RALS-LM unit root test, which accounts for structural breaks and non-normal error distributions. The findings reveal that both the EF and its largest component, the CF, are stationary, suggesting that past environmental policy interventions have yielded transitory effects. Only the CRF and FGF exhibit nonstationarity, indicating that agricultural and fisheries-related policies may have induced permanent shifts in environmental outcomes. Overall, the results suggest that environmental policies in Türkiye to date have not led to long-term and permanent improvements in the ecological footprint indicator. Therefore, it is concluded that policy design should be more comprehensive, consistent, and structurally transformative to achieve environmental sustainability.
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