Sürdürülebilir Kalkınma Amaçları
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- ArticleYalnız künye2026Data-driven forecasting of photovoltaic performance with explainable artificial intelligence: Experimental validation using environmental and thermal predictors
Karakoyun, Yakup; Katipoğlu, Okan Mert; Dogan, Ahmet; Canbaz, Ali
PV systems meet energy demand, but environmental and operational factors affect their performance. This study used a three-scenario ablation framework to predict PV power from baseline meteorological and operational variables to under-panel temperature and finally to the full thermal configuration, including water temperature (a newly studied variable). Scenario 1 included time, air temperature, panel angle, relative humidity, and average wind speed; Scenario 2 added under-panel temperature; and Scenario 3 added water temperature. Direct thermal predictors were evaluated separately from meteorological inputs using this design. Four sophisticated ML algorithms, Random Forest (RF), CatBoost, Support Vector Machine (SVM), and one-dimensional Convolutional Neural Network (1D CNN), were refined by Randomized Search (RS) and evaluated using metrics. According to the first group of power prediction results under the full Scenario 3 configuration, the CatBoost-RS model demonstrated superior performance with RMSE = 10.61, MAE = 8.41, AIC = 222.22, KGE = 0.957, and R2 = 0.99. For the second group, the SVM-RS model outperformed others, achieving RMSE = 8.02, MAE = 5.50, AIC = 200.36, KGE = 0.994, and R2 = 0.992. Comparative analysis showed that CatBoost-RS was the most robust predictor, while RF exhibited proficiency in managing nonlinearity in this experimental setting. One-way ANOVA, paired t -test, Wilcoxon signed-rank test, bootstrap confidence intervals, and effect size (Cohen’s d) analyses across both groups show that optimised ensemble and kernel-based models outperform the convolutional approach in predictive performance. Experimental validation and high predictive accuracy improve PV performance prediction in the proposed method.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Machine learning-based predictive model for temperature and comfort parameters in indoor enviroment using experimantal data
Dogan, Ahmet; Kayaci, Nurullah; Bacak, Aykut
The research introduces an artificial neural network model that predicts temperature and assesses thermal comfort metrics for a cooling room, demonstrating how machine learning advancements can enhance thermal efficiency and cost-effectiveness in building design. The study utilized the Levenberg-Marquardt (LM) artificial neural network (ANN) approach to derive the average temperature and thermal comfort metrics collected under actual operating settings. The Predicted Mean Vote (PMV) and Predicted Percentage Dissatisfied (PPD)values were measured at three distinct sites and then compared to the trial findings. The model uses a dataset of 205 observations, with 143 cases used for training and 31 examples for testing and validation. The ANN model demonstrated effective training, with negligible errors in estimated error values. The mean squared error values for average temperature and thermal comfort parameters were 0.0342, 0.0376, 0.0571, 0.0029, and 0.2296. The R values for temperature measurements are 0.9947 and 0.9923, 0.9847 and 0.9437, and 0.9737, demonstrating a highly effective engineering method. The ANN model provided precise predictions for temperature and thermal comfort metrics, such as PMV and PPD in a cooling chamber, with a tolerance of ± 15 %. The LM approach, a machine learning methodology, produced excellent outcomes, particularly at lower temperatures, with 15 % of the data exceeding this range.
RDL Kalıcı Kaydıİncele - Book ChapterYalnız künye2025An In-depth Analysis of the Energy and Exergy Performance of Radiant Heating Integrated with Ground Source Heat Pumps in Building Environments
Dogan, Ahmet; Kayaci, Nurullah; Demir, Hakan
Addressing global priorities such as energy conservation, the reduction of fossil fuel consumption, and the urgent need to combat climate change is of paramount importance. One promising way to achieve these objectives is the exploration of radiant heating and cooling (RHC) systems as alternatives to conventional building climate systems. RHC systems hold the potential to make substantial contributions to both energy efficiency and the enhancement of indoor thermal comfort. RHC systems have garnered attention for their capacity to not only provide an acceptable level of thermal comfort but also their alignment with renewable energy sources. In this study, a comprehensive analysis of energy and exergy performance was undertaken in a building environment. This environment was equipped with a ground source heat pump (GSHP) system linked with radiant panels (RPs). The research was conducted within the confines of the Science and Technology Application and Research Center at YTU. To ensure the precision and reliability of the conducted analyses, a range of critical measurements were taken. These measurements encompassed parameters such as the input and output temperatures of both the ground and panel water, power consumption data for all the equipment within the heat pump (HP) system, and recordings of indoor and outdoor air temperature values. This meticulous data collection process serves as the foundation for the subsequent comprehensive assessment. Furthermore, as part of this study, measurements were carried out under various configurations of the radiant panels. The objective was to evaluate the system’s exergy destruction and overall performance across different scenarios. By comparing the outcomes of these diverse configurations, the research aims to provide valuable insights into the optimal use and efficiency of RHC systems in real-world applications.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Deep learning and adaptive boosting for hydroelectric power prediction using hydro-meteorological data: Insights and feature importance analysis
Karakoyun, Yakup; Katipoğlu, Okan Mert; Dogan, Ahmet
This study explores the use of modern artificial intelligence (AI) models such as Adaptive Boosting (AdaBoost), Autoencoder Based Regression Model (Autoencoder), Deep Neural Network (DNN), Echo State Network (ESN), Light Gradient Boosting Machine (LGBM) Residual Neural Network (ResNet) and feature importance techniques to predict energy production. Daily rainfall, streamflow, temperature and energy production data between 2016 and 2019 were used in the study. According to the feature importance analysis performed using the LGBM model, streamflow, temperature, and precipitation were identified as the most critical parameters influencing energy production. This structured AI-driven approach provided robust model evaluation and reliable performance measurements. As revealed by the analysis, the LGBM model showed the most accurate result with the values of root mean square error (RMSE: 98.27), mean absolute error (MAE: 73.93), Akaike information criterion (AIC: 17,100), Nash–Sutcliffe efficiency coefficient (NSE: 0.69), Kling–Gupta efficiency (KGE: 0.77), coefficient of determination (R2: 0.69), mean bias error (MBE: −5.23), bias factor (BF: 1.02), and percent bias (PBIAS: 1.51), while the DNN model came in with the second-best results. These findings emphasize both the effectiveness of deep learning and ensemble AI models and their practical application in improving hydroelectric power generation forecasting. Furthermore, the study underscores the importance of accurately monitoring and managing streamflow for the optimization of hydroelectric systems.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024Toprak Kaynaklı Isı Pompalı Radyant Isıtma Sisteminde Enerji ve Ekserji Analizi
Kayaci, Nurullah; Dogan, Ahmet; Demir, Hakan
Enerji tasarrufu, fosil yakıt tüketiminin azaltılması ve iklim değişikliğiyle mücade - le etme çabaları, küresel ölçekte öncelikli konular arasında yer almaktadır. Bina ısıtma ve soğutmasında, konvansiyonel sistemler yerine radyant ısıtma ve soğutma (RIS) sistemlerine odaklanmak, enerji verimliliğinin yanı sıra ısıl konfor açısından da önemli katkılar sağlayacaktır. Radyant ısıtma ve soğutma sistemleri, kabul edile- bilir bir ısıl konfor seviyesi sunabilme ve yenilenebilir enerji kaynaklarıyla uyumlu olma özellikleri sayesinde sistem verimliliğini artırmaktadır. Bu çalışmada, Yıldız Teknik Üniversitesi Bilim ve Teknoloji Uygulama ve Araştırma Merkezi bünyesinde yer alan toprak kaynaklı ısı pompası ile entegre duvar ve tavanda radyant paneller bulunan ofis odası sisteminin enerji, ekserji performans analizleri yapılmıştır. Ya- pılan analizlerin doğru bir şekilde yapılabilmesi için, toprak ve mahal su giriş-çıkış sıcaklıkları, tüm çevrimlerin debi ölçümleri, ısı pompası grubundaki tüm ekipman- ların güç tüketimleri, oda içi ve dış hava sıcaklık değerleri ölçülmüştür. Ayrıca bu çalışmada radyant panellerin farklı konfigürasyonlarında ölçümler alınarak sistem ekserji yıkımı ve performansı karşılaştırılmıştır.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024The effect of internal blinds on thermal comfort: An experimental analysis in an office room with radiant panels
Dogan, Ahmet; Kayaci, Nurullah; Demir, Hakan
This study investigated the impact of two types of internal blinds on the indoor thermal environment of a highly glazed office room in Istanbul during summer. The research examined four configurations: (i) no blinds, (ii) roller blinds, (iii) layered roller blinds in night mode and (iv) layered roller blinds in day mode. Measurements of air velocity, average radiation temperature, air temperature and relative humidity were conducted to assess thermal comfort. The findings revealed that internal blinds significantly influence thermal comfort in radiantly cooled office spaces during summer. Without blinds, the indoor environment only met ISO 7730 comfort category C, with average predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD) values of 0.6 and 12.5%, respectively. However, with internal blinds, comfort categories A and B were achieved, resulting in improved thermal comfort levels, with average PMV and PPD values ranging from 0.2 to 0.3 and 6% to 7.5%, respectively. Roller blinds, in particular, proved highly effective in enhancing thermal comfort and reducing mean radiant and indoor air temperatures in highly glazed radiant cooling offices. This demonstrated their energy-efficient potential for improving indoor thermal comfort.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2024Adaptive strategies and thermal perceptions in intermittently used congregational Spaces: A case study
Gemici, Zafer; Dogan, Ahmet
In spaces with considerable volumes that are sporadically used and typically not operating at full capacity, existing thermal comfort models face limitations. This research aims to reveal disparities between these models and survey-based comfort assessments. Comfort calculations were conducted using conventional thermal comfort models based on measured parameters and compared with survey data. Notable differences prompted the formulation of a more robust correlation, accounting for 83.4 % of the variations in Predicted Mean Votes (PMV). A regression model derived from survey data shows a high coefficient of determination (R2 = 0.84), supporting its applicability for large intermittently used spaces like mosques. Addressing limitations of existing models, this research introduces a practical and simplified approach to predicting thermal comfort, enhancing assessments in these distinctive spaces. The study also examines mosque indoor air quality dynamics. Despite sporadic ventilation, CO2 levels rose from 1500 ppm to 2500 ppm during Friday prayers, posing potential health risks. Before prayers, indoor CO2 and humidity met ASHRAE Standard 62, ISO 7730, and EN15231 values. To mitigate health concerns from elevated CO2 levels, improved ventilation systems are necessary. Comfort temperatures were only achieved during prayer periods.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2023Experimental Investigation of Mean Radiant Temperature Trends for a Ground Source Heat Pump-Integrated Radiant Wall and Ceiling Heating System
Dogan, Ahmet; Kayaci, Nurullah; Kanbur, Baris Burak; Demir, Hakan
Mean radiant temperature (MRT) is one of the six primary factors that determine thermal comfort in a given thermal environment. In this study, the average radiant temperature was determined using a calculation method based on the surrounding surface temperatures and view factors. The present study specifically investigated the use of calculated radiant temperature, compared to measured radiant temperature, for predicting the mean vote (PMV) and percentage of dissatisfied (PPD) comfort parameters. The method was validated by the experimental measurements via the black sphere thermometer at five different reference points in a test room, including radiant panels on the ceiling and walls. By using global thermometer measurements, the proposed approach achieved a high degree of compatibility and an accuracy of 0.17 °C, which was the difference between calculated and measured values. The results demonstrated the reliability of the procedure using view factors and surrounding surface temperatures to calculate the radiant temperature in the designated test room; here, a straightforward method for evaluating the thermal conditions of an office room and determining the optimal location of an air temperature sensor in PMV-controlled radiant systems was also proposed. This study contributes to the increasing field of research on thermal comfort and offers knowledge that is beneficial for the design and optimization of indoor environments.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2022An experimental comparison of radiant wall and ceiling cooling system integrated with ground source heat pump and direct expansion fan coil system in a highly glazed office room
Dogan, Ahmet; Kayaci, Nurullah; Demir, Hakan; Kemal Sevindir, Mustafa
Hydronic radiant systems, which provide a thermal comfort environment, come to the fore in different building applications due to their energy saving potential and being integrated with renewable energy systems. The aim of this study is to present a full-scale experiment to compare the thermal comfort performances of radiant ceiling, wall and wall + ceiling systems coupled ground source heat pump and direct expansion fan coil system implemented in Yildiz Technical University Science and Technology Application and Research Center, Istanbul, Turkey. Air velocity, horizontal and vertical air temperature differences, mean radiant temperature and humidity values, which are four physical parameters that can affect the thermal satisfaction of office workers for 9 different experimental conditions, are measured for fan coil and all radiant systems. The results indicate that vertical temperature differences between ankle and head level vary from 0.9 °C to 1.4 °C for both fan coil system and all radiant system configurations in different location of the test room. Acceptable overall comfort in indoor environments Class A only showed stable performance with an average of 0.12 PMV and 5.3 % PPD when ceiling + wall radiant panels were actively cooled. The result shows that the difference between room air temperature and room operating temperature varies between 0.45 °C and 0.6 °C when cooling loads are met by radiant systems. This may not make it possible to use indoor air temperature instead of indoor operating temperature in the control of radiant systems. Moreover, the thermal environment of the radiant system are more uniform than fan coil system due to low air temperature stratification, air velocity and small differences between mean radiant temperature and indoor air temperature.
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