Solar energy has a key role in producing clean and emissions-free power compare to conventional methods. However, sustainable development also requires a reliable and predictable energy source. It also needs methods to measure and predict predictable supply. The main aim of the study is to improve reliable and precise solar radiation prediction models on monthly mean daily basis using various machine learning techniques. Simple Membership Function and Fuzzy Rule Generating Technique (SMGRT), which does not require error and trial for model adjustment, is the first-choice model in this study. Experience and observations about the model will greatly reduce the volume of processing for the fuzzy SMGRT model. On the other hand, Deep Learning (DL) and Adaptive Neuro-Fuzzy Inference System (ANFIS) have become increasingly popular in understanding nonlinear data structures and solving complex problems. Therefore, DL and ANFIS were also applied to estimate solar radiation. The data set used in the study were created using sunshine duration (s), extra-terrestrial solar radiation (H₀ ), relative humidity (RH), cloudiness (C), air temperature (T) and soil temperature (ST) parameters. Estimation performance of models was evaluated by using several statistical indicators which are Mean Bias Error (MBE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Correlation Coefficient (R²). When the performances of the models were compared, it was seen that all three models obtained remarkable results. In addition, it was shown that the models performed well based on the metrics in the testing phase. The SMGRT model has slightly better performance than DL and ANFIS for different input combinations. SMGRT Model 1 (with inputs H₀, s, and T) shows the best statistical performance (MBE = 0.156, MSE = 1.878, RMSE = 1.371, and R² = 0.960) not only in SMGRT models but also in others.

The current experimental study investigates the details of the formation, structure, and development of wing tip vortices that are generated by an aerofoil of NACA0012 profile having a rectangular planform of 151 mm chord using the particle image velocimetry technique. Experiments have been carried out at different angles of attack, which affect the vortex strength, vortex size, tangential velocity, and vorticity distribution along the downstream stations at chord lengths ranging from x/ c = 0.1 to 1.6, at a fixed free-stream velocity of 212 mm/s corresponding to the Reynolds number, Re_c = 32 000 based on the chord length. The radius of the vortex core is found to vary approximately between 5 and 7.5 per cent of the chord length of the wing. The results show that the flow around the tip is dominated by the structures of multiple vortices. The maximum normalized tangential velocity increases with increasing angle of attack. The peak magnitude of the tangential velocity is ∼15 per cent of the free-stream velocity for an angle of attack of α = 4°, whereas it is ∼40 per cent of the free-stream velocity for α = 12°.

The understanding of the behavior of a wind turbine is difficult due to changes in weather conditions. To obtain the response of a wind turbine influenced by changes in both wind speed and its direction, using the meteorological station data is often preferred to using the real turbine data. Furthermore, simulated data can be easily extrapolated to varied turbine hub heights. In order to estimate the most effective power output in this study, a wind turbine simulation was developed. The simulation depends on the real meteorological data. For the purpose, three modeling techniques, namely Multi-Nonlinear Regression (MNLR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and support vector machines (SVM) were used. In SVM learning process, polynomial and radial basis kernel functions were used. Models were compared to wind turbine measurement values in the same region for similar data. MNLR was used to determine quantify the strength of the relationship between parameters and to eliminate the ineffective parameters. Efficient parameters preferred for training and testing phases of the SVM and ANFIS. The Subtractive Clustering and Grid Partitioning methods were used to identify the inference parameters of ANFIS. According to performance evaluations, MNLR-ANFIS modeling based on Subtractive Clustering gave better results than Grid Partitioning. The results showed that proposed collaborative model could be applied to wind power estimation problems.

Concentrated solar power technology offers an effective pathway for large-scale renewable electricity generation in areas with abundant solar resources. This study develops and evaluates a cascaded multigeneration system that integrates a solar power tower with a subcritical-CO₂ Brayton cycle, a bottoming Rankine cycle, and a multi-effect distillation unit designed for Mediterranean conditions. The system achieves a maximum net power output of 23.48 MW and a freshwater production rate of 14.25 kg/s during peak summer conditions. The analysis reveals that high solar availability enables CO₂ mitigation of up to 27,434.55 kg-CO₂/h, thereby emphasizing the strong environmental benefits of the proposed system. The integrated Sb-CO₂–Rankine–Desalination configuration attains overall thermal efficiencies of 0.39–0.43, while exergy efficiencies reach 0.58 and 0.73 for the Sb-CO2 and Rankine subsystems, respectively. Sensitivity analysis further confirms the strong pressure dependence of the Sb-CO₂ cycle, with an optimum operation condition around 10 bar and 1100 °C, whereas the Rankine cycle exhibits a steady efficiency enhancement with increasing pressure. The proposed system achieves a simple payback period of 15.3 years, demonstrating its economic feasibility under Mediterranean climatic conditions.

As Turkey uses about 25.7% of energy consumption in residential area and doing this with natural gas imported from the abroad, production of efficient insolation materials is important to decrease energy dependence and deficiency. Due to both waste minimisation and money saving, there are great efforts on developing new environment friendly insulation materials. In the scope of the current study, the heat transfer coefficient was acquired by applying the linear heat conduction coefficient measurement device to find the heat transfer coefficient of the pomegranate seed sample obtained from various processes for the production of insolation material. The sample also have been examined with a two-port vector network analyser to see electromagnetic property. At the end of the experiment, the heat transfer coefficient of the sample produced from pomegranate seeds was calculate as k = 0.6115 W/mK. Moreover, it has been found that the obtained sample performs better than the other insulation materials in terms of electromagnetic perspective. With this feature of the sample, it has been found that using as insolation material in the areas with the high electro-magnetic field can minimize the effect of the harmful electromagnetism in the place where it is applied.

Energy is one of the most necessary and fundamental needs for human beings to continue their lives. Solar energy, which is one of the most important renewable energy sources, has great energy potential in our country with its long radiation duration and radiation amount. The primary thing to do in solar power plant (SPP) installation is to analyze the climatic characteristics of the region. Therefore, having and analyzing the meteorological data of the region has important advantages for the solar power plant to be established. The importance of solar radiation data in the installation of a solar power plant in a region is directly proportional to the payback period of the plant under normal conditions. In this study, a 1 MW SPP feasibility study was conducted in Besni district of Adıyaman province. Depending on the energy potential of the regions, it is calculated that the payback period is between 2-5.7 years for Adıyaman province. Considering the state's electricity pricing, it is seen that the payback period in the industry is about 2 years, while it has a period of 5.7 years for house installation. SPP is analyzed in the light of solar radiation data in the Adıyaman region which will be a pioneer for future studies thanks to the depreciation period as well as analyses performed. In addition to these, the parameters to be considered during the SPP installation stages will be specified in detail.