Hasan Demir

In this study, an experimental setup was established to measure the energy that can be harvested depending on the frequency of periodic mechanical pulses using Polyvinylidene Fluoride (PVDF) piezoelectric material. The measured data were employed in Artificial Neural Network (ANN), K-Nearest Neighbours (KNN), Support Vector Machine (SVM), and Random Forest (RF) algorithms, and the models were compared in terms of prediction performance and processing time. The performance of the models was evaluated using regression success metrics such as coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe Efficiency (NSE), computation time and additionally Taylor diagram and Bland-Altman analysis were applied to further evaluate the accuracy and reliability of the models. According to the results, all models achieved high accuracy, with the highest R² value obtained from the RF model (R² = 0.9756), followed closely by SVM (R² = 0.9752) and ANN (R² = 0.9730). The RF model also showed the lowest error rates (MAE = 3.5874, RMSE = 8.7020) and a strong NSE value (0.9751), making it the most balanced and reliable model overall. The ANN model demonstrated competitive performance, though a noticeable gap was observed between training and testing errors (MSEtrain = 139.76 vs. MSEtest = 86.59), indicating potential overfitting tendencies. The KNN model, despite its very fast computation time (0.0066 s), exhibited lower predictive accuracy compared to the other models (R² = 0.9464, RMSE = 16.6871). These findings suggest that Random Forest provides the best compromise between accuracy, stability, and efficiency for piezoelectric energy harvesting prediction tasks. The developed predictive models will serve as a valuable reference for researchers conducting feasibility studies, particularly in applications such as powering CMOS circuits and sensors through periodic pulse-based energy harvesting.

Renewable energy technologies are becoming more and more relevant in a variety of engineering fields as a result of the move toward low-carbon, sustainable energy systems. Although research has historically concentrated on power generation, it now covers a broad range of applications, including precision agriculture, smart grids, energy storage, healthcare devices, and sustainable buildings. However, existing review studies are often limited to single disciplines or specific technologies, lacking a unified cross-disciplinary perspective that captures the interconnected nature of modern renewable energy systems. This gap motivates the need for a comprehensive review that bridges multiple engineering domains. This review provides a comprehensive synthesis of literature on renewable energy applications in electrical and electronics, computer, environmental, biomedical, architectural, and agricultural engineering. In electrical and electronics engineering, the use of renewable energy sources is largely based on the efficient generation of electricity from natural resources such as solar, wind, and ocean energy. Computer engineering contributes through artificial intelligence (AI), Internet of Things (IoT) architectures, digital twins, and cybersecurity solutions, optimizing energy management. Environmental engineering emphasizes life cycle assessment, carbon footprint reduction, and circular economy strategies. In biomedical engineering, energy harvesting and self-powered devices illustrate micro-scale applications of renewable energy. Architectural engineering integrates renewable systems through building-integrated photovoltaics, net-zero energy designs, and smart building management, while agricultural engineering uses solar-powered irrigation, biomass utilization, agrivoltaic systems, and other sustainable practices. To support a low-carbon future with integrated and sustainable engineering solutions, this study not only highlights innovations within individual fields but also showcases how different disciplines can connect and work together. Overall, the review offers a novel cross-disciplinary framework that advances the understanding of renewable energy systems beyond isolated applications and provides direction for future integrative research.

This study evaluates the performance of bifacial and monofacial photovoltaic (PV) modules in agrivoltaic systems (AV) using PVsyst simulations, addressing the challenge of balancing agricultural productivity with PV yield. Its novelty lies in integrating field‐measured, monthly albedo data from a wheat field into system modelling and analyzing the specific influence of module height on bifacial performance. The methodology includes albedo measurements, AV design, and comparative simulations of monofacial and bifacial modules at 4 m (AV1) and 5 m (AV2) installation heights. Results indicate that bifacial modules produce 19.46% more energy than monofacial modules, and the height difference between AV1 and AV2 yields only a marginal annual gain of 380 kWh. Aligning module azimuth to the field orientation (−44°) minimizes agricultural disruption but causes a 5.3% loss in global radiation, illustrating operational trade‐offs. A layout of five module rows spaced 10 m apart prevents shading overlap, yielding a low shading factor (0.016). Overall, the simulations demonstrate that realistic albedo integration can enhance bifacial output by up to 19.48%, while also revealing the occasional incompatibility between maximizing PV generation and maintaining agricultural functionality in AVs.

Solar photovoltaic (PV) is a crucial renewable energy source that converts sunlight into electricity using silicon-based semiconductor materials. However, due to the non-linear characteristic behavior of the PV module, the module’s output power varies according to the solar radiation and the ambient temperature. To address this challenge, maximum power point tracking (MPPT) techniques are employed to extract the maximum amount of power from the PV modules. This paper offers a comprehensive review of widely used traditional and advanced MPPT approaches in PV systems, along with current developments and future directions in the field. Under uniform insolation, these methods are compared based on their strengths and weaknesses, including sensed parameters, circuitry, tracking speed, implementation complexity, true MPPT, accuracy, and cost. Additionally, MPPT algorithms are evaluated in terms of their performance in reaching maximum power point (MPP) under partial shading condition (PSC). Existing research clearly demonstrates that the advanced techniques exhibit superior efficiency in comparison to traditional methods, although at the cost of increased design complexity and higher expenses. By presenting a detailed review and providing comparison tables of widely used MPPT techniques, this study aims to provide valuable insights for researchers and practitioners in selecting appropriate MPPT approaches for PV applications.

Photovoltaic (PV) modules have emerged as a promising technology in the realm of sustainable energy solutions, specifically in the harnessing of solar energy. Photovoltaic modules, which use solar energy to generate electricity, are often used on terrestrial platforms. In recent years, there has been an increasing inclination towards the installation of photovoltaic (PV) modules over water surfaces, including lakes, reservoirs, and even oceans. The novel methodology introduces distinct benefits and complexities, specifically pertaining to the thermal characteristics of the modules. In order to accomplish this objective, a photovoltaic (PV) module system with a capacity of 1 MW was developed as a scenario in the PVsyst Program. The scenario simulation was conducted on the Mamasın Dam, situated in the Gökçe village within the Aksaray province. To conduct the efficiency analysis, a comparative evaluation was conducted between bifacial and monofacial modules, which were installed from above the water at 1 m. The comparison was made considering two different types of modules. Additionally, the albedo effect, water saving amount, and CO₂ emissions of the system were also investigated. Albedo measurements were made in summer when the PV power plant will operate most efficiently. As a result of the simulations, it was found that bifacial modules produce 12.4% more energy annually than monofacial modules due to the albedo effect. It is estimated that PV power plant installation will save 19,562.695 and 17,253.475 tons of CO₂ emissions in bifacial and monofacial systems, respectively.

Polymer materials have been used in a wide area from our homes to industry today. For this reason, creep curves from the mechanical properties of polymer materials are an important factor for the correct use of polymers. This article presents a new constant load creep test machine design which is a mechatronic system to perform creep testing of polymers. A low-cost creep test machine was developed by mounting clamps, pulleys, weights and steel cable on top of a main frame. With the measurement unit, the linear movement of the steel cable was detected and recorded with the data logging system together with the ambient temperature. Tests were made with a 15% calcium carbonate reinforced polypropylene specimen. It was observed that the experimental data obtained were compatible with a standard creep test curve. These findings confirm that the presented constant load creep test machine design works efficiently.

This study shows the dust effect on solar panel efficiency based on experimental measurements. Sieve analysis has been performed to investigate the effect on the efficiency of the panel. Dust particles have been divided into different sizes such as (-75), (+75/-105), (+105/-250), (+250/-450). In addition, each piece weighed 20 g, uniformly sprinkled onto a 40-Watt SUNNY polycrystalline panel. In order to obtain the output voltages of the PV panel used a data acquisition card. Additionally, a photodiode sensor has been used in order to obtain the light reflection data from the panel to the outside while there had covered different diameters of dust particles on the panel. The data set consisting of electrical parameters have been used to compered both experimental study and Artificial Neural Network (ANN) output. There were 5 different data sets with the clean PV panel in this study (randomly selected data from it used as %40 training - %60 test) and the clean panel data have been used twice in the training and test part of the ANN. In this study, it has been observed that the ANN method can be used to estimate panel efficiency due to the linearity (The R-value was gotten nearly equal to 1 and it shows that there is a linear relationship between outputs and targets.) if the appropriate transfer function is selected. Also, this study shows that as the particle diameter covering the panel gets smaller, the output voltage of the panel decreases linearly.