Accurate forecasting of solar energy is essential for balancing supply and demand, enhancing energy planning, and supporting the integration of renewable resources into modern electricity grids. While recent research has heavily focused on machine learning-based models such as Long Short-Term Memory networks for solar energy forecasting, these approaches often lack transparency and interpretability. This study presents an interpretable by design photovoltaic (PV) forecasting framework that couples hierarchical factor analysis (HFA) with ridge regression. HFA compresses high dimensional meteorology into three physics meaningful second order factors after which a single parameter ridge model provides coefficient level transparency and regularization in this compact space. Using 15 min measurements from a 93.6 kWp plant in Adıyaman, Türkiye (May 17, 2021–Jan 12, 2025), we evaluate under a unified chronological split (0.64/0.16/0.20). The model combines strong generalization with clear insights into how meteorological variables affect solar power generation, ensuring transparency and verifiability. These results highlight regression-based methods as robust, explainable alternatives to complex deep learning models in photovoltaic forecasting.Since development and forecasting using highly multivariate models is typically not an easy task, our approach is designed to provide a more streamlined model through which future prediction is easier. Simplifying complexity and making it easier to understand how parameters affect the result, our proposed model simplifies finding the most important drivers of solar power generation.

The use of electrical energy from renewable energy sources has increased considerably in recent years. Photovoltaic (PV) solar energy, which is one of the renewable energy sources, takes its source from the sun, which is considered an unlimited source. With the widespread use of photovoltaic systems, some legal regulations by governments have been made for their installation. Simulation programs are used for the design, layout, technical and economic analysis of PV systems. In this study, the design was made using the PVsyst, PV*SOL, Solarius programs, and the PVGIS website used in PV systems. The projects are designed to have an output power of 25 kW. According to the simulation results, the PVGIS website provides approximate data, since technical details cannot be entered. In the results of PVGIS and all simulation programs, it has been calculated that the system can produce 42-48 MWh of electrical energy annually. Power Ratio (PR) results of the system were in the range of 78-85%. Annual electrical energy production per installed kW has been calculated to be 1436-1635 kWh/year. It is stated that the annual saved CO2 will be 18.66-22.226 tons /year. When the simulation results are analyzed monthly, the differences between the months are remarkable. The reason for these deviations can be explained by the annual solar radiation (kWh/m2) data and databases used by the programs. It has been observed that the simulation programs consider the monthly average temperatures and sunshine durations of the region. It is thought that there is a need to compare real-time data with simulation results in future studies. It is considered that this study will be helpful for future studies.

The main outdoor factors that reduce the efficiency of the photovoltaic (PV) panel are the reflection and refraction of light, dirt, dust, and organic waste accumulating on the panel surface. In this article, an antireflection, self-cleaning coating was applied on the PV panel cover glass with a new method. With the coating, the surface has been given a hydrophobic feature. As a coating method, a 3-D printer has not been seen in the literature and used as a new method. The electrospinning method has also been tried as an alternative method. Solutions in different combinations were developed using polylactic acid or polymethylmethacrylate polymer, chloroform (CHCl₃) as a solvent, and silicon dioxide (SiO₂) and titanium dioxide (TiO₂) nanoparticles as primary materials in a modified 3-D printer for bioprinting. Five PV panels were obtained by applying different 3-D parameters from three solutions, which have the best results. Coating thicknesses are in the range of 3.12–8.47 μm. Coated and uncoated PV panels were tested in outdoor conditions for ten-day periods. The power outputs of the PV panels were measured, and their ten-day average efficiency was presented. According to the results, the highest efficiency increase is 8.7%. The highest light transmittance is 88.2% at 550 nm. In addition, hydrophobic properties were observed on all surfaces and the water contact angle was measured as 96.18°.

Accurate short-term power forecasting is essential for enhancing the efficiency and reliability of energy systems. Nonetheless, conventional techniques for forecasting struggle to detect nonlinear patterns in power time series, as maintaining both stability and accuracy in predictions is tough. This research presents a unique prediction framework that integrates a Multilayer Perceptron (MLP) with survivor-based evolutionary selection strategies. The proposed neural network architecture comprises three hidden layers containing 32, 16, and 8 neurons, respectively. This enables the network to extract features while preserving essential information progressively. A Survivor selection process is employed to enhance the model’s efficacy. This approach retains the optimal training models for subsequent training phases. This technique enhances both predictive accuracy and training efficiency. The amalgamation of Survivor-based selection methodologies with MLP architectures for short-term power generation forecasting is overlooked in the existing literature, although it holds promise. Thus, the proposed model is evaluated against established baselines, including Linear Regression (LR), Random Forest (RF), and Support Vector Regression (SVR). The results from 30 distinct trials indicate that the proposed MLP (32-16-8) combined with the Survivor approach exhibits the minimal prediction errors, with a mean absolute error (MAE) of 5.3588 and a root mean square error (RMSE) of 10.0216. This strategy is superior in minimizing errors compared to alternative methods. Furthermore, statistical analyses utilizing the Wilcoxon signed-rank test and paired t-test indicate that the proposed method significantly outperforms SVR and RF, while displaying performance comparable to LR. The findings indicate that including a Survivor-based selection mechanism in the MLP training process is an effective and reliable method for forecasting short-term generation power.

It is seen that the damage to the environment has increased with the use of fossil fuels around the world. It is known that studies continue to minimize the damage to the environment with alternative energy generation methods. Recently, it is seen that generating electrical energy using solar energy, known as clean energy, has an important place. With the developing semiconductor technologies, the use of photovoltaic systems is increasing day by day. The aim of this study is to estimate the amount of energy that will be produced by simulating and modeling the performance of PV (Photovoltaic) systems using PVsyst and PV*SOL programs before the Photovoltaic systems are installed in the region. In the study, grid-connected roof system modeling was made in Bakırköy district of Istanbul province. In the modeling of the system, a total of 90 solar panels were placed on an area of 114.9 m2 , in East and West directions. In total, it is predicted that 17.1 kW of energy will be obtained when the system is used. In the system design, the avoided CO₂ emission is calculated as 8,856 kg/year and the amortization period is calculated as 7.2 years. When the programs are used, the analysis of the system is made before the implementation and it is seen that time and cost savings are achieved.

The most common type of identification system today is RFID. RFID circuits are used as covered with plastic. With the increase in usage areas, it is also used on metal, wood, paper, and plastic product. In this study, the behavior of the same circuit on different surfaces was investigated. The surface impedance and signal reflection coefficients of RFID tag antennas were investigated based on paper, plastic, and textile surfaces. According to the results of the electrical and mechanical tests, the best results in terms of reflectance coefficients and surface impedances of RFID tags are on PET surfaces. The surface impedance and the reflection coefficients were high on paper surfaces. The lowest values were measured on textile surfaces. According to the results, it has been seen that RFID antenna application on plastic, paper, and textile surfaces is possible and usable.