# Solar Power Prediction Modeling Based on Artificial Neural Networks under Partial Shading
## 1 Introduction  
Currently, there is global recognition of the importance of sustainable development and environmental protection. Renewable energy is emerging as a viable solution that aligns with these demands [1]. Among the renewable energy sources, solar photovoltaic (PV) power is gaining significant attention due to its high potential and various advantages. One of the greatest advantages of solar PV is its ability to harness unlimited solar energy without geographical limitations. This means that solar panels can be installed and utilized almost anywhere, making it a highly versatile energy solution. Additionally, solar energy is an abundant and infinite resource, unlike fossil fuels that are finite and depletable. However, it is important to acknowledge that the production of energy from solar PV systems is influenced by various environmental factors [2; 3]. These factors include the availability of irradiation, climate conditions, and local environmental considerations. While solar PV offers immense benefits, it is crucial to consider these variables to maximize its efficiency and effectiveness. Therefore, in the pursuit of sustainable energy development, it is essential to explore the potential of solar PV and address the environmental challenges associated with its energy production. By understanding and mitigating these factors, we can fully leverage the advantages of solar PV and contribute to a greener and more sustainable future. They are highly affected by temperature and irradiation. Based on standard-test conditions, when the temperature exceeds 298 K, the maximum voltage decreases, giving rise to the reduction in the total power production. When the irradiation decreases below 1000 W/m\({}^{2}\), the maximum current decreases, thus reducing the total power production. This deteriorates the power-generation efficiency of a photovoltaic system. Partial shading in a solar power system occurs due to factors such as snow, trees, snow, sand, clouds, and so on. This study focuses on developing a model that improves the power-generation efficiency and enables accurate power prediction under partial shading conditions. When photovoltaic cells are partially shaded, the hot spot effect occurs, resulting in excessive power being dissipated as heat. This hot spot effect shortens the lifetime of both thephotovoltaic cell and modules and reduces energy production efficiency. Moreover, if some cells receive nonuniform irradiation owing to environmental variables, a voltage difference is created between the cells in series, preventing the current from flowing and causing the entire cell to fail. The power loss caused by partial shading is addressed using a bypass diode [2; 3]. A photovoltaic system consists of multiple cells connected in series. By connecting a bypass diode, the entire cell string becomes partial substrings. The substrings are connected in parallel, and a path is formed for the current to flow normally under partial shade. For a photovoltaic system employing bypass diodes to increase the power-generation efficiency, artificial neural networks (ANN) are used to make accurate predictions on the power production [2; 3; 4]. Recent research involves the use of perovskite as a solar cell material to enhance power production efficiency, and there is a study that utilizes artificial neural networks to predict electricity generation by training on these data [5]. In previous studies, various methods such as utilizing the maximum power point, parameter learning in mathematically represented models, and other approaches have been used to predict solar power [6; 7]. The five parameters are affected by temperature and irradiation in the equations, which calculate the electrical characteristics of a photovoltaic system [8]. In this study, the main five parameters of solar photovoltaic systems are individually expressed in terms of temperature and irradiance. The mathematical expressions of five parameters are trained using the ANN model called the multilayer perceptron.