# Power Prediction in Photovoltaic Systems with Neural Networks: A Multi-Parameter Approach
## 1 Introduction  
In today's world, while the demand for energy is constantly increasing, the use of fossil fuels, which play a significant role in energy production, is diminishing due to their limited reserves and also their causing adverse environmental effects. This situation leads to a shift towards renewable energy sources, which are environmentally friendly and sustainable. Among renewable energy sources obtained from naturally replenishing resources, such as wind, wave, and biomass, solar energy, in particular, stands out in meeting the need for electricity. As the role of photovoltaic (PV) systems in energy production continues to grow, improving the efficiency of these systems holds critical importance in terms of both economic and environmental sustainability. The main factors affecting the efficiency of PV systems include meteorological variables, such as temperature, solar radiation, wind speed, humidity, and cloud cover. Additionally, the limitation of PV systems is that electricity generation is only possible during daylight hours. To overcome this limitation, energy storage solutions are being developed. However, energy storage requires additional space and incurs extra costs. Thus, energy management and planning play a crucial role. Renewable energy sources are not only clean and domestically available but also preferable due to their potential to bemore cost-effective and reliable. However, it should not be overlooked that certain renewable energy sources, such as solar energy, generate intermittent power, and environmental factors significantly influence their energy production performance. In this context, many methods have been proposed in the literature to increase the efficiency of PV systems [1; 2; 3]. These proposed methods aim to contribute to energy efficiency and sustainability in the established plants. Before establishing a power plant, it is crucial to conduct a feasibility study that includes planning related to the process, determining the energy potential of the region where the installation will be placed, assessing the supply-demand balance, and calculating costs. In summary, energy management and planning must be conducted considering all these factors while maintaining a balance between supply and demand. Otherwise, significant challenges may arise for both consumers and power generation plants. In feasibility studies, accurately determining the energy potential of the region where the plant will be established ensures the correct assessment of plant capacity, equipment to be used, and, consequently, costs. Accurately determining the energy potential of the region also ensures the correct prediction of the energy to be produced by the plant. Proper power forecasting of power plants at the installation stage can help mitigate these challenges. Accurate power forecasting enhances energy management by providing answers to key questions, such as which power plant should be utilized, at what time intervals, and in what quantities, thereby reducing uncertainties in energy security. Furthermore, when renewable energy sources are available (solar power plants when sunlight is sufficient, wind power plants when wind speeds are adequate, hydroelectric plants when river flow is appropriate, etc. ), planned reductions in fossil fuel consumption can yield economic benefits, allowing consumers to access cheaper energy. In recent years, with the increasing number of PV plants, energy production is directly affected by changing weather conditions since energy production depends on sunlight. Therefore, before the establishment of a PV plant, or during capacity expansion planning, it becomes essential to make energy forecasts considering the changing climate and environmental conditions in order to foresee the future of the plant. Otherwise, incorrect energy forecasts will lead to faulty energy planning. This will dampen the economic advantages of renewable energy and negatively affect both grid stability and energy management.