DEVELOPMENT OF A MACHINE LEARNING ALGORITHM FOR PREDICTING SOLAR ENERGY GENERATION

Renewable energy has emerged as one of the most promising solutions to the environmental crisis of the modern era. Among different types of renewable energy sources, solar energy has the potential to provide a significant contribution to global energy needs. However, predicting the amount of energy that a solar panel will generate at a given time is challenging due to several factors such as weather conditions, location, and the orientation of the panel. To address this issue, the development of machine learning algorithms for predicting solar energy generation has become an active area of research. The purpose of this study is to develop a machine learning algorithm that can accurately predict the amount of solar energy that a solar panel can generate under varying weather conditions. The algorithm takes into account various weather parameters such as temperature, humidity, wind speed, and cloud cover, as well as the location and orientation of the solar panel. The dataset used in this study is obtained from a solar panel installed at a location in the United States and contains hourly solar energy generation data along with weather parameters.