Machine Learning based Solar PV Power Prediction
Ram Prakash Ponraj, Vijay Ravindran, Krishnakumar Chittibabu, T Akash, Ann Agnetta Chandru, Emmanuel Louis
Abstract
Solar Energy is abundant in nature, the power can be extracted in many forms from the Sun. Electrical Power Generation is one of the useful contribution of solar energy, which can be generated using Solar Photovoltaic Cells. The basic parameters of the solar energy like irradiance, temperature, etc., are variables in nature. Solar PV power prediction is a critical aspect of solar PV system management and useful for load synchronization. The development of more accurate and reliable solar PV power prediction methods is essential for the continued growth and success of the renewable energy sector. Various machine learning algorithms can be used to develop a model that can accurately predict the output of a PV system. In this article, a simple random forest machine learning approach was used to predict the potential power generated by a PV system. The model is trained using historical data of the PV system output, such as the amount of energy produced, temperature, irradiance, and other factors. Results of the algorithm is presented, compared with Decision Tree algorithm and the output parameters are discussed.