A-Day-Ahead Photovoltaic Power Prediction Based on Long Short Term Memory Algorithm

In recent years, Photovoltaic System (PV) have been installed in parking lots in order to provide the green energy to Electric vehicles (EVs). Energy Synchronizing between PV generations and EVs demand is a function of different variables, and it is very challenging. Having an accurate prediction of PV generation helps to ease the complexity of this problem. Although various Machine Learning (ML) techniques have been applied and resulted well, traditional ML approaches need years of history of PV generations to make an accurate prediction. In many cases, parking lots or the houses recently equipped by PV panels, and this information is not available. Therefore, the primary motivation of this work is to build a reliable deep learning forecasting model based on Long Short Term Memory (LSTM) architecture in order to make a short-term prediction based on the limited previous observation. The proposed model is applied to a month of the PV power generation data and resulted in the promising accuracy with the Mean Absolute Percentage Error (MAPS) value of 0.028.