Short-Term Photovoltaic Power Forecasting Using a Neural Network Dynamic Time Series

Currently, short-term photovoltaic (PV) power forecasting is crucial for optimizing PV power operation in both off-grid and grid-connected systems.This paper compares different PV power forecasting models: Nonlinear Autoregressive (NAR), Nonlinear Input-Output (NIO), and Nonlinear Autoregressive with External Input (NARX) for a PV rooftop 23.1 kWp.The primary input data consists of solar irradiance and temperatures from 1 to 6 hours before the present time.The training process utilizes one year of data ( 2019) to develop the learning model.Data from January 2020 to April 2020 were used to test the models.This paper reports the optimized time series and number of neurons, with MAPE and R 2 used as accuracy indicators for evaluating the models.The best model, NARX achieved MAPE=6.84% and R 2 =0.9 with a time series delay of 3 hours and 50 neurons.