Methodology Based on Artificial Neural Networks for Hourly Forecasting of PV Plants Generation

Abstract

We propose a methodology for the hourly forecast of the photovoltaic (PV) power of two plants (6.03 kWp and 7.37 kWp) installed in the metropolitan region of Fortaleza - CE. The methodology uses two Artificial Neural Networks (ANN) to predict time series: Perceptron type with Multiple Layers (MLP) and radial base functions (RBF), trained with historical data of hourly PV power collected during the year 2020 in the locations under study. System performance meters are applied (correlation coefficient - R, Nash-Sutcliffe efficiency - NSE and relative trend - VR). The data evaluated in each plant are treated using MLP and RBF networks, as well as the Persistence method, seeking to increase the study reliability. ANN results indicate potential to learn the behavior of the plants, with R above 80%, VR close to zero and NSE above 0.50 in two of the applications. In this specific case, despite being similar networks, MLP shows a higher accuracy than RBF.