A predictive model for solar photovoltaic power using the Levenberg-Marquardt and Bayesian regularization algorithms and real-time weather data

Abstract

The stability of power production in photovoltaics (PV) power plants is an important issue for large-scale gridconnected systems. This is because it affects the control and operation of the electrical grid. An efficient forecasting model is proposed in this paper to predict the next-day solar photovoltaic power using the Levenberg-Marquardt (LM) and Bayesian Regularization (BR) algorithms and real-time weather data. The correlations between the global solar irradiance, temperature, solar photovoltaic power, and the time of the year were studied to extract the knowledge from the available historical data for the purpose of developing a real-time prediction system. The solar PV generated power data were extracted from the power plant installed on-top of the faculty of engineering building at Applied Science Private University (ASU), Amman, Jordan and weather data with real-time records were measured by ASU weather station at the same university campus. Huge amounts of training, validation, and testing experiments were carried out on the available records to optimize the Neural Networks (NN) configurations and compare the performance of the LM and BR algorithms with different sets and combinations of weather data. Promising results were obtained with an excellent realtime overall performance for next-day forecasting with a Root Mean Square Error (RMSE) value of 0.0706 using the Bayesian regularization algorithm with 28 hidden layers and all weather inputs. The Levenberg-Marquardt algorithm provided a 0.0753 RMSE using 23 hidden layers for the same set of learning inputs. This research shows that the Bayesian regularization algorithm outperforms the reported real-time prediction systems for the PV power production.