PVPF tool: an automatedWeb application for real-time photovoltaic power forecasting

Abstract

In this paper, we propose a fully automated machine learning based forecasting system, called Photovoltaic Power Forecasting (PVPF) tool, that applies optimised neural networks algorithms to real-time weather data to provide 24 hours ahead forecasts for the power production of solar photovoltaic systems installed within the same region. This system imports the real-time temperature and global solar irradiance records from the ASU weather station and associates these records with the available solar PV production measurements to provide the proper inputs for the pre-trained machine learning system along with the records’ time with respect to the current year. The machine learning system was pre-trained and optimised based on the Bayesian Regularization (BR) algorithm, as described in our previous research, and used to predict the solar power PV production for the next 24 hours using weather data of the last five consecutive days. Hourly predictions are provided as a power/time curve and published in real-time at the website of the renewable energy center (REC) of Applied Science Private University (ASU). It is believed that the forecasts provided by the PVPF tool can be helpful for energy management and control systems and will be used widely for the future research activities at REC.