Hardware implementation of an artificial neural network model to predict the energy production of a photovoltaic system

Abstract

An artificial neural network trained using only the data of solar radiation presents a good solution to predict, in real time, the power produced by a photovoltaic system. Even though the neural network can run on a Personal Computer, it is expensive to have a control room with a Personal Computer for small photovoltaic installations. A FPGA running the neural network hardware will be faster and less expensive. In this work, to assist the hardware implementation of an artificial neural network with a FPGA, a specific tool was used: an Automatic General Purpose Neural Hardware Generator. This tool allows for an automatic configuration system that enables the user to configure the artificial neural network, releasing the user from the details of the physical implementation. The results show that it is possible to accurately model the photovoltaic installation based on data from a nearby meteorological installation and the hardware implementation produces low cost and precise results.