Sun Tracking using a Weightless Q-Learning Neural Network

Abstract

Photovoltaic(PV) systems are one of the leading technologies to address climate change. Tracking systems improve energy generation by moving the surface to follow the sun's position however, these methods do not ensure optimal results in cloudy environments. This article proposes a closed-loop control algorithm for tracking based on reinforcement learning and weightless neural networks, compared to an astrological model. The method was applied in a single PV array on a single-axis tracking system, simulated with PVLib. Results showed that the architecture could improve results in cloudy environments but not in a clear-sky situation, as expected for a first approach.