Improving Solar Panel Efficiency Using Reinforcement Learning

Solar panels sustainably harvest energy from the sun. To improve performance, panels are often equipped with a tracking mechanism that computes the sun’s position in the sky throughout the day. Based on the tracker’s estimate of the sun’s location, a controller orients the panel to minimize the angle of incidence between solar radiant energy and the photovoltaic cells on the surface of the panel, increasing total energy harvested. Prior work has developed efficient tracking algorithms that accurately compute the sun’s location to facilitate solar tracking and control. However, always pointing a panel directly at the sun does not account for diffuse irradiance in the sky, reflected irradiance from the ground and surrounding surfaces, or changing weather conditions (such as cloud coverage), all of which are contributing factors to the total energy harvested by a solar panel. In this work, we show that a reinforcement learning (RL) approach can increase the total energy harvested by solar panels by learning to dynamically account for such other factors. We advocate for the use of RL for solar panel control due to its effectiveness, negligible cost, and versatility. Our contribution is twofold: (1) an adaption of typical RL algorithms to the task of improving solar panel performance, and (2) an experimental validation in simulation based on typical solar and irradiance models for experimenting with solar panel control. We evaluate the utility of various RL approaches compared to an idealized controller, an efficient state-of-the-art direct tracking algorithm, and a fixed panel in our simulated environment. We experiment across different time scales, in different places on earth, and with dramatically different percepts (sun coordinates and raw images of the sky with and without clouds), consistently demonstrating that simple RL algorithms improve over existing baselines.