Modern solar panel systems have achieved full solar tracking capabilities but lacks the ability to handle power loss due to shadowing. In this work, we present a novel and intuitive method of increasing solar outputs by leveraging reinforcement learning and a 5-DOF robotic arm manipulator design. We utilized in-game cameras to emulate shadow ratios of solar modules derived from current-voltage sensor readings. The reinforcement agent was trained in a simulated environment equipped with geographically-accurate sun positioning and shadow casting using ray-tracing. Our approach showed promising results in simplifying and integrating solar tracking and shadow detection. Publication link
Simulation environment was coded in C# using Unity 3D game engine.
- Location's latitude and longitude (2 states, float)
- Solar altitude and azimuth (2 states, float)
- Shadow ratio per module (6 states, float)
- Current motor angles in degrees (5 states, float)
- Target angle of all motors in degrees (5 continuous values)
- +1 when all solar panels are within
- incidence angle limit < 15 degrees
- shadow ratio limit < 0.1
- -1 when collision occurs
- -0.001 for every time step
- 3000 steps