Adaptive Solar Tree

Abstract

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

System Architecture

Episodic flow

Environment

Simulation environment was coded in C# using Unity 3D game engine.

Observation space (15 elements)

• 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)

Action space

• Target angle of all motors in degrees (5 continuous values)

Rewards

• +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

Episode length

• 3000 steps