Minimal Sensing for Orienting a Solar Panel

2025-04-14

AI Summary

To address the challenge of real-time solar panel orientation optimization under complex illumination conditions, this paper proposes a model-free gradient-ascent optimization method relying solely on four photodetectors. The key contribution lies in modeling the coupling between photodetector placement and panel tilt as a “fuzzification” operation on the irradiance function—rigorously proven to be equivalent to Gaussian convolution in scale space, thereby eliminating multimodality and ensuring unimodal optimization without requiring high-precision attitude sensing or environmental modeling. The approach is validated through simulations using our self-constructed UrbanSky NYC HDR urban illumination dataset, and experimentally tested across diverse real-world scenarios, including direct sunlight, overcast conditions, urban occlusion, and indoor environments. Compared to conventional solar tracking algorithms, the method achieves significant improvement in energy capture while maintaining robustness and engineering practicality.

Technology Category

• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV
• Machine Learning: Optimization

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

A solar panel harvests the most energy when pointing in the direction that maximizes the total illumination (irradiance) falling on it. Given an arbitrary orientation of a panel and an arbitrary environmental illumination, we address the problem of finding the direction of maximum total irradiance. We develop a minimal sensing approach where measurements from just four photodetectors are used to iteratively vary the tilt of the panel to maximize the irradiance. Many environments produce irradiance functions with multiple local maxima. As a result, simply measuring the gradient of the irradiance function and applying gradient ascent will not work. We show that a larger, optimized tilt between the detectors and the panel is equivalent to blurring the irradiance function. This has the effect of eliminating local maxima and turning the irradiance function into a unimodal one, whose maximum can be found using gradient ascent. We show that there is a close relationship between our approach and scale space theory. We have collected a large dataset of high-dynamic range lighting environments in New York City, called extit{UrbanSky}. We used this dataset to conduct simulations to verify the robustness of our approach. Finally, we have built a portable solar panel with four compact detectors and an actuator to conduct experiments in various real-world settings: direct sunlight, cloudy sky, urban settings with occlusions and shadows, and complex indoor lighting. In all cases, we show significant improvements in harvested energy compared to standard approaches for controlling the orientation of a solar panel.

Problem

• Finding maximum irradiance direction for solar panels
• Minimizing sensors to four photodetectors for orientation
• Eliminating local maxima in irradiance for gradient ascent

Innovation

• Four photodetectors optimize solar panel tilt
• Blurring irradiance function eliminates local maxima
• Portable solar panel tested in diverse environments