A Hybrid Ensemble Learning Framework for Image-Based Solar Panel Classification

TL;DR

This paper introduces a novel dual ensemble neural network framework for image-based solar panel classification, significantly improving accuracy and robustness for automated maintenance and inspection tasks.

Contribution

The paper proposes a hybrid ensemble learning model that combines multiple ensemble techniques into a dual framework, achieving state-of-the-art performance in solar panel image classification.

Findings

Outperforms existing ensemble methods in accuracy and robustness

Achieves state-of-the-art results on the Deep Solar Eye dataset

Demonstrates potential for scalable automated solar panel inspection

Abstract

The installation of solar energy systems is on the rise, and therefore, appropriate maintenance techniques are required to be used in order to maintain maximum performance levels. One of the major challenges is the automated discrimination between clean and dirty solar panels. This paper presents a novel Dual Ensemble Neural Network (DENN) to classify solar panels using image-based features. The suggested approach utilizes the advantages offered by various ensemble models by integrating them into a dual framework, aimed at improving both classification accuracy and robustness. The DENN model is evaluated in comparison to current ensemble methods, showcasing its superior performance across a range of assessment metrics. The proposed approach performs the best compared to other methods and reaches state-of-the-art accuracy on experimental results for the Deep Solar Eye dataset, effectively serving predictive maintenance purposes in solar energy systems. It reveals the potential of hybrid ensemble learning techniques to further advance the prospects of automated solar panel inspections as a scalable solution to real-world challenges.