AI Trained IoT Based Automated Solar Panel Cleaning System

Abstract

The collection of dust significantly decreases the efficiency of photovoltaic (PV) modules. In order to reduce the impact of dust on photovoltaic (PV) systems in a cost-efficient way, it is important to use optimal cleaning methods.

The determination of the interval is required. In order to achieve this goal, machine learning (ML) models can be employed to identify the level of dust on photovoltaic (PV) systems that exceeds a predetermined threshold. This study aims to examine the effects of dust on photovoltaic (PV) systems in Bangladesh and suggests a new machine learning (ML) classification approach for detecting dust. Additionally, a cleaning system will be developed. Multiple machine learning classifiers were deployed and their performance was assessed. The Artificial Neural Network (ANN) emerged as the top-performing model, with an accuracy of 98.11%. When the machine learning model detects dust, the user can activate the water sprinkler cleaning system remotely. This technology successfully eliminates dust by spraying pressured water over the panel. The proposed cleaning mechanism successfully improved the efficiency of dusty PV modules to match that of clean modules (14.87%). A quantitative analysis was conducted to measure the reduction in productivity as a monetary loss in order to evaluate the feasibility of the cleaning system. The findings indicate that the suggested cleaning technique is financially feasible for photovoltaic systems with capacities above 2.89 kWp.