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https://github.com/harmanveer-2546/dust-detection-on-solar-panel-using

The accumulation of dust, snow, bird drops etc. on the surface of solar panels reduces the efficiency of the solar modules and hence the amount of produced energy. Monitoring and cleaning solar panels is a crucial task, hence developing an optimal procedure to monitor and clean these panels is very important in order to increase modules efficiency.
https://github.com/harmanveer-2546/dust-detection-on-solar-panel-using

classification dirty inceptionv3-model keras numpy pandas plotly python regularizer sequential solar-panels tensorflow visualization

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The accumulation of dust, snow, bird drops etc. on the surface of solar panels reduces the efficiency of the solar modules and hence the amount of produced energy. Monitoring and cleaning solar panels is a crucial task, hence developing an optimal procedure to monitor and clean these panels is very important in order to increase modules efficiency.

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README 

        
# Dust Detection on solar panels by using Deep Learning InceptionV3 Model:



InceptionV3 is a convolutional neural network (CNN) architecture designed for image recognition and classification. It's known for its efficiency and accuracy, making it a popular choice for various computer vision tasks. Here's a quick breakdown:



* Developed by Google: InceptionV3 is part of the Inception family of CNNs created by Google researchers.

     

### Focus on Efficiency:



This architecture achieves high accuracy while keeping the number of parameters (complexity) relatively low compared to other models.



Building Blocks: It uses building blocks called "Inception modules" that combine different convolutional filters to extract diverse features from images.



### Pre-trained for Image Recognition:



InceptionV3 often comes pre-trained on a massive image dataset called ImageNet, allowing it to be fine-tuned for specific tasks like dust detection on solar panels.



Overall, InceptionV3 is a powerful and versatile tool for image recognition tasks, making it a good choice for projects like dust detection in solar panel cleaning.



The accumulation of dust, snow, bird drops etc. on the surface of solar panels reduces the efficiency of the solar modules and hence the amount of produced energy. Monitoring and cleaning solar panels is a crucial task, hence developing an optimal procedure to monitor and clean these panels is very important in order to increase modules efficiency, reduce maintenance cost and reducing the use of resources.



## Objectives:

This project is to investigate the ability of different machine learning classifiers to detect dust on the solar panel surfaces with the highest possible accuracy.



1.    Solar panels work by converting sunlight into electricity. If dirt, dust, or other debris accumulates on the surface of the solar panels, it can reduce the amount of sunlight that is absorbed, which can lead to a decrease in the amount of electricity that is generated.



2.    The amount of energy loss depends on the level of dirt and debris on the solar panels. According to the Solar Energy Power Association, dirty solar panels can lose up to 20% of their energy output. The National Renewable Energy Laboratory puts that figure even higher, at 25%.



3.    In addition to reducing the amount of electricity that is generated, dirty solar panels can also shorten the lifespan of the solar panels. This is because the dirt and debris can trap moisture, which can cause corrosion and other damage to the solar panels.



4. For these reasons, it is important to clean solar panels regularly. The frequency of cleaning will depend on a number of factors, including the environment in which the solar panels are located. In general, however, most manufacturers recommend that solar panels be cleaned at least twice a year.