A stacking ensemble classifier-based machine learning model for classifying pollution sources on photovoltaic panels

Abstract

Solar energy is a very efficient alternative for generating clean electric energy. However, pollution on the surface of solar panels reduces solar radiation, increases surface transmittance, and raises the surface temperature. All these factors cause photovoltaic (PV) panels to be less efficient. To address this problem, a stacking ensemble classifier-based machine learning model is proposed. In this study, different sources of pollution on each solar panel are used, and their power generation is recorded. The proposed model includes gradient boost, extra tree, and random forest classifiers, with the extra tree classifier serving as a meta-learner. The model takes into account various weather features during the training process, including irradiance and temperature, aiming to increase its accuracy and robustness in identifying pollution sources on the PV panel. Moreover, the proposed model is evaluated using various methods in order to examine performance metrics such as accuracy, F1 score, and precision. Results show that the model can achieve an accuracy score of 97.37%. The model’s performance is also compared to state-of-the-art machine learning models, demonstrating its superiority in accurately classifying pollution sources on PV panels. By utilizing different sources of pollution and weather features during training, the model can accurately classify different pollution sources, resulting in increased power generation efficiency and the longevity of PV panels. The main results of this study can be used to manage and maintain PV panels since the model can identify PV modules that need to be cleaned to keep producing the most power. Furthermore, the efficiency, reliability, and sustainability of PV panels can be further enhanced by the proposed model.

Estimating the volume of penumbra in rodents using DTI and stack-based ensemble machine learning framework

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Machine learning assisted prediction of land surface temperature (LST) based on major air pollutants over the Annamayya District of India

“…These two classes were considered essential classes, and the datasets were passed onto the machine learning frameworks. Three ML algorithms, i.e., Extra Trees classifier (ET) [41,42], Logistic regression (LR) [43,44], and Random Forest (RF) [43], were used in this study as they performed better than others.…”

Stacked Ensemble Deep Learning for Outdoor Insulator Surface Condition Classification: A Profound Study on Water Droplets

“…Several studies on tools made of different materials, but subjected to the same operating conditions, were grouped into a singular category, showing significant findings [48][49][50][51]. In [48], five types of solar panels were used to collect data on different types of contamination.…”

“…Several studies on tools made of different materials, but subjected to the same operating conditions, were grouped into a singular category, showing significant findings [48][49][50][51]. In [48], five types of solar panels were used to collect data on different types of contamination. Similarly, a stacked ensemble learning technique was employed to explore traffic sign recognition [49], malware detection [50], and sand-dust storm forecasting [50].…”