Analyzing the performance of photovoltaic systems using support vector machine classifier

Hafdaoui, Hichem; Boudjelthia, El Amin Kouadri; Chahtou, Amina; Bouchakour, Salim; Belhaouas, N.

doi:10.1016/j.segan.2021.100592

Cited by 12 publications

(8 citation statements)

References 12 publications

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“…After seven (07) months of monitoring, a 270 W monocrystalline PV module's performance study was conducted using a Support Vector Machine (SVM) as a classification and analysis approach. This study demonstrates the value of SVM classification and Artificial intelligence in handling databases for photovoltaic system monitoring 13 .…”

Section: Literature Reviewmentioning

confidence: 74%

An Artificial Intelligence and Machine Learning Model to Estimate the Cleaning Periodicity for Dusty Solar Photovoltaic (PV) Modules in A Composite Environment

Pimpalkar,

Sahu,

Patil

2023

jmmf

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Solar energy is harnessed on a considerable scale nowadays. By 2030, the solar power output is expected to increase to 2500 GW marginally. High cell temperatures and soiling significantly affect the performance of solar photovoltaic systems. This study clarifies the effect of dust deposition on the transmission and output power of photovoltaic modules. The analytical and machine-learning models were developed to analyze the effects of soil deposition on the photovoltaic panels. The field data were used to train and test the algorithm for developing the machine-learning model. An optimum cleaning and maintenance schedule is then proposed based on the site's environmental conditions. The novelty of the research was to gather environmental parameters in real-time conditions that affect the soiling rate of photovoltaic panels, further affecting the conversion efficiency of photovoltaic panels. Based on the theoretical model developed, the cleaning frequency of the module was observed to be 18 days, considering 5% power loss and dust density accumulation of 2g/m2. A random forest model was developed considering ambient temperature, solar irradiance, relative humidity, wind speed, dust concentration, and energy generated. The predicted cleaning frequency is observed to be 25 days using the random forest model.

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Section: Literature Reviewmentioning

confidence: 74%

An Artificial Intelligence and Machine Learning Model to Estimate the Cleaning Periodicity for Dusty Solar Photovoltaic (PV) Modules in A Composite Environment

Pimpalkar,

Sahu,

Patil

2023

jmmf

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“…SVM is a supervised learning method used for regression, classification, outlier detection, and feature selection problems [38]. The main objective of an SVM algorithm is to create an optimal hyperplane that separates the classes as much as possible.…”

Section: Classification Models 2431 Support Vector Machinementioning

confidence: 99%

IoT System for Gluten Prediction in Flour Samples Using NIRS Technology, Deep and Machine Learning Techniques

et al. 2023

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Gluten is a natural complex protein present in a variety of cereal grains, including species of wheat, barley, rye, triticale, and oat cultivars. When someone suffering from celiac disease ingests it, the immune system starts attacking its own tissues. Prevalence studies suggest that approximately 1% of the population may have gluten-related disorders during their lifetime, thus, the scientific community has tried to study different methods to detect this protein. There are multiple commercial quantitative methods for gluten detection, such as enzyme-linked immunosorbent assays (ELISAs), polymerase chain reactions, and advanced proteomic methods. ELISA-based methods are the most widely used; but despite being reliable, they also have certain constraints, such as the long periods they take to detect the protein. This study focuses on developing a novel, rapid, and budget-friendly IoT system using Near-infrared spectroscopy technology, Deep and Machine Learning algorithms to predict the presence or absence of gluten in flour samples. 12,053 samples were collected from 3 different types of flour (rye, corn, and oats) using an IoT prototype portable solution composed of a Raspberry Pi 4 and the DLPNIRNANOEVM infrared sensor. The proposed solution can collect, store, and predict new samples and is connected by using a real-time serverless architecture designed in the Amazon Web services. The results showed that the XGBoost classifier reached an Accuracy of 94.52% and an F2-score of 92.87%, whereas the Deep Neural network had an Accuracy of 91.77% and an F2-score of 96.06%. The findings also showed that it is possible to achieve high-performance results by only using the 1452–1583 nm wavelength range. The IoT prototype portable solution presented in this study not only provides a valuable contribution to the state of the art in the use of the NIRS + Artificial Intelligence in the food industry, but it also represents a first step towards the development of technologies that can improve the quality of life of people with food intolerances.

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“…Admite la clasificación binaria y la separación de puntos de datos en dos clases. Para la clasificación multiclase, se utiliza el mismo principio después de dividir el problema de clasificación múltiple en múltiples problemas de clasificación binaria (Arredondo et al, 2017;Hafdaoui et al, 2022).…”

Section: Nivel De Tráfico Descripciónunclassified

Clasificación de la ocupación espectral para la toma de decisiones en redes inalámbricas cognitivas implementando extracción de características y aprendizaje automático

2022

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La radio cognitiva (CR) es una tecnología que busca maximizar el uso de los recursos de frecuencia al permitir que los usuarios sin licencia accedan a las bandas de espectro de forma oportunista. Se analiza el proceso de toma de decisiones a través de la clasificación de tres niveles de tráfico. Para evaluar el proceso, se utilizan tres métricas de costo y una de beneficio. Los resultados muestran que el clasificador que utiliza máquinas de soporte vectorial presenta el mejor desempeño, seguido por KnNC (K-nearest Neighbor Classifier) y DAC (Discriminan Analysis Classifier). El desempeño más bajo, con los indicadores de desempeño más deficiente, lo obtiene BDT (Binary Decision Tree). Se concluye que la CR ofrece un conjunto de soluciones al hacer que el espectro se use de manera dinámica.

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Analyzing the performance of photovoltaic systems using support vector machine classifier

Cited by 12 publications

References 12 publications

An Artificial Intelligence and Machine Learning Model to Estimate the Cleaning Periodicity for Dusty Solar Photovoltaic (PV) Modules in A Composite Environment

An Artificial Intelligence and Machine Learning Model to Estimate the Cleaning Periodicity for Dusty Solar Photovoltaic (PV) Modules in A Composite Environment

IoT System for Gluten Prediction in Flour Samples Using NIRS Technology, Deep and Machine Learning Techniques

Clasificación de la ocupación espectral para la toma de decisiones en redes inalámbricas cognitivas implementando extracción de características y aprendizaje automático

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