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Dynamic Threshold Fine-Tuning in Anomaly Severity Classification for Enhanced Solar Power Optimization

By
Mohamed Khalifa Boutahir ,
Mohamed Khalifa Boutahir

Engineering science and technology laboratory, IDMS Team, Faculty of Sciences and Tech-niques, Moulay Ismail University of Meknes, Morocco

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Abdelaaziz Hessane ,
Abdelaaziz Hessane

Engineering science and technology laboratory, IDMS Team, Faculty of Sciences and Tech-niques, Moulay Ismail University of Meknes, Morocco

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Imane Lasri ,
Imane Lasri

Laboratory of Conception and Systems (Electronics, Signals and Informatics), Faculty of Sci-ences, Mohammed V University in Rabat, Morocco

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Salma Benchikh ,
Salma Benchikh

Advanced systems engineering laboratory, National school of applied sciences, Ibn Tofail Uni-versity, Kenitra, Morocco

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Yousef Farhaoui ,
Yousef Farhaoui

Engineering science and technology laboratory, IDMS Team, Faculty of Sciences and Tech-niques, Moulay Ismail University of Meknes, Morocco

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Mourade Azrour ,
Mourade Azrour

Engineering science and technology laboratory, IDMS Team, Faculty of Sciences and Tech-niques, Moulay Ismail University of Meknes, Morocco

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Abstract

This study explores an innovative approach to anomaly severity classification within the realm of solar power optimization. Leveraging established machine learning algorithms—including Isolation Forest (IF), Local Outlier Factor (LOF), and Principal Component Analysis (PCA)—we introduce a novel framework marked by dynamic threshold fine-tuning. This adaptive paradigm aims to refine the accuracy of anomaly classification under varying environmental conditions, addressing factors such as dust storms and equipment irregularities. The research builds upon datasets derived from Errachidia, Morocco. Results underscore the effectiveness of dynamically adjusting severity thresholds in optimizing anomaly classification and subsequently improving the overall efficiency of solar power generation. The study not only reaffirms the robustness of the initial framework but also emphasizes the practical significance of fine-tuning anomaly severity classification for real-world applications in solar energy management. By providing a more nuanced perspective on anomaly detection, this research advances our understanding of the intricate precision required for optimal solar power generation efficiency. The findings contribute valuable insights into the broader field of machine learning applications in renewable energy, offering a pathway for the refinement of existing frameworks for enhanced sustainability and operational effectiveness.

How to Cite

1.
Khalifa Boutahir M, Hessane A, Lasri I, Benchikh S, Farhaoui Y, Azrour M. Dynamic Threshold Fine-Tuning in Anomaly Severity Classification for Enhanced Solar Power Optimization. Data and Metadata [Internet]. 2023 Dec. 28 [cited 2024 Apr. 22];2:94. Available from: https://dm.saludcyt.ar/index.php/dm/article/view/94

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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