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Energy Performance Analysis of Photovoltaic Integrated with Microgrid Data Analysis Using Deep Learning Feature Selection and Classification Techniques

Author

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  • Sana Qaiyum

    (School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA 70504, USA)

  • Martin Margala

    (School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA 70504, USA)

  • Pravin R. Kshirsagar

    (Department of Data Science, Tulsiramji Gaikwad Patil College of Engineering and Technology, Nagpur 441108, India)

  • Prasun Chakrabarti

    (Department of Computer Science and Engineering, ITM SLS Baroda University, Vadodara 391510, India)

  • Kashif Irshad

    (Interdisciplinary Research Centre for Renewable Energy and Power System, King Fahad University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

Abstract

Microgrids are an essential element of smart grids, which contain distributed renewable energy sources (RESs), energy storage devices, and load control strategies. Models built based on machine learning (ML) and deep learning (DL) offer hope for anticipating consumer demands and energy production from RESs. This study suggests an innovative approach for energy analysis based on the feature extraction and classification of microgrid photovoltaic cell data using deep learning algorithms. The energy optimization of a microgrid was carried out using a photovoltaic energy system with distributed power generation. The data analysis has been carried out for feature analysis and classification using a Gaussian radial Boltzmann with Markov encoder model. Based on microgrid energy optimization and data analysis, an experimental analysis of power analysis, energy efficiency, quality of service (QoS), accuracy, precision, and recall has been conducted. The proposed technique attained power analysis of 88%, energy efficiency of 95%, QoS of 77%, accuracy of 93%, precision of 85%, and recall of 77%.

Suggested Citation

  • Sana Qaiyum & Martin Margala & Pravin R. Kshirsagar & Prasun Chakrabarti & Kashif Irshad, 2023. "Energy Performance Analysis of Photovoltaic Integrated with Microgrid Data Analysis Using Deep Learning Feature Selection and Classification Techniques," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:11081-:d:1194904
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    References listed on IDEAS

    as
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    Cited by:

    1. Tong Zhu & Gechao Huang & Xuetong Ouyang & Weilin Zhang & Yanfeng Wang & Xi Ye & Yuhong Wang & Shilin Gao, 2024. "Analysis and Suppression of Harmonic Resonance in Photovoltaic Grid-Connected Systems," Energies, MDPI, vol. 17(5), pages 1-22, March.
    2. Liu, Hao-Dong & Zhang, Hang & Wang, Jie-Ping & Dou, Jin-Xiao & Guo, Rui & Li, Guang-Yue & Liang, Ying-Hua & Yu, Jiang-long, 2024. "Construction of macromolecular model of coal based on deep learning algorithm," Energy, Elsevier, vol. 294(C).

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