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A Hybrid Deep Learning-Based Network for Photovoltaic Power Forecasting

Author

Listed:
  • Altaf Hussain
  • Zulfiqar Ahmad Khan
  • Tanveer Hussain
  • Fath U Min Ullah
  • Seungmin Rho
  • Sung Wook Baik
  • Chun Wei

Abstract

For efficient energy distribution, microgrids (MG) provide significant assistance to main grids and act as a bridge between the power generation and consumption. Renewable energy generation resources, particularly photovoltaics (PVs), are considered as a clean source of energy but are highly complex, volatile, and intermittent in nature making their forecasting challenging. Thus, a reliable, optimized, and a robust forecasting method deployed at MG objectifies these challenges by providing accurate renewable energy production forecasting and establishing a precise power generation and consumption matching at MG. Furthermore, it ensures effective planning, operation, and acquisition from the main grid in the case of superior or inferior amounts of energy, respectively. Therefore, in this work, we develop an end-to-end hybrid network for automatic PV power forecasting, comprising three basic steps. Firstly, data preprocessing is performed to normalize, remove the outliers, and deal with the missing values prominently. Next, the temporal features are extracted using deep sequential modelling schemes, followed by the extraction of spatial features via convolutional neural networks. These features are then fed to fully connected layers for optimal PV power forecasting. In the third step, the proposed model is evaluated on publicly available PV power generation datasets, where its performance reveals lower error rates when compared to state-of-the-art methods.

Suggested Citation

  • Altaf Hussain & Zulfiqar Ahmad Khan & Tanveer Hussain & Fath U Min Ullah & Seungmin Rho & Sung Wook Baik & Chun Wei, 2022. "A Hybrid Deep Learning-Based Network for Photovoltaic Power Forecasting," Complexity, Hindawi, vol. 2022, pages 1-12, October.
  • Handle: RePEc:hin:complx:7040601
    DOI: 10.1155/2022/7040601
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    Cited by:

    1. Wellcome Peujio Jiotsop-Foze & Adrián Hernández-del-Valle & Francisco Venegas-Martínez, 2024. "Transforming Mexico’s Electric Load Infrastructure: A Quantile Transformer Network Deep Learning Approach, 2019-2020," International Journal of Energy Economics and Policy, Econjournals, vol. 14(5), pages 527-533, September.
    2. Yiling Fan & Zhuang Ma & Wanwei Tang & Jing Liang & Pengfei Xu, 2024. "Using Crested Porcupine Optimizer Algorithm and CNN-LSTM-Attention Model Combined with Deep Learning Methods to Enhance Short-Term Power Forecasting in PV Generation," Energies, MDPI, vol. 17(14), pages 1-17, July.
    3. Meshari D. Alanazi & Ahmad Saeed & Muhammad Islam & Shabana Habib & Hammad I. Sherazi & Sheroz Khan & Mohammad Munawar Shees, 2023. "Enhancing Short-Term Electrical Load Forecasting for Sustainable Energy Management in Low-Carbon Buildings," Sustainability, MDPI, vol. 15(24), pages 1-17, December.

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