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GGNet: A novel graph structure for power forecasting in renewable power plants considering temporal lead-lag correlations

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  • Zhu, Nanyang
  • Wang, Ying
  • Yuan, Kun
  • Yan, Jiahao
  • Li, Yaping
  • Zhang, Kaifeng

Abstract

Power forecast for each renewable power plant (RPP) in the renewable energy clusters is essential. Though existing graph neural networks (GNN)-based models achieve satisfactory prediction performance by capturing dependencies among distinct RPPs, the static graph structure employed in these models ignores crucial lead-lag correlations among RPPs, resulting from the time difference of the air flow at spatially dispersed RPPs. To address this problem, this paper proposes a novel dynamic graph structure using multiple temporal granularity groups (TGGs) to characterize the lead-lag correlations among RPPs. A granular-based GNN called GGNet is designed to generate an optimal adjacency matrix for the proposed graph structure. Specifically, a two-dimensional convolutional neural network (2D-CNN) is used to quantify the uncertain lead-lag correlations among RPPs; secondly, a gate mechanism is used to calculate a dynamic adjacency matrix; Finally, a graph attention network (GAT) is used to aggregate the information on RPPs based on the well-learned adjacency matrix. Case studies conducted using real-world datasets, with wind power plants and photovoltaic power plants, show our method outperforms state-of-the-art (SoTA) ones with better performance. Compared with the SoTA models, the RMSEN and MAEN of wind power plants for 1–4 h forecast steps decreased on average by 22.925% and 13.18%, respectively; the RMSEN and MAEN of photovoltaic power plants for 1–4 h forecast steps decreased on average by 48.95% and 18.75%, respectively. The results show that the proposed framework can generate improved performance with accuracy and robustness.

Suggested Citation

  • Zhu, Nanyang & Wang, Ying & Yuan, Kun & Yan, Jiahao & Li, Yaping & Zhang, Kaifeng, 2024. "GGNet: A novel graph structure for power forecasting in renewable power plants considering temporal lead-lag correlations," Applied Energy, Elsevier, vol. 364(C).
    • Handle: RePEc:eee:appene:v:364:y:2024:i:c:s0306261924005774
    DOI: 10.1016/j.apenergy.2024.123194
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    References listed on IDEAS

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    2. Alejandro Garcia-Basurto & Angel Perez-Cruz & Aurelio Dominguez-Gonzalez & Juan J. Saucedo-Dorantes, 2024. "Modeling and Prediction of Carbon Monoxide during the Start-Up in ICE through VARX Regression," Energies, MDPI, vol. 17(11), pages 1-21, May.
    3. Afshin Tatar & Amin Shokrollahi & Abbas Zeinijahromi & Manouchehr Haghighi, 2024. "Deep Learning for Predicting Hydrogen Solubility in n-Alkanes: Enhancing Sustainable Energy Systems," Sustainability, MDPI, vol. 16(17), pages 1-24, August.
    4. Yanan Xue & Jinliang Yin & Xinhao Hou, 2024. "Short-Term Wind Power Prediction Based on Multi-Feature Domain Learning," Energies, MDPI, vol. 17(13), pages 1-25, July.
    5. Yeeun Moon & Younjeong Lee & Yejin Hwang & Jongpil Jeong, 2024. "Long Short-Term Memory Autoencoder and Extreme Gradient Boosting-Based Factory Energy Management Framework for Power Consumption Forecasting," Energies, MDPI, vol. 17(15), pages 1-21, July.
    6. Haoda Ye & Qiuyu Zhu & Xuefan Zhang, 2024. "Short-Term Load Forecasting for Residential Buildings Based on Multivariate Variational Mode Decomposition and Temporal Fusion Transformer," Energies, MDPI, vol. 17(13), pages 1-22, June.

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