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A deep neural network with two-step decomposition technique for predicting ultra-short-term solar power and electrical load

Author

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  • Udenze, Peter I.
  • Gong, Jiaqi
  • Soltani, Shohreh
  • Li, Dawen

Abstract

Solar penetration and energy consumption are rapidly increasing and transforming the modern power system due to population growth, urbanization, industrialization, and the electrification of transportation and heating. These changes bring uncertainties such as load demand dynamics and solar intermittent, which are being addressed through forecasting techniques to ensure a safe and reliable power system. However, the forecasting approach requires quality data by applying signal processing techniques to ensure accurate prediction. In this study, a two-step decomposition technique involving the denoising of the first intrinsic mode function (IMF) obtained from complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), integrated with a hybrid convolutional neural network-bidirectional long short-term memory (CNN-Bi-LSTM) model to enhance prediction accuracy has been proposed. The original time series was first decomposed into thirteen (13) IMFs, or sub-series by CEEMDAN. Frequency analysis was carried out on the generated IMFs to understand their individual characteristics and the frequency component of each IMF generated at the first stage of decomposition. Subsequently, high frequency component in the first IMF was removed differently using CEEMDAN or variational mode decomposition (VMD). The first and second stage decomposed signals are then input into the CNN-Bi-LSTM architecture, where the CNN efficiently captures local features and short-term dependencies, while the Bi-LSTM component excels at modeling long-term dependencies and temporal dynamics. Here, modeling results showed that two-step decomposition strategies improve the accuracy of the forecast significantly by reducing the uncertainties associated with solar power generation and electrical load demand. Our study showed that CEEMDAN-CEEMDAN based model is the best fit for datasets with low sampling frequency (electrical load dataset) while the CEEMDAN-VMD based model works well for datasets with both low (electrical load) and high (solar power) sampling frequencies.

Suggested Citation

  • Udenze, Peter I. & Gong, Jiaqi & Soltani, Shohreh & Li, Dawen, 2025. "A deep neural network with two-step decomposition technique for predicting ultra-short-term solar power and electrical load," Applied Energy, Elsevier, vol. 382(C).
    • Handle: RePEc:eee:appene:v:382:y:2025:i:c:s0306261924025960
    DOI: 10.1016/j.apenergy.2024.125212
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    1. van der Meer, D.W. & Widén, J. & Munkhammar, J., 2018. "Review on probabilistic forecasting of photovoltaic power production and electricity consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1484-1512.
    2. Kumari, Pratima & Toshniwal, Durga, 2021. "Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting," Applied Energy, Elsevier, vol. 295(C).
    3. Oliver Doelle & Nico Klinkenberg & Arvid Amthor & Christoph Ament, 2023. "Probabilistic Intraday PV Power Forecast Using Ensembles of Deep Gaussian Mixture Density Networks," Energies, MDPI, vol. 16(2), pages 1-17, January.
    4. Santhakumari, Manju & Sagar, Netramani, 2019. "A review of the environmental factors degrading the performance of silicon wafer-based photovoltaic modules: Failure detection methods and essential mitigation techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 83-100.
    5. Pang, Zhihong & Niu, Fuxin & O’Neill, Zheng, 2020. "Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons," Renewable Energy, Elsevier, vol. 156(C), pages 279-289.
    6. Yang, Dazhi & Wang, Wenting & Gueymard, Christian A. & Hong, Tao & Kleissl, Jan & Huang, Jing & Perez, Marc J. & Perez, Richard & Bright, Jamie M. & Xia, Xiang’ao & van der Meer, Dennis & Peters, Ian , 2022. "A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: Towards carbon neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    7. Aghaei, M. & Fairbrother, A. & Gok, A. & Ahmad, S. & Kazim, S. & Lobato, K. & Oreski, G. & Reinders, A. & Schmitz, J. & Theelen, M. & Yilmaz, P. & Kettle, J., 2022. "Review of degradation and failure phenomena in photovoltaic modules," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    8. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    9. Mehrnaz Anvari & Elisavet Proedrou & Benjamin Schäfer & Christian Beck & Holger Kantz & Marc Timme, 2022. "Data-driven load profiles and the dynamics of residential electricity consumption," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    10. Khan, Waqas & Walker, Shalika & Zeiler, Wim, 2022. "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach," Energy, Elsevier, vol. 240(C).
    11. Gangqiang Li & Huaizhi Wang & Shengli Zhang & Jiantao Xin & Huichuan Liu, 2019. "Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach," Energies, MDPI, vol. 12(13), pages 1-17, July.
    12. Karijadi, Irene & Chou, Shuo-Yan & Dewabharata, Anindhita, 2023. "Wind power forecasting based on hybrid CEEMDAN-EWT deep learning method," Renewable Energy, Elsevier, vol. 218(C).
    13. Fu, Wenlong & Zhang, Kai & Wang, Kai & Wen, Bin & Fang, Ping & Zou, Feng, 2021. "A hybrid approach for multi-step wind speed forecasting based on two-layer decomposition, improved hybrid DE-HHO optimization and KELM," Renewable Energy, Elsevier, vol. 164(C), pages 211-229.
    14. Nam, SeungBeom & Hur, Jin, 2019. "A hybrid spatio-temporal forecasting of solar generating resources for grid integration," Energy, Elsevier, vol. 177(C), pages 503-510.
    15. Fu, Yang & Ying, Feixiang & Huang, Lingling & Liu, Yang, 2023. "Multi-step-ahead significant wave height prediction using a hybrid model based on an innovative two-layer decomposition framework and LSTM," Renewable Energy, Elsevier, vol. 203(C), pages 455-472.
    16. Yang, Youlong & Che, Jinxing & Deng, Chengzhi & Li, Li, 2019. "Sequential grid approach based support vector regression for short-term electric load forecasting," Applied Energy, Elsevier, vol. 238(C), pages 1010-1021.
    17. Perera, Maneesha & De Hoog, Julian & Bandara, Kasun & Senanayake, Damith & Halgamuge, Saman, 2024. "Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data," Applied Energy, Elsevier, vol. 361(C).
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