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An integrated framework of gated recurrent unit based on improved sine cosine algorithm for photovoltaic power forecasting

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  • Ma, Huixin
  • Zhang, Chu
  • Peng, Tian
  • Nazir, Muhammad Shahzad
  • Li, Yiman

Abstract

Accurate prediction of photovoltaic power is of great significance to the storage and utilization of solar power. In this research, a deep learning model for photovoltaic power prediction based on gated recurrent unit network (GRU), improved sine cosine algorithm (ISCA), and complete ensemble empirical mode decomposition (CEEMD) is proposed. Firstly, CEEMD is used to decompose the original photovoltaic data into several intrinsic mode function (IMF) components and one residual. Secondly, each sub-pattern after decomposition is processed by partial least-squares analysis (PLS). Third, the nonlinear strategy is used to improve SCA, and the Hill-climbing strategy is added to the local search part to improve the performance of the algorithm. Fourth, each sub-pattern is predicted by GRU, then the learning rate and the number of hidden layer neurons of GRU are optimized by the ISCA. Finally, the predicted results of each sub-model are combined to generate the final prediction results. In this study, the proposed model is applied to four photovoltaic power data sets, and different experimental comparison models are established. The experimental results show that the CEEMD-PLS-ISCA-GRU model in this study can obtain good prediction results in all data sets.

Suggested Citation

  • Ma, Huixin & Zhang, Chu & Peng, Tian & Nazir, Muhammad Shahzad & Li, Yiman, 2022. "An integrated framework of gated recurrent unit based on improved sine cosine algorithm for photovoltaic power forecasting," Energy, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:energy:v:256:y:2022:i:c:s0360544222015535
    DOI: 10.1016/j.energy.2022.124650
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    4. Zhang, Yue & Wang, Yeqin & Zhang, Chu & Qiao, Xiujie & Ge, Yida & Li, Xi & Peng, Tian & Nazir, Muhammad Shahzad, 2024. "State-of-health estimation for lithium-ion battery via an evolutionary Stacking ensemble learning paradigm of random vector functional link and active-state-tracking long–short-term memory neural netw," Applied Energy, Elsevier, vol. 356(C).
    5. Liu, Jincheng & Li, Teng, 2024. "Multi-step power forecasting for regional photovoltaic plants based on ITDE-GAT model," Energy, Elsevier, vol. 293(C).
    6. Suo, Leiming & Peng, Tian & Song, Shihao & Zhang, Chu & Wang, Yuhan & Fu, Yongyan & Nazir, Muhammad Shahzad, 2023. "Wind speed prediction by a swarm intelligence based deep learning model via signal decomposition and parameter optimization using improved chimp optimization algorithm," Energy, Elsevier, vol. 276(C).
    7. Ajith Gopi & Prabhakar Sharma & Kumarasamy Sudhakar & Wai Keng Ngui & Irina Kirpichnikova & Erdem Cuce, 2022. "Weather Impact on Solar Farm Performance: A Comparative Analysis of Machine Learning Techniques," Sustainability, MDPI, vol. 15(1), pages 1-28, December.

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