Approach for Short-Term Power Load Prediction Utilizing the ICEEMDAN–LSTM–TCN–Bagging Model

Zheng, Guo-qiang; Kong, Ling-rui; Su, Zhong-e; Hu, Miao-sen; Wang, Guo-dong

doi:10.1007/s42835-024-02040-1

J. Electr. Eng. Technol.

2024

DOI: 10.1007/s42835-024-02040-1

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Approach for Short-Term Power Load Prediction Utilizing the ICEEMDAN–LSTM–TCN–Bagging Model

Guo-qiang Zheng,

Ling-rui Kong,

Zhong-e Su

et al.

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Enhancing streamflow prediction in the Wujiang River basin: a two-stage decomposition approach with deep learning integration

Zhao,

Zhiwen

2024

Journal of Water and Climate Change

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Global climate change and human activities have profoundly impacted the geological and hydrological processes in watersheds, increasing the challenges in streamflow prediction. In this study, we propose a streamflow prediction model based on deep learning and dual-mode decomposition specifically tailored for the Wujiang River basin, a significant tributary on the southern bank of the upper Yangtze River. This model effectively addresses the prediction error issue caused by high-frequency components through dual-mode decomposition of time-series data. The results demonstrate that employing the coupled complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)–variational mode decomposition (VMD)–temporal convolutional network (TCN)–long short-term memory network (LSTM) model reduces prediction errors by at least 35% compared to single decomposition models. Furthermore, compared to individual TCN or LSTM models, the TCN–LSTM coupled model exhibits greater stability and higher prediction accuracy during training, with reductions in mean absolute error and root mean square error by 43.13 and 24.57%, respectively. This model holds promising prospects for application and can provide crucial insights for water resource management.

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Enhancing streamflow prediction in the Wujiang River basin: a two-stage decomposition approach with deep learning integration

Zhao,

Zhiwen

2024

Journal of Water and Climate Change

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Approach for Short-Term Power Load Prediction Utilizing the ICEEMDAN–LSTM–TCN–Bagging Model

Cited by 1 publication

References 28 publications

Enhancing streamflow prediction in the Wujiang River basin: a two-stage decomposition approach with deep learning integration

Enhancing streamflow prediction in the Wujiang River basin: a two-stage decomposition approach with deep learning integration

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