Short-term Load Prediction based on CEEMDAN-Attention-BiGRU

Aiming at the core concept of green development, a short-term load prediction method based on CEEMDAN-Attention-BiGRU is proposed. Firstly, the modal decomposition of the original load sequence is carried out by using Complete EEMD with Adaptive Noise (CEEMDAN), and the modal components of each intrinsic mode function with different frequencies are predicted by Bidirectional Gate Recurrent Unit (BiGRU) based on attention mechanism with different hyperparameters. Finally, the final load prediction results are obtained by combining the prediction values through the full connection layer. The simulation results show that the method proposed in this paper can effectively improve the accuracy of short-term load prediction and has practical application value.

關鍵字

Load Prediction ； Modal Decomposition ； BiGRU ； Attention

參考文獻

KIM Y, SON H G, KIM S. Short term electricity load forecasting for institutional buildings[J]. Energy Reports, 2019, 5: 1270-1280.

Wu Z , Huang N E . ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD[J]. Advances in Adaptive Data Analysis, 2011, 1(01).

Yeh J R , Shieh J S , Huang N E . Complementary Ensemble Empirical Mode Decomposition: a Novel Noise Enhanced Data Analysis Method.[J]. Advances in Adaptive Data Analysis, 2010, 2(2).

Mandic D P , Rehman N U , Wu Z , et al. Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis[J]. IEEE Signal Processing Magazine, 2013, 30(6):74-86.

TORRES M E, COLOMINAS M A, SCHLOTTHAUER G, et al. A complete ensemble empirical mode decomposition with adaptive noise[C] // IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, 2011 :4144-4147.

延伸閱讀

Hai, N. P., Binh, T. T. K., & Tuyen, N. P. (2019). A New Prediction Model T-DGM(2,n) and Its Application. International Journal of Uncertainty and Innovation Research, 1(1), 63-76. https://www.airitilibrary.com/Article/Detail?DocID=P20190619001-201904-201906190011-201906190011-63-76
Shi, J., & Zhang, Y. (2021). Short-term Power Load Forecasting based on Machine Learning. International Core Journal of Engineering, 7(6), 333-341. https://doi.org/10.6919/ICJE.202106_7(6).0039
Islam, B., Baharudin, Z., Raza, Q., & Nallagownden, P. (2014). A Hybrid Neural Network and Genetic Algorithm Based Model for Short Term Load Forecast. Research Journal of Applied Sciences, Engineering and Technology, 7(13), 2667-2673. https://www.airitilibrary.com/Article/Detail?DocID=20407467-201404-201507060015-201507060015-2667-2673
Hung, Y. H. (2011). Load Prediction and Contract Capacity Optimization Research [master's thesis, National Taiwan University]. Airiti Library. https://doi.org/10.6342/NTU.2011.02278
Lan, Y., Xue, L., & Liao, X. (2021). Short-term Power Load Forecasting based on RBF Neural Network. International Core Journal of Engineering, 7(5), 51-59. https://doi.org/10.6919/ICJE.202105_7(5).0007

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