Short-term Wind Power Forecasts based on VMD-KPCA and CFSBOA-BiLSTM
DOI:
https://doi.org/10.62051/ijepes.v2n1.05Keywords:
Short-term Wind Power Forecasting, Variational Modal Decomposition, Kernel Principal Component Analysis, Butterfly Optimization Algorithm, Bi-directional Long Short-term MemoryAbstract
Accurate prediction of wind power is of great significance to reduce the impact of large-scale wind power connection and improve the security and stability of power grid. A short-term wind power forecasting method based on VMD-KPCA and CFSBOA-BiLSTM is proposed. Firstly, the key environmental factors restricting the volatility of wind power are fully considered, and the variational mode decomposition method is used to decompose the wind power and meteorological factors. Secondly, the kernel principal component analysis method is used to reduce the dimension and construct the sub-sequence characteristic data set. Finally, the multi-strategy improved butterfly algorithm is used to optimize the BiLSTM network hyperparameters and establish the CFSBOA-BiLSTM prediction model. The simulation analysis uses the measured data of a wind farm in northwest China. The experimental results show that the combined forecasting model can fully mine the hidden information of the data and improve the accuracy of short-term wind power forecasting. Compared with the BiLSTM model, RMSE, MAE and MAPE decreased by 60.37%, 66.6% and 67.59%, respectively.
References
Zhang Y, Sun H, Guo Y. Wind power prediction based on PSO-SVR and grey combination model[J]. IEEE Access, 2019, 7: 136254-136267.
Dong X, Sun Y, Li Y, et al. Spatio-temporal convolutional network based power forecasting of multiple wind farms[J]. Journal of Modern Power Systems and Clean Energy, 2021, 10(2): 388-398.
Foley A M, Leahy P G, Marvuglia A, et al. Current methods and advances in forecasting of wind power generation[J]. Renewable energy, 2012, 37(1): 1-8.
Gu B, Zhang T, Meng H, et al. Short-term forecasting and uncertainty analysis of wind power based on long short-term memory, cloud model and non-parametric kernel density estimation[J]. Renewable Energy. 2021, 164: 687-708.
An G, Jiang Z, Cao X, et al. Short-term wind power prediction based on particle swarm optimization-extreme learning machine model combined with AdaBoost algorithm[J]. IEEE access, 2021, 9: 94040-94052.
Tian Z, Ren Y, Wang G. Short-term wind power prediction based on empirical mode decomposition and improved extreme learning machine[J]. Journal of Electrical Engineering & Technology, 2018, 13(5): 1841-1851.
He Y, Wang Y. Short-term wind power prediction based on EEMD–LASSO–QRNN model[J]. Applied Soft Computing, 2021, 105: 107288.
Zhang Y, Zhang L, Sun D, et al. Short-Term Wind Power Forecasting Based on VMD and a Hybrid SSA-TCN-BiGRU Network[J]. Applied Sciences, 2023, 13(17): 9888.
Guo P, Qi Z, Huang W. Short-term wind power prediction based on genetic algorithm to optimize RBF neural network[C]//2016 Chinese Control and Decision Conference (CCDC). IEEE, 2016: 1220-1223.
Tuerxun W, Xu C, Guo H, et al. A wind power forecasting model using LSTM optimized by the modified bald eagle search algorithm[J]. Energies, 2022, 15(6): 2031.
Zhang Y, Zhao Y, Kong C, et al. A new prediction method based on VMD-PRBF-ARMA-E model considering wind speed characteristic[J]. Energy Conversion and Management, 2020, 203: 112254.
Wang G, Yang J, Qian Y, et al. KPCA-CCA-Based Quality-Related Fault Detection and Diagnosis Method for Nonlinear Process Monitoring[J]. IEEE Transactions on Industrial Informatics, 2022.
Ding G, Qin L. Study on the prediction of stock price based on the associated network model of LSTM[J]. International Journal of Machine Learning and Cybernetics, 2020, 11: 1307-1317.
Wang Y, Zhao Y, Addepalli S. Practical options for adopting recurrent neural network and its variants on remaining useful life prediction[J]. Chinese Journal of Mechanical Engineering, 2021, 34(3): 45-64.
Cai R, Qin B, Chen Y, et al. Sentiment analysis about investors and consumers in energy market based on BERT-BiLSTM[J]. IEEE access, 2020, 8: 171408-171415.
Arora S, Singh S. Butterfly optimization algorithm: a novel approach for global optimization[J]. SOFT COMPUTING, 2019, 23(3): 715-734.
LI S Y, HE Q, DU N S. Butterfly optimization algorithm for chaotic feedback sharing and group synergy[J]. Journal of Frontiers of Computer Science & Technology, 2022, 16(7): 1661.
Shah D, Shah T, Jamal S S. Digital audio signals encryption by Mobius transformation and Hénon map[J]. Multimedia systems, 2020, 26: 235-245.
Tanyildizi E, Demir G. Golden sine algorithm: A novel math-inspired algorithm[J]. Advances in Electrical and Computer Engineering, 2017, 17(2): 71-78.
Jebli I, Belouadha F Z, Kabbaj M I, et al. Prediction of solar energy guided by pearson correlation using machine learning[J]. Energy, 2021, 224: 120109.
Liu Y J, Fan Y F, Bai X Y, et al. Short-term prediction of wind power based on feature crossover mechanism and error compensation [J]. Journal of Electrotechnics. 2022: 1-12.
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