Short-Term Wind Power Prediction Method Based on Closed-Loop Clustering and Multi-Objective Optimization

doi:10.16183/j.cnki.jsjtu.2024.079

Abstract

Abstract:

In the field of regional short-term combined forecasting of wind power, although deep learning methods can effectively learn the predictive features of each individual model, they tend to rely heavily on the training data distribution, which results in overfitting when the sample data size is small. Additionally, although clustering methods are used to improve the accuracy of regional-level forecasting, existing methods typically aim to minimize the dissimilarity among wind farms, such as geographic dissimilarity, without considering consistency with the forecasting objectives. To address these issues, this paper proposes a short-term wind power prediction method based on closed-loop clustering and multi-objective optimization. Initially, wind farms are divided into multiple clusters by the closed-loop clustering method. For each cluster, the Bootstrap method is utilized to randomly extract N training subsets with replacement from the original dataset. Subsequently, N convolutional neural networks are trained independently using these subsets. Finally, multi-objective optimization is employed to integrate the prediction results from the N convolutional neural networks. Case studies utilizing wind farm data from Inner Mongolia Autonomous Region, China, demonstrate that the proposed method reduces root mean square error by 33.81% compared with the long short-term memory model, by 24.08% compared with the convolutional neural network-based combined forecasting model, and by 14.05% compared to predictions based on the K-means clustering method.

Key words: new energy forecasting, clustering, multi-objective optimization, deep learning

CLC Number:

TM614

GUO Qi, YAN Jun, HAO Qianpeng, HAN Dong, YANG Zhihao, YAN Xinyue, ZHANG Haipeng, LI Ran. Short-Term Wind Power Prediction Method Based on Closed-Loop Clustering and Multi-Objective Optimization[J]. Journal of Shanghai Jiao Tong University, 2026, 60(2): 246-255.

Figures/Tables 8

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预测模型	MAE	RMSE	MAPE/ %	准确率/ %	合格率/ %
预测产品1	785.06	1002.08	17.49	95.89	98.82
预测产品2	1503.47	1736.69	45.15	92.87	97.20
SVR	1257.81	1494.41	38.90	93.87	97.03
MLP	952.01	1147.00	21.75	95.29	98.58
LSTM	759.44	963.75	18.76	96.04	100.00
本文模型	553.34	637.89	14.27	97.34	100.00

模型	MAE	RMSE	MAPE/ %	准确率/ %	合格率/ %
优化法	816.70	980.80	18.64	95.97	99.10
XGBoost法	757.71	911.64	27.27	96.26	100.00
CNN组合法	732.84	840.21	20.52	96.55	100.00
本文模型	553.34	637.89	14.27	97.34	100.00

聚类方法	MAE	RMSE	MAPE/ %	准确率/ %	合格率/ %
单体法	640.96	763.060	23.50	96.87	100.00
总体法	575.55	746.204	19.82	96.93	100.00
K-means	631.86	742.160	16.46	96.95	100.00
闭环聚类	553.34	637.890	14.27	97.34	100.00