Load prediction for island microgrids based on evaluation factor reconstruction and DECN-BiGRU

doi:10.3969/j.issn.2097-0706.2026.01.009

Abstract

Abstract:

Aiming at the strong nonlinearity， non-stationarity， and multi-source coupling characteristics of island microgrid loads， a load prediction method was proposed， integrating robust empirical mode decomposition （REMD） based on evaluation factor reconstruction with detail-enhanced convolutional network （DECN） and bidirectional gated recurrent unit （BiGRU）. Multi-scale feature decoupling was achieved through REMD and evaluation factor reconstruction. A DECN-BiGRU hybrid architecture was constructed to fuse local differences and global dependency features， and multi-task learning was introduced to optimize the coupling relationships among components. Experiments showed that the model reduced the mean absolute percentage error by 68.78% compared with traditional methods and reduced the mean absolute error by 68.97% compared with deep learning models， thereby verifying the effectiveness of multi-modal feature fusion and bidirectional modeling. The research findings provide reference for power scheduling and energy storage configuration in island microgrids.

Key words: island microgrid, load prediction, robust empirical mode decomposition, detail-enhanced convolutional network, bidirectional gated recurrent unit, evaluation factor reconstruction, multi-task learning, energy storage

CLC Number:

TK01：TM7

LIANG Fuguang, MA Zhongqiang. Load prediction for island microgrids based on evaluation factor reconstruction and DECN-BiGRU[J]. Integrated Intelligent Energy, 2026, 48(1): 85-97.

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超参数	数值	超参数	数值
训练轮次	200	批次大小	128
步长(窗口大小)	24	学习率	0.001
优化器	Adam	损失函数	RMSE
卷积核	3×3	DECN层数	1
BiGRU层数	1	BiGRU隐藏层大小	128
全连接层大小	128	随机失活(Dropout)	0.2

分量	MSE	ZCR	组合因子
IMF 1	0.803	0.316	0.706
IMF 2	0.690	0.116	0.575
IMF3	0.750	0.059	0.612
IMF 4	0.582	0.026	0.471
IMF 5	0.442	0.013	0.356
IMF 6	0.365	0.006	0.293
IMF 7	0.198	0.003	0.159
IMF 8	0.080	0.001	0.064

项目	重构分量1	重构分量2	重构分量3
MSE	1.090	0.665	0.215
ZCR	0.164	0.015	0.003

分解重构方式	MAPE/%	MAE/kW	RMSE/kW
未分解	2.27	14.69	21.60
仅分解	1.80	11.55	16.77
组合因子分解重构	1.18	7.37	9.69

预测模型	MAPE/%	MAE/kW	RMSE/kW
ARIMA	3.78	23.75	30.64
SVR	3.16	20.07	27.06
CNN-LSTM	3.07	20.09	27.70
DECN	2.61	16.83	22.66
BiGRU	2.04	13.08	18.52
DECN-BiGRU	1.18	7.37	9.69