基于CEEMD-ITSA-BiLSTM组合模型的短期负荷预测

doi:10.16180/j.cnki.issn1007-7820.2024.04.005

Abstract

Abstract:

Accurate short-term load forecasting of power system is helpful to flexible planning of system resources, reasonable scheduling of units, and improvement of system operation efficiency. In view of the accuracy of load forecasting, this study proposes a short-term load forecasting model based on CEEMD-ITSA-BiLSTM (Complete Ensemble Empirical Mode Decomposition-Improved Tunicate Swarm Algorithm-Bidirectional Long Short-Term Memory). CEEMD decomposition is carried out on the time series load data to obtain several stable IMF (Intrinsic Mode Function), and BiLSTM modeling and prediction are carried out for each IMF. To improve the accuracy of BiLSTM, ITSA algorithm is used to optimize the parameters of the super parameters such as the number of hidden layer nodes, learning rate and training times of BiLSTM, and CEEMD-ITSA-BiLSTM load forecasting model is established. The simulation experiment is conducted with the actual load data, and the single BiLSTM model and the BiLSTM model optimized by different algorithms are compared. The results show that the RMSE (Root Mean Square Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) error indexes of CEEMD-ITSA-BiLSTM model are increased by 48.54%, 51.32% and 44.78%, respectively when compared with the BiLSTM model, and are significantly lower than other comparison models.

Key words: short term load forecasting, prediction accuracy, CEEMD, IMF, TSA, parameter optimization, BiLSTM neural network, error index

CLC Number:

TP18

GAO Dian, ZHANG Jing. Short-Term Load Forecasting Based on CEEMD-ITSA-BiLSTM Combined Model[J].Electronic Science and Technology, 2024, 37(4): 30-37.

Figures/Tables 8

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Table 1.

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Figure 6.

Table 2.

References 26

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算法模型	隐含层数	训练次数	学习率
GA	70	88	0.073
PSO	64	83	0.065
SOA	66	85	0.059
GWO	54	79	0.051
TSA	49	75	0.044
ITSA	44	68	0.038

算法模型	RMSE/MW	MAE/MW	MAPE/%
BiLSTM	326.43	270.15	3.35
TSA-BiLSTM	253.19	195.30	2.83
CEEMD-BiILSTM	271.50	230.19	3.06
ITSA-BiLSTM	210.99	174.82	2.26
CEEMD-GA-BiLSTM	198.69	163.64	2.31
CEEMD-PSO-BiLSTM	193.94	149.89	2.07
CEEMD-SOA-BiLSTM	201.68	162.30	2.28
CEEMD-GWO-BiLSTM	189.32	149.70	2.07
CEEMD-TSA-BiLSTM	179.33	144.59	1.93
CEEMD-ITSA-BiLSTM	167.98	131.52	1.85

Short-Term Load Forecasting Based on CEEMD-ITSA-BiLSTM Combined Model

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