A Stacking Learning Model Based on Multiple Similar Days for Short-Term Load Forecasting

Qi Jiang, Yuxin Cheng, Haozhe Le, Chunquan Li and Peter X. Liu
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Qi Jiang: School of Information Engineering, Nanchang University, Nanchang 330031, China
Yuxin Cheng: School of Information Engineering, Nanchang University, Nanchang 330031, China
Haozhe Le: School of Information Engineering, Nanchang University, Nanchang 330031, China
Chunquan Li: School of Information Engineering, Nanchang University, Nanchang 330031, China
Peter X. Liu: School of Information Engineering, Nanchang University, Nanchang 330031, China

Mathematics, 2022, vol. 10, issue 14, 1-20

Abstract: It is challenging to obtain accurate and efficient predictions in short-term load forecasting (STLF) systems due to the complexity and nonlinearity of the electric load signals. To address these problems, we propose a hybrid predictive model that includes a sliding-window algorithm, a stacking ensemble neural network, and a similar-days predictive method. First, we leverage a sliding-window algorithm to process the time-series electric load data with high nonlinearity and non-stationarity. Second, we propose an ensemble learning scheme of stacking neural networks to improve forecasting performance. Specifically, the stacking neural networks contain two types of networks: the base-layer and the meta-layer networks. During the pre-training process, the base-layer network integrates a radial basis function (RBF), random vector functional link (RVFL), and backpropagation neural network (BPNN) to provide a robust predictive model. The meta-layer network utilizes a deep belief network (DBN) and the improved broad learning system (BLS) to enhance predictive accuracy. Finally, the similar-days prediction method is developed to extract the relationship of electric load data in different time dimensions, further enhancing the robustness and accuracy of the model. To demonstrate the effectiveness of our model, it is evaluated using real data from five regions of the United States in three consecutive years. We compare our method with several state-of-the-art and conventional neural-network-based models. Our proposed algorithm improves the prediction accuracy by 16.08%, 16.83%, and 22.64% compared to DWT-EMD-RVFL, SWT-LSTM, and EMD-BLS, respectively. Empirical results demonstrate that our model achieves better accuracy and robustness compared with the baselines.

Keywords: short-term load forecasting (STLF); stacking ensemble learning; similar-days forecasting; sliding window; broad learning system–backpropagation (BLS–BP) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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