BILSTM-SimAM: An improved algorithm for short-term electric load forecasting based on multi-feature

Mingju Chen; Fuhong Qiu; Xingzhong Xiong; Zhengwei Chang; Yang Wei; Jie Wu; Mingju Chen; Fuhong Qiu; Xingzhong Xiong; Zhengwei Chang; Yang Wei; Jie Wu

doi:10.3934/mbe.2024102

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 2: 2323-2343. doi: 10.3934/mbe.2024102

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BILSTM-SimAM: An improved algorithm for short-term electric load forecasting based on multi-feature

1.
School of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644002, China
2.
Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science
3.
State Grid Sichuan Electric Power Company Electric Power Scientific Research Institute, Chengdu 644002, China

Received: 30 December 2023 Revised: 30 December 2023 Accepted: 09 January 2024 Published: 15 January 2024

With the growing number of user-side resources connected to the distribution system, an occasional imbalance between the distribution side and the user side arises, making short-term power load forecasting technology crucial for addressing this issue. To strengthen the capability of load multi-feature extraction and improve the accuracy of electric load forecasting, we have constructed a novel BILSTM-SimAM network model. First, the entirely non-recursive Variational Mode Decomposition (VMD) signal processing technique is applied to decompose the raw data into Intrinsic Mode Functions (IMF) with significant regularity. This effectively reduces noise in the load sequence and preserves high-frequency data features, making the data more suitable for subsequent feature extraction. Second, a convolutional neural network (CNN) mode incorporates Dropout function to prevent model overfitting, this improves recognition accuracy and accelerates convergence. Finally, the model combines a Bidirectional Long Short-Term Memory (BILSTM) network with a simple parameter-free attention mechanism (SimAM). This combination allows for the extraction of multi-feature from the load data while emphasizing the feature information of key historical time points, further enhancing the model's prediction accuracy. The results indicate that the R² of the BILSTM-SimAM algorithm model reaches 97.8%, surpassing mainstream models such as Transformer, MLP, and Prophet by 2.0%, 2.7%, and 3.6%, respectively. Additionally, the remaining error metrics also show a reduction, confirming the validity and feasibility of the method proposed.
- VMD,
- multi-feature,
- BILSTM,
- simple attention,
- short-term load forecasting
Citation: Mingju Chen, Fuhong Qiu, Xingzhong Xiong, Zhengwei Chang, Yang Wei, Jie Wu. BILSTM-SimAM: An improved algorithm for short-term electric load forecasting based on multi-feature[J]. Mathematical Biosciences and Engineering, 2024, 21(2): 2323-2343. doi: 10.3934/mbe.2024102

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Abstract

With the growing number of user-side resources connected to the distribution system, an occasional imbalance between the distribution side and the user side arises, making short-term power load forecasting technology crucial for addressing this issue. To strengthen the capability of load multi-feature extraction and improve the accuracy of electric load forecasting, we have constructed a novel BILSTM-SimAM network model. First, the entirely non-recursive Variational Mode Decomposition (VMD) signal processing technique is applied to decompose the raw data into Intrinsic Mode Functions (IMF) with significant regularity. This effectively reduces noise in the load sequence and preserves high-frequency data features, making the data more suitable for subsequent feature extraction. Second, a convolutional neural network (CNN) mode incorporates Dropout function to prevent model overfitting, this improves recognition accuracy and accelerates convergence. Finally, the model combines a Bidirectional Long Short-Term Memory (BILSTM) network with a simple parameter-free attention mechanism (SimAM). This combination allows for the extraction of multi-feature from the load data while emphasizing the feature information of key historical time points, further enhancing the model's prediction accuracy. The results indicate that the R² of the BILSTM-SimAM algorithm model reaches 97.8%, surpassing mainstream models such as Transformer, MLP, and Prophet by 2.0%, 2.7%, and 3.6%, respectively. Additionally, the remaining error metrics also show a reduction, confirming the validity and feasibility of the method proposed.

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