A New Hybrid Model Based on SCINet and LSTM for Short-Term Power Load Forecasting

• Published in: Energies (Basel) 2024-01, Vol.17 (1), p.95
• Main Authors: Liu, Mingping, Li, Yangze, Hu, Jiangong, Wu, Xiaolong, Deng, Suhui, Li, Hongqiao
• Format: Article
• Language: English
• Subjects: Accuracy; Alternative energy sources; Automobiles, Electric; China; complex patterns; Deep learning; dynamics; Electric power systems; Energy resources; Forecasting; Forecasting techniques; Interactive learning; long short-term memory network; Machine learning; Neural networks; Renewable resources; sample convolution and interaction network; short-term load forecasting; Time series; Trends
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17010095

A stable and reliable power system is crucial for human daily lives and economic stability. Power load forecasting is the foundation of dynamically balancing between the power supply and demand sides. However, with the popularity of renewable energy sources and electric vehicles, it still struggles to achieve accurate power load forecasting due to the complex patterns and dynamics of load data. To mitigate these issues, this paper proposes a new hybrid model based on a sample convolution and integration network (SCINet) and a long short-term memory network (LSTM) for short-term power load forecasting. Specifically, a feed-forward network (FFN) is first used to enhance the nonlinear representation of the load data to highlight the complex temporal dynamics. The SCINet is then employed to iteratively extract and exchange information about load data at multiple temporal resolutions, capturing the long-term dependencies hidden in the deeper layers. Finally, the LSTM networks are performed to further strengthen the extraction of temporal dependencies. The principal contributions of the proposed model can be summarized as follows: (1) The SCINet with binary tree structure effectively extracts both local and global features, proving advantageous for capturing complex temporal patterns and dynamics; (2) Integrating LSTM into the SCINet-based framework mitigates information loss resulting from interactive downsampling, thereby enhancing the extraction of temporal dependencies; and (3) FNN layers are strategically designed to enhance the nonlinear representations prior to feeding the load data fed into the SCINet and LSTM. Three real-world datasets are used to validate the effectiveness and generalization of the proposed model. Experimental results show that the proposed model has superior performance in terms of evaluation metrics compared with other baseline models.