Forecasting of Short-Term Load Using the MFF-SAM-GCN Model

Yongqi Zou, Wenjiang Feng, Juntao Zhang and Jingfu Li
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Yongqi Zou: School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400030, China
Wenjiang Feng: School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400030, China
Juntao Zhang: School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400030, China
Jingfu Li: School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400030, China

Energies, 2022, vol. 15, issue 9, 1-16

Abstract: Short-term load forecasting plays a significant role in the operation of power systems. Recently, deep learning has been generally employed in short-term load forecasting, primarily in the extraction of the characteristics of digital information in a single dimension without taking into account of the impact of external variables, particularly non-digital elements on load characteristics. In this paper, we propose a joint MFF-SAM-GCN to realize short-term load forecasting. First, we utilize a Bi-directional Long Short-Term Memory (Bi-LSTM) network and One-Dimensional Convolutional Neural Network (1D-CNN) in parallel connection to form a multi-feature fusion (MFF) framework, which can extract spatiotemporal correlation features of the load data. In addition, we introduce a Self-Attention Mechanism (SAM) to further enhance the feature extraction capability of the 1D-CNN network. Then with the deployment of a Graph Convolutional Network (GCN), the external non-digital features such as weather, strength, and direction of wind, etc., are extracted. Moreover, the generated weight matrices are incorporated into the load features to enhance feature recognition ability. Finally, we exploit Bayesian Optimization (BO) to find the optimal hyperparameters of the model to further improve the prediction accuracy. The simulation is taken from our proposed model and six benchmark schemes by using the bus load dataset of the Shandong Open Data Network, China. The results show that the RMSE of our proposed MFF-SAM-GCN model is 0.0284, while the SMAPE is 9.453%,the MBE is 0.025, and R-squared is 0.989, which is better than the selected three traditional machine learning methods and the three deep learning models.

Keywords: short-term load forecasting; bi-directional long short-term memory; one-dimensional convolutional neural network; graph convolutional network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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