Enhancing Aggregate Load Forecasting Accuracy with Adversarial Graph Convolutional Imputation Network and Learnable Adjacency Matrix

Junhao Zhao, Xiaodong Shen (), Youbo Liu, Junyong Liu and Xisheng Tang
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Junhao Zhao: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Xiaodong Shen: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Youbo Liu: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Junyong Liu: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Xisheng Tang: Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China

Energies, 2024, vol. 17, issue 18, 1-28

Abstract: Accurate load forecasting, especially in the short term, is crucial for the safe and stable operation of power systems and their market participants. However, as modern power systems become increasingly complex, the challenges of short-term load forecasting are also intensifying. To address this challenge, data-driven deep learning techniques and load aggregation technologies have gradually been introduced into the field of load forecasting. However, data quality issues persist due to various factors such as sensor failures, unstable communication, and susceptibility to network attacks, leading to data gaps. Furthermore, in the domain of aggregated load forecasting, considering the potential interactions among aggregated loads can help market participants engage in cross-market transactions. However, aggregated loads often lack clear geographical locations, making it difficult to predefine graph structures. To address the issue of data quality, this study proposes a model named adversarial graph convolutional imputation network (AGCIN), combined with local and global correlations for imputation. To tackle the problem of the difficulty in predefining graph structures for aggregated loads, this study proposes a learnable adjacency matrix, which generates an adaptive adjacency matrix based on the relationships between different sequences without the need for geographical information. The experimental results demonstrate that the proposed imputation method outperforms other imputation methods in scenarios with random and continuous missing data. Additionally, the prediction accuracy of the proposed method exceeds that of several baseline methods, affirming the effectiveness of our approach in imputation and prediction, ultimately enhancing the accuracy of aggregated load forecasting.

Keywords: aggregate load forecasting; learnable adjacency matrix; data imputation; spatio-temporal correlation; graph convolutional neural 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: 2024
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