Predicting Industrial Electricity Consumption Using Industry–Geography Relationships: A Graph-Based Machine Learning Approach

Xiangpeng Zhan, Xiaorui Qian, Wei Liu, Xinru Liu, Yuying Chen, Liang Zhang (), Huawei Hong, Yimin Shen and Kai Xiao
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Xiangpeng Zhan: State Grid Fujian Marketing Service Center (Metering Center), Fuzhou 350013, China
Xiaorui Qian: State Grid Fujian Marketing Service Center (Metering Center), Fuzhou 350013, China
Wei Liu: School of Mathematical Sciences, Xiamen University, Xiamen 361005, China
Xinru Liu: School of Mathematical Sciences, Xiamen University, Xiamen 361005, China
Yuying Chen: State Grid Fujian Marketing Service Center (Metering Center), Fuzhou 350013, China
Liang Zhang: School of Mathematical Sciences, Xiamen University, Xiamen 361005, China
Huawei Hong: State Grid Fujian Marketing Service Center (Metering Center), Fuzhou 350013, China
Yimin Shen: State Grid Fujian Marketing Service Center (Metering Center), Fuzhou 350013, China
Kai Xiao: State Grid Fujian Marketing Service Center (Metering Center), Fuzhou 350013, China

Energies, 2024, vol. 17, issue 17, 1-16

Abstract: Accurately predicting industrial electricity consumption is of paramount importance for optimizing energy management and operational efficiency. Traditional forecasting approaches face significant challenges in capturing the complex factors influencing industrial electricity consumption, often due to the inadequate representation of correlations, thus limiting their predictive capabilities. To overcome these limitations, we propose a novel graph-based forecasting model termed Industry–Geography Time Series Forecasting Model (IG-TFM). Our approach leverages historical electricity consumption data and geographical information relevant to similar industries to construct an industry–geography relationship graph. This graph serves as the foundation of a comprehensive network that encompasses all industries of interest, allowing us to identify sectors closely associated with the target industry. The structured graph data are then processed within a graph convolutional neural network framework, which effectively captures the impact of geographical location, industry similarities, and inter-industry relationships on electricity consumption patterns. Utilizing this enriched representation, we develop our IG-TFM for accurate time series forecasting of industrial electricity consumption. Experiments conducted on real-world data, including 31 industries across 9 cities in a southern province of China, demonstrate the significant advantages of our proposed method across key performance indicators such as the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). These findings underscore the importance and efficacy of employing complex networks to encode sequence-related information, thereby substantially improving prediction accuracy in industrial electricity consumption forecasting.

Keywords: graph machine learning; industrial electricity consumption; relation graph; time series forecasting (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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