Design of an Energy Supply and Demand Forecasting System Based on Web Crawler and a Grey Dynamic Model

Gang Lin, Yanchun Liang () and Adriano Tavares
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Gang Lin: School of Computer Science, Zhuhai College of Science and Technology, Zhuhai 519041, China
Yanchun Liang: School of Computer Science, Zhuhai College of Science and Technology, Zhuhai 519041, China
Adriano Tavares: Department of Industrial Electronics, School of Engineering, University of Minho, 4800-058 Guimares, Portugal

Energies, 2023, vol. 16, issue 3, 1-11

Abstract: An energy supply and demand forecasting system can help decision-makers grasp more comprehensive information, make accurate decisions and even plan a carbon-neutral future when adjusting energy structure, developing alternative energy resources and so on. This paper presents a hierarchical design of an energy supply and demand forecasting system based on web crawler and a grey dynamic model called GM(1,1) which covers all the process of data collection, data analysis and data prediction. It mainly consists of three services, namely Crawler Service (CS), Algorithm Service (AS), Data Service (DS). The architecture of multiple loose coupling services makes the system flexible in more data, and more advanced prediction algorithms for future energy forecasting works. In order to make higher prediction accuracy based on GM(1,1), this paper illustrates some basic enhanced methods and their combinations with adaptable variable weights. An implementation for testing the system was applied, where the model was set up for coal, oil and natural gas separately, and the enhanced GM was better with relative error about 9.18% than original GM on validation data between 2010 and 2020. All results are available for reference on adjusting of energy structure and developing alternative energy resources.

Keywords: energy supply and demand forecasting system; crawler service; algorithm service; data service; GM(1,1) (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: 2023
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