Design of an energy supply and demand forecasting system based on web crawler and a grey dynamic model

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.
This research was funded by NSFC grant number 61972174, Guangdong Science and Technology Planning Project grant number 2020A0505100018, Guangdong Universities’ Innovation Team Project grant number 2021KCXTD015, Guangdong Key Disciplines Project grant number 2021ZDJS138, and 2021 University-level Teaching Quality Project grant number ZLGC20210203.