Chinese Journal of Intelligent Science and Technology ›› 2021, Vol. 3 ›› Issue (2): 202-210.doi: 10.11959/j.issn.2096-6652.202121

• Special Topic: Industrial Internet of Minds • Previous Articles     Next Articles

Power policy quantification based on PMC index model and its application in load forecasting

Tianbin LIU1, Hang ZHAO2, Chen WANG1, Hongxia YUAN2, Yinya ZHANG1, Chenxi HU2, Jinxing LI2, Tianlu GAO2, Jun ZHANG2   

  1. 1 Central China Branch of State Grid Corporation of China, Wuhan 430077, China
    2 School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
  • Revised:2021-01-15 Online:2021-06-15 Published:2021-06-01
  • Supported by:
    The Science and Technology Program of the Central China Branch of SGCC(521400180005)

Abstract:

Policy has a direct impact on power system load.In order to fully explore the relationship between policy factors and load, and improve the accuracy of load forecasting, a quantitative method of power policy based on policy modeling consistency (PMC) index was proposed and it was applied to load forecasting.Firstly, the PMC evaluation system of electric power field was established, and then the PMC index of power policy text was obtained by text mining technology.Finally, the load forecasting model based on long shrot term memory was constructed.The quantitative index of power policy, weather, date and other influencing factors were input into the model, and compared with the model without considering policy factors.The experiment shows that the load forecasting model with policy factors achieves good results.After adding policy quantitative data, the error mean absolute percentage error of load forecasting model is reduced from 1.67 to 0.98, and mean absolute error is reduced from 28.97 to 19.68, which indicates that PMC model has a certain ability of policy quantification.

Key words: power policy, PMC, policy quantification, evaluation model

CLC Number: 

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