Prediction Accuracy of Stackelberg Game Model of Electricity Price in Smart Grid Power Market Environment

Zhichao Zhang, Xue Li, Yanling Zhao, Zhaogong Zhang and Bin Li ()
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Zhichao Zhang: College of Automotive and Mechanical Engineering, Harbin Cambridge University, Harbin 150069, China
Xue Li: College of Automotive and Mechanical Engineering, Harbin Cambridge University, Harbin 150069, China
Yanling Zhao: College of Automotive and Mechanical Engineering, Harbin Cambridge University, Harbin 150069, China
Zhaogong Zhang: School of Computer and Big Data, Heilongjiang University, Harbin 150080, China
Bin Li: College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China

Energies, 2025, vol. 18, issue 3, 1-16

Abstract: With the deepening of power market reform and the increasingly fierce competition in the power market, the accurate prediction of electricity price has become an important demand for power market participants to make scientific decisions, optimize resource allocation, and reduce risks. Electricity price forecast can provide a key reference for the power market, help market participants make wise decisions, promote competition and efficient operation and cope with complex market fluctuations, provide a scientific basis for various entities to optimize resource allocation, reduce risks and improve benefits, and promote the sustainable development of the power industry. This study presents a dynamic retail price prediction method for smart grid based on the Stackelberg game model. Firstly, the correlation test is used to verify the strong correlation between electric load and electricity price. Secondly, the parameters of the Stackelberg model are determined, and the load and electricity price are tested using the white noise test. Finally, by comparing the BP neural network model and quantifying the model parameters, the superiority of the model is verified. The results show that the Stackelberg game model has higher prediction accuracy than the BP neural network model in electricity price prediction.

Keywords: smart grid; blockchain; electricity price forecasting; Stackelberg model (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: 2025
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