Short-Term Power Load Forecasting of Multi-Charging Piles Based on Improved Gate Recurrent Unit

Abstract

In order to accurately predict the power consumption data of charging piles, assist related enterprises to accurately predict the benefits of charging piles and further optimize the relationship between households and transformers, this paper proposes an improved Gate Recurrent Unit (IGRU) prediction model based on spline interpolation. This method first extracts relevant data features based on Pearson correlation analysis, and then fuses feature data and performs spline interpolation input prediction network model for charging pile load pre-diction. Finally, based on the power consumption data of 174 charging piles in the jurisdiction of a provincial capital city in southwest China, the prediction accuracy of the model is verified. Through case analysis and experimental comparison, it shows that the improve gate recurrent unit (IGRU) prediction model designed in this paper is superior to the traditional deep learning prediction model in terms of stability, prediction accuracy and generalization ability.

Keywords

• Charging pile power consumption
• short-term power load forecasting
• gate recurrent unit
• transformer-customer relationship