Load prediction of urban gas based on Attention-GRU model

Abstract

With the improvement of pricing mechanism and the advancement of supply-storage-sale system reform for natural gas, many opportunities and challenges are brought in the process of urban gas procurement, transport channels and user balance. Therefore, the analysis and prediction of natural gas consumption is of great significance to the construction of urban energy security system. According to the “supply-storage-sale” plan management system of natural gas, the accurate gas prediction model for the gas supply and consumption sides was specially studied, a neural network prediction model based on time series characteristics in combination with the feature combination of Attention + GRU model was proposed,and a gas load prediction model with wider application range and higher prediction accuracy with the GRU algorithm and Attention mechanism fused was also established. In addition, the algorithm model of cyclic neural network GRU in combination with Attention was applied to urban gas load prediction for the first time, showing better prediction effect than other algorithm models, and it could provide support for enhancing the stable supply of natural gas in the region.

Keywords

• urban gas
• gas load
• prediction
• neural network
• gru model
• attention mechanism