Data Analysis and Intelligent Scheduling of Power Customer Service Based on Artificial Intelligence

With the increasing complexity of power demand forecasting, traditional methods face challenges in handling multiple influencing factors. This paper proposes a power load forecasting model based on the Temporal Fusion Transformer (TFT) to improve the accuracy and stability of power demand forecasting. By introducing a variable self-attention mechanism and gating mechanism, the TFT model effectively captures long-and short-term dependencies, while considering external factors such as weather and holidays that affect power demand. The study further enhances the model's forecasting capability and robustness through optimization strategies like multi-step forecasting, deep feature crossing, and model fusion. Experimental results show that the optimized TFT model demonstrates outstanding performance in power load forecasting, providing strong support for intelligent power scheduling.