Abstract: Accurate load forecasting is crucial for efficient scheduling and optimized allocation of energy resources in power systems. It can effectively reduce energy waste and improve energy utilization efficiency. By accurately predicting electricity demand, the power system can develop more optimized power generation plans, ensure supply-demand balance, and thereby reduce operating costs. This article provides an overview of traditional load forecasting methods and validates the effectiveness of a new smart power plant load forecasting technology based on big data analysis through a neural network model. By comparing and analyzing the prediction results of traditional load forecasting methods and new smart power stations, it has been confirmed that big data-driven neural network models have significant advantages in improving prediction accuracy and optimizing power system operations.