Data-Driven Smart Grid Optimization: Innovative Applications and Performance Graphs of Ai Algorithms in Electricity Load Forecasting

With China's economic growth and industrial upgrading, the demand for electricity has increased greatly, posing challenges to the stable operation of the power system. Power load forecasting is a core technology to ensure the safe and economic operation of the power grid, and its accuracy is crucial. The development of information technology has made data-driven smart grid optimization a hot spot, and artificial intelligence (AI) algorithms have great potential in power load forecasting. In this study, the innovative applications of AI algorithms in power load forecasting are discussed in depth, and the current research status is reviewed to analyze the challenges and opportunities. The applications of representative AI algorithms, such as neural networks, support vector machines, random forests and deep learning, in data preprocessing, feature extraction, model training and forecasting are introduced in detail. Through case studies, the advantages of AI algorithms in improving prediction accuracy and stability are demonstrated, and future research directions and challenges such as big data processing, real-time optimization of algorithms, and enhancement of model interpretability are pointed out, which will provide useful references for the safe and economic operation of power systems.