AI Operator Evaluation Methods and Technical Research in the Power Industry

With the digital transformation of the power industry, the application of Artificial Intelligence (AI) technologies in fields such as smart grids, equipment monitoring, and load forecasting has become increasingly pervasive. As the fundamental computational units in neural networks, AI operators play a crucial role. However, existing AI operators often lack optimization tailored to the specific needs of the power industry, and their performance varies significantly across different hardware platforms. This paper proposes an AI operator evaluation method for the power industry, aimed at comprehensively assessing operators' execution performance, efficiency, power consumption, and customization capabilities on various hardware platforms, providing the power industry with efficient, stable, and scalable AI operator support. The paper first constructs a multi-dimensional evaluation framework covering key indicators such as hardware adaptability, algorithm integration capability, execution efficiency, and robustness, and evaluates the operators on various hardware platforms including GPUs, FPGAs, and ASICs. Through comparative analysis, the performance of operators in core power industry tasks, including load forecasting, fault detection, and scheduling optimization, is evaluated. Experimental results show that the optimized operators significantly improve in inference speed, power efficiency, and accuracy, better meeting the power industry's demands for real-time processing, precision, and energy efficiency. Finally, the paper discusses the importance of operator customization and scalability and outlines future directions for operator optimization and application.