1. Introduction
Load monitoring is an important part of energy management [1]. Since its introduction, the research focus of load monitoring has changed from performance improvement based on artificial intelligence methods to practical deployment based on optimization methods [2]. However, practical deployment of load monitoring is not as simple and efficient as theoretical research, and practical deployment needs to consider practical issues. First, the number of parameters and computation volume of the load monitoring model become important bottlenecks, and the excessive number of parameters and complex computation volume will cause a great burden to the deployed system, which puts forward higher requirements to the system. Second, the performance of the load monitoring model should be good enough, and the deployed load monitoring model will run for a long time and be updated very infrequently, which should ensure that the deployed model has the optimal performance. Finally, the deployed load monitoring model should be Finally, the deployed load monitoring model should balance the number of model parameters, the amount of computation and the model accuracy, and the less parameters and computation cause the model accuracy to drop sharply is to be avoided. Therefore, the actual deployment of load monitoring becomes a key issue to maintain the model performance and minimize the parameters and calculations.