Optimizing Total Factor Productivity Measurement for Power Grid Enterprises Using Machine Learning Algorithms

Total factor productivity (TFP) of power grid enterprises, as a key indicator of resource utilization efficiency and production capacity, is of great significance in promoting the high-quality development of the power industry. Traditional productivity measurement methods have limitations in data complexity, variable selection and model adaptability, making it difficult to accurately reflect the productivity level and development trend of power grid enterprises. With the continuous development of the construction of the grid enterprise think tank, its project quality management work has received more and more attention, and the application of machine learning algorithms in big data analysis provides a new optimization way for total factor productivity measurement. This paper proposes a total factor productivity measurement method for power grid enterprises based on machine learning algorithms, which optimizes the accuracy and robustness of the measurement through the processes of feature engineering, model selection and result interpretation. Through the empirical analysis of power grid enterprises, this paper verifies the effectiveness of the proposed method in improving the measurement accuracy and model adaptability, and the prospect of the application of machine learning algorithms in the productivity measurement and management of power grid enterprises.