Application of GBDT algorithm and data fusion in power engineering big data analysis

The field of electric power engineering generates vast amounts of complex data, encompassing various aspects such as power generation, transmission, distribution, and consumption. With the rapid advancement of big data technologies, effectively managing and analyzing this data has become a critical challenge. Traditional methods of data processing often struggle to handle the scale and intricacy of power system data. This paper proposes a novel power engineering data analysis and prediction method based on the Gradient Boosting Decision Tree (GBDT) algorithm, which is particularly well-suited for large, high-dimensional datasets. By leveraging the power of GBDT, this approach aims to enhance the accuracy of predictions related to power system performance, equipment maintenance, and load forecasting. Furthermore, it highlights the potential of big data technologies to optimize operations, reduce costs, and improve the efficiency of power grids. The goal is to contribute to the digital transformation of the power industry and foster smarter, more resilient energy systems.