Efficient algorithms for data modeling and recognition of distribution networks based on random forests

The rapid development of computer technology has promoted the efficient processing and precise analysis of distribution network data. Aiming at the problem of low topological association identification efficiency in the process of distribution network data modeling and identification, a distribution network data identification model combining principal component analysis, random forest and gradient boosting decision tree is proposed in this study. The integrated processing of distribution network data and topological association identification is achieved. The results show that the research model achieved 96.72% in the data delay scenario, much higher than the comparison models. The F1 value of the research model remained above 0.908 under different data scales. However, the maximum F1 values of random forest combined with gradient boosting decision tree and public information model combined with random forest are merely 0.882 and 0.834 respectively. The research model can provide reliable technical support for data modeling and intelligent dispatching of distribution networks.