Research on Power Data-Driven Water Supply Optimization Based on Multi-Objective Optimization

In this study, we propose a deep mining method for power data information based on weighted deep forest, which constructs the model through data preprocessing and feature extraction, and shows high accuracy in power load prediction and fault detection. Experimental results show that the proposed model has significant advantages over traditional machine learning models, and provides new ideas and methods for the intelligent development of the power industry. In the future, we will continue to optimize the performance of the model, expand the application field, and make greater contributions to the intelligent and sustainable development of the power industry.