Power Engineering Cost Prediction Based on Data Feature Analysis and Neural Networks

In order to achieve more accurate analysis and processing of power engineering data in a shorter time frame, and to accurately predict the cost of power engineering, this study proposes a novel data processing method that integrates data mining and neural network technologies. This method firstly utilizes data mining algorithms to collect and organize historical data, then performs data preprocessing, and finally relies on neural network algorithms to construct prediction models. Based on specific application analyses of data mining and neural network technologies in the field of power engineering cost, this paper further conducts case studies to explore the effectiveness of this method in practical applications. The research results demonstrate that this integrated approach is not only applicable to different types of power engineering cost data, but also capable of providing accurate and efficient cost predictions, showing promising potential for analysis and application.