Research on integrated intelligent energy management system based on big data analysis and machine learning

TL;DR

This paper presents a framework leveraging big data analysis and machine learning to enhance document management efficiency in integrated smart energy projects, significantly improving project control and construction efficiency.

Contribution

It introduces a novel implementation framework and machine learning methods for optimizing document management in smart energy projects, achieving over 95% accuracy with sufficient data.

Findings

Machine learning models can optimize document management efficiency.

Big data analysis improves project control and process tracking.

Model accuracy exceeds 95% with adequate training data.

Abstract

The application of big data is one of the significant features of integrated smart energy. Applying it to the file management of integrated smart energy projects is of great significance for improving the efficiency of project management and control. This article first discussed the benefits and challenges of implementing big data analysis in document management and control of integrated smart energy projects. In addition, an implementation framework for big data analysis in integrated smart energy project document management was developed, and a method for optimizing the efficiency of integrated smart energy project document management through machine learning was proposed. Using various types of data and information generated during the project document management process, the efficiency of the entire process project document control through three different machine learning methods was optimized. The result of fitting a penalty linear regression model shows that when there is enough data as a training set, the accuracy of the model achieved can reach over 95\%. By using big data analysis and machine learning to analyze the efficiency of comprehensive smart energy project document management, it is possible to track the entire process of comprehensive smart energy project documents and optimize business processes, thereby strengthening project construction control and improving project construction efficiency.