Intro
Preface
Contents
Acronyms
1 Introduction
1.1 Background and Significance
1.1.1 Background
1.1.2 Significance
1.2 Current Research Status
1.2.1 Energy Plan Optimization
1.2.2 Prediction Methods and Models
1.2.3 Energy Distribution Optimization
1.3 Main Content
References
2 Energy System Analysis and Modeling
2.1 Mathematical Model of Multi-medium Energy
2.2 Energy Analysis
2.2.1 By-product Gas
2.2.2 Steam
2.2.3 Electricity
2.3 Energy System Unit Operation Model
2.3.1 Model of Energy Conversion Unit

2.3.2 Model of Energy Storage Unit
2.3.3 Model of Waste Heat Recovery Unit
2.4 Energy System Operating Condition Analysis
2.4.1 Main Process Working Conditions Analysis
2.4.2 Working Condition Analysis of Energy Conversion Equipment
2.5 Conclusion
References
3 Energy Efficiency Evaluation and Optimization Methods
3.1 Evaluation Index System of Energy Efficiency in Steel Enterprises
3.2 Comprehensive Evaluation Method Based on Combination Weighting
3.2.1 Determination of Subjective Weight
3.2.2 Determination of Objective Weight

3.2.3 Determination of Combination Weight
3.2.4 Fuzzy Comprehensive Evaluation
3.3 Example Analysis
3.3.1 Calculation of Index Weight
3.3.2 Energy Efficiency Evaluation of Iron and Steel Enterprises
3.4 Conclusion
References
4 Energy Planning Optimization of Iron and Steel Enterprises
4.1 Energy Planning Optimization Under Ordinary Working Conditions
4.1.1 Mathematical Model for Energy Planning Optimization
4.1.2 An Improved MOEA/D Optimization Method Based on the Degree of Population Evolution
4.1.3 Simulation Analysis

4.2 Energy Planning Optimization Under Multiple Working Conditions
4.2.1 Multiple Working Conditions Factors
4.2.2 Energy System Model
4.2.3 Improvement of Optimization Algorithm
4.2.4 Simulation Analysis
4.2.5 Optimal Results
4.3 Simulation Analysis
References
5 Prediction of Production and Consumption of BFG
5.1 Feature Extraction and Weight Distribution of Production and Consumption
5.1.1 Statistical Characteristics of Time Series
5.1.2 Characteristics of Multi-scale Sample Entropy
5.1.3 Feature Weight Allocation on Account of CRITIC

5.2 SOM-K-means Double Clustering Algorithm
5.2.1 K-means Algorithm
5.2.2 SOM Neural Network
5.2.3 Clustering Evaluation Index
5.2.4 Implementation of SOM-K-means Double Clustering
5.3 XGBoost Classification-Regression Prediction Model
5.3.1 The Principle of XGBoost Algorithm
5.3.2 XGBoost Classification-Regression Prediction
5.4 Simulation Example
5.4.1 Description of the Data Set
5.4.2 Construction of Prediction Model Based on Feature Clustering and XGBoost
5.4.3 Analysis of Simulation Results
5.5 Conclusion
References