智慧城市--大数据预测方法与应用 pdf下载

基本信息

• 商品名称：智慧城市--大数据预测方法与应用(英文版)(精)
• 作者：刘辉
• 定价：198
• 出版社：科学
• 书号：9787030631947

其他参考信息（以实物为准）

• 出版时间：2020-01-01
• 印刷时间：2020-01-01
• 版次：1
• 印次：1
• 开本：16开
• 包装：精装
• 页数：314

目录

Contents
Part I Exordium
1 Key Issues of Smart Cities
1.1 Smart Grid and Buildings
1.1.1 Overview of Smart Grid and Building
1.1.2 The Importance of Smart Grid and Buildings in Smart City
1.1.3 Framework of Smart Grid and Buildings
1.2 Smart Traffic Systems
1.2.1 Overview of Smart Traffic Systems
1.2.2 The Importance of Smart Traffic Systems for Smart City
1.2.3 Framework of Smart Traffic Systems
1.3 Smart Environment
1.3.1 Overview of Smart Environment for Smart City
1.3.2 The Importance of Smart Environment for Smart City
1.3.3 Framework of Smart Environment
1.4 Framework of Smart Cities
1.4.1 Key Points of Smart City in the Era of Big Data
1.4.2 Big Data Time-series Forecasting Methods in Smart Cities
1.4.3 Overall Framework of Big Data Forecasting in Smart Cities
1.5 The Importance Analysis of Big Data Forecasting Architecture for Smart Cities
1.5.1 Overview and Necessity of Research
1.5.2 Review on Big Data Forecasting in Smart Cities
1.5.3 Review on Big Data Forecasting in Smart Gird and Buildings
1.5.4 Review on Big Data Forecasting in Smart Traffic Systems
1.5.5 Review on Big Data Forecasting in Smart Environment
References
Part II Smart Grid and Buildings
2 Electrical Characteristics and Correlation Analysis in Smart Grid
2.1 Introduction
2.2 Extraction of Building Electrical Features
2.2.1 Analysis of Meteorological Elements
2.2.2 Analysis of System Load
2.2.3 Analysis of Thermal Perturbation
2.3 Cross-Correlation Analysis of Electrical Characteristics
2.3.1 Cross-Correlation Analysis Based on MI
2.3.2 Cross-Correlation Analysis Based on Pearson Coefficient
2.3.3 Cross-Correlation Analysis Based on KendallCoefficient
2.4 Selection of Electrical Characteristics
2.4.1 Electrical Characteristics of Construction Power Grid
2.4.2 Feature Selection Based on Spearman Correlation Coefficient
2.4.3 Feature Selection Based on CFS
2.4.4 Feature Selection Based on Global Search-ELM
2.5 Conclusion
References
3 Prediction Model of City Electricity Consumption
3.1 Introduction
3.2 Original Electricity Consumption Series
3.2.1 Regional Correlation Analysis of Electricity Consumption Series
3.2.2 Original Sequences for Modeling
3.2.3 Separation of Sample