智慧城市作为城市新发展方向，在智能电网、智能交通、智能环境等多领域都提出了更高的需求。《智慧城市：大数据预测方法与应用(英文版)》全面介绍了智慧城市大数据预测方法的相关基本理论、关键技术和应用实例。《智慧城市：大数据预测方法与应用(英文版)》分为4篇11章，**篇介绍智慧城市的**工程，包括智能电网、智能交通、智能环境；第二篇介绍智能电网大数据预测及识别方法；第三篇介绍智能交通大数据预测方法。

Contents Part I Exordium 1 Key Issues of Smart Cities 3 1.1 Smart Grid and Buildings 3 1.1.1 Overview of Smart Grid and Building 4 1.1.2 The Importance of Smart Grid and Buildings in Smart City 5 1.1.3 Framework of Smart Grid and Buildings 6 1.2 Smart Traffic Systems 6 1.2.1 Overview of Smart Traffic Systems 6 1.2.2 The Importance of Smart Traffic Systems for Smart City 6 1.2.3 Framework of Smart Traffic Systems 8 1.3 Smart Environment 8 1.3.1 Overview of Smart Environment for Smart City 8 1.3.2 The Importance of Smart Environment for Smart City 10 1.3.3 Framework of Smart Environment 11 1.4 Framework of Smart Cities 11 1.4.1 Key Points of Smart City in the Era of Big Data 11 1.4.2 Big Data Time-series Forecasting Methods in Smart Cities 12 1.4.3 Overall Framework of Big Data Forecasting in Smart Cities 13 1.5 The Importance Analysis of Big Data Forecasting Architecture for Smart Cities 14 1.5.1 Overview and Necessity of Research 14 1.5.2 Review on Big Data Forecasting in Smart Cities 15 1.5.3 Review on Big Data Forecasting in Smart Gird and Buildings 18 1.5.4 Review on Big Data Forecasting in Smart Traffic Systems 21 1.5.5 Review on Big Data Forecasting in Smart Environment 22 References 23 Part II Smart Grid and Buildings 2 Electrical Characteristics and Correlation Analysis in Smart Grid 27 2.1 Introduction 27 2.2 Extraction of Building Electrical Features 28 2.2.1 Analysis of Meteorological Elements 29 2.2.2 Analysis of System Load 30 2.2.3 Analysis of Thermal Perturbation 31 2.3 Cross-Correlation Analysis of Electrical Characteristics 33 2.3.1 Cross-Correlation Analysis Based on MI 33 2.3.2 Cross-Correlation Analysis Based on Pearson Coefficient 35 2.3.3 Cross-Correlation Analysis Based on KendallCoefficient 37 2.4 Selection of Electrical Characteristics 40 2.4.1 Electrical Characteristics of Construction Power Grid 40 2.4.2 Feature Selection Based on Spearman Correlation Coefficient 41 2.4.3 Feature Selection Based on CFS 43 2.4.4 Feature Selection Based on Global Search-ELM 45 2.5 Conclusion 46 References 48 3 Prediction Model of City Electricity Consumption 51 3.1 Introduction 51 3.2 Original Electricity Consumption Series 54 3.2.1 Regional Correlation Analysis of Electricity Consumption Series 54 3.2.2 Original Sequences for Modeling 55 3.2.3 Separation of Sample 56 3.3 Short-Term Deterministic Prediction of Electricity Consumption Based on ARIMA Model 58 3.3.1 Model Framework of ARIMA 58 3.3.2 Theoretical Basis of ARIMA 59 3.3.3 Modeling Steps of ARIMA Predictive Model 60 3.3.4 Forecasting Results 64 3.4 Power Consumption Interval Prediction Based on ARIMA-ARCH Model 69 3.4.1 Model Framework of ARCH 69 3.4.2 The Theoretical Basis of the ARCH 69 3.4.3 Modeling Steps of ARIMA-ARCH Interval Predictive Model 70 3.4.4 Forecasting Results 71 3.5 Long-Term Electricity Consumption Prediction Based on the SARIMA Model 76 3.5.1 Model Framework of the SARIMA 76 3.5.2 The Theoretical Basis of the SARIMA 77 3.5.3 Modeling Steps of the SARIMA Predictive Model 78 3.5.4 Forecasting Results 79 3.6 Big Data Prediction Architecture of Household Electric Power 81 3.7 Comparative Analysis of Forecasting Performance 84 3.8 Conclusion 86 References 88 4 Prediction Models of Energy Consumption in Smart Urban Buildings 89 4.1 Introduction 89 4.2 Establishment of Building Simulating Model 91 4.2.1 Description and Analysis of the BEMPs 91 4.2.2 Main Characters of DeST Software 94 4.2.3 Process of DeST Modeling 95 4.3 Analysis and Comparison of Different Parameters 101 4.3.1 Introduction of the Research 101 4.3.2 Meteorological Parameters 102 4.3.3 Indoor Thermal Perturbation 103 4.3.4 Enclosure Structure and Material Performance 105 4.3.5 Indoor Design Parameters 106 4.4 Data Acquisition of Building Model 108 4.4.1 Data After Modeling 108 4.4.2 Calculation of Room Temperature and Load 108 4.4.3 Calculation of Shadow and Light 108 4.4.4 Calculation of Natural Ventilation 109 4.4.5 Simulation of the Air-Conditioning System 110 4.5 SVM Prediction Model for Urban Building Energy Consumption 110 4.5.1 The Theoretical Basis of the SVM 110 4.5.2 Steps of Modeling 112 4.5.3 Forecasting Results 114 4.6 Big Data Prediction of Energy Consumption in Urban Building 115 4.6.1 Big Data Framework for Energy Consumption 117 4.6.2 Big Data Storage and Analysis for Energy Consumption 117 4.6.3 Big Data Mining for Energy Consumption 117 4.7 Conclusion 119 References 120 Part III Smart Traffic Systems 5 Characteristics and Analysis of Urban Traffic Flow in Smart Traffic Systems 125 5.1 Introduction 125 5.1.1 Overview of Trajectory Prediction of Smart Vehicle 125 5.1.2 The Significance of Trajectory Prediction for Smart City 126 5.1.3 Overall Fr