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智慧城市：大数据预测方法与应用(英文版)
  • 书号：9787030631947
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  • 外文书名：
  • 装帧：圆脊精装
    开本：B5
  • 页数：314
    字数：无
    语种：zh-Hant
  • 出版社：科学出版社
    出版时间：1900-01-01
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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。本书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。全书分为4篇11章，第一篇介绍智慧城市的重点工程，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。

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目录

  • 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 Framework of Model 127
    5.2 Traffic Flow Time Distribution Characteristics and Analysis 129
    5.2.1 Original Vehicle Trajectory Series 129
    5.2.2 Separation of Sample 131
    5.3 The Spatial Distribution Characteristics and Analysis of Traffic Flow 132
    5.3.1 Trajectory Prediction of Urban Vehicles Based on Single Data 132
    5.3.2 Trajectory Prediction of Urban Vehicles Based on Multiple Data 140
    5.3.3 Trajectory Prediction of Urban Vehicles Under EWT Decomposition Framework 146
    5.3.4 Comparative Analysis of Forecasting Performance 153
    5.4 Conclusion 156
    References 157
    6 Prediction Model of Traffic Flow Driven Based on Single Data in Smart Traffic Systems 159
    6.1 Introduction 159
    6.2 Original Traffic Flow Series for Prediction 161
    6.3 Traffic Flow Deterministic Prediction Driven by Single Data 162
    6.3.1 Modeling Process 162
    6.3.2 The Prediction Results 167
    6.4 Traffic Flow Interval Prediction Model Driven by Single Data 167
    6.4.1 The Framework of the Interval Prediction Model 167
    6.4.2 Modeling Process 170
    6.4.3 The Prediction Results 174
    6.5 Traffic Flow Interval Prediction Under Decomposition Framework 175
    6.5.1 The Framework of the WD-BP-GARCH Prediction Model 175
    6.5.2 Modeling Process 184
    6.5.3 The Prediction Results 187
    6.6 Big Data Prediction Architecture of Traffic Flow 190
    6.7 Comparative Analysis of Forecasting Performance 191
    6.8 Conclusion 193
    References 193
    7 Prediction Models of Traffic Flow Driven Basedon Multi-Dimensional Data in Smart Traffic Systems 195
    7.1 Introduction 195
    7.2 Analysis of Traffic Flow and Its Influencing Factors 196
    7.3 Elman Prediction Model of Traffic Flow Based on Multiple Data 198
    7.3.1 The Framework of the Elman Prediction Model 198
    7.3.2 Modeling Process 198
    7.3.3 The Prediction Results 201
    7.4 LSTM Prediction Model of Traffic Flow Based on Multiple Data 202
    7.4.1 The Framework of the LSTM Prediction Model 202
    7.4.2 Modeling Process 205
    7.4.3 The Prediction Results 206
    7.5 Traffic Flow Prediction Under Wavelet Packet Decomposition 207
    7.5.1 The Framework of the WPD-Prediction Model 207
    7.5.2 Modeling Process 210
    7.5.3 The Prediction Results 214
    7.6 Comparative Analysis of Forecasting Performance 216
    7.7 Conclusion 220
    References 222
    Part IV Smart Environment
    8 Prediction Models of Urban Air Quality in Smart Environment 227
    8.1 Introduction 227
    8.2 Original Air Pollutant Concentrations Series for Prediction 228
    8.2.1 Original Sequence for Modeling 228
    8.2.2 Separation of Sample 231
    8.3 Air Quality Prediction Model Driven by Single Data 232
    8.3.1 Model Framework 232
    8.3.2 Theoretical Basis of ELM 232
    8.3.3 Steps of Modeling 233
    8.3.4 Forecasting Results 233
    8.4 Air Quality Mixture Prediction Model Driven by Multiple Data 234
    8.4.1 Model Framework 234
    8.4.2 Steps of Modeling 235
    8.4.3 Forecasting Results 237
    8.5 Air Quality Prediction Under Feature Extraction Framework 238
    8.5.1 Model Framework 238
    8.5.2 Theoretical Basis of Feature Extraction Method 238
    8.5.3 Steps of Modeling 250
    8.5.4 Forecasting Results 251
    8.6 Big Data Prediction Architecture of Urban Air Quality 253
    8.6.1 The Idea of Urban Air Quality Prediction Based on Hadoop 253
    8.6.2 Parallelization Framework of the ELM 254
    8.6.3 The Parallelized ELM Under the MapReduce Framework 254
    8.7 Comparative Analysis of Forecasting Performance 256
    8.8 Conclusion 258
    References 259
    9 Prediction Models of Urban Hydrological Status in Smart Environment 261
    9.1 Introduction 261
    9.2 Original Hydrological State Data for Prediction 262
    9.2.1 Original Sequence for Modeling 262
    9.2.2 Separation of Sample 265
    9.3 Bayesian Classifier Prediction of Water Level Fluctuation 265
    9.3.1 Model Framework 265
    9.3.2 Theoretical Basis of the Bayesian Classifier 266
    9.3.3 Steps of Modeling 267
    9.3.4 Forecasting Results 268
    9.4 The Elman Prediction of Urban Water Level 269
    9.4.1 Model Framework 269
    9.4.2 The Theoretical Basis of the Elman 270
    9.4.3 Steps of Modeling 270
    9.4.4 Forecasting Results 271
    9.5 Urban River Water Level Decomposition Hybrid Prediction Model 272
    9.5.1 Model Framework 272
    9.5.2 The Theoretical Basis 272
    9.5.3 Steps of Modeling 276
    9.5.4 Forecasting Results 277
    9.5.5 Influence and Analysis of Decomposition Parameters on Forecasting Performance of Hybrid Models 280
    9.6 Comparative Analysis of Forecasting Performance 284
    9.7 Conclusion 287
    References 288
    10 Prediction Model of Urban Environmental Noise in Smart Environment 289
    10.1 Introduction 289
    10.1.1 Hazard of Noise 289
    10.1.2 The Significance of Noise Prediction for Smart City 290
    10.1.3 Overall Framework of Model 291
    10.2 Original Urban Environmental Noise Series 292
    10.2.1 Original Sequence for Modeling 292
    10.2.2 Separation of Sample 294
    10.3 The RF Prediction Model for Urban Environmental Noise 295
    10.3.1 The Theoretical Basis of the RF 295
    10.3.2 Steps of Modeling 295
    10.3.3 Forecasting Results 296
    10.4 The BFGS Prediction Model for Urban Environmental Noise 298
    10.4.1 The Theoretical Basis of the BFGS 298
    10.4.2 Steps of Modeling 299
    10.4.3 Forecasting Results 299
    10.5 The GRU Prediction Model for Urban Environmental Noise 302
    10.5.1 The Theoretical Basis of the GRU 302
    10.5.2 Steps of Modeling 303
    10.5.3 Forecasting Results 304
    10.6 Big Data Prediction Architecture of Urban Environmental Noise 305
    10.6.1 Big Data Framework for Urban Environmental Noise Prediction 307
    10.6.2 Big Data Storage for Urban Environmental Noise Prediction 308
    10.6.3 Big Data Processing of Urban Environmental Noise Prediction 308
    10.7 Comparative Analysis of Forecasting Performance 310
    10.8 Conclusion 312
    References 313
    List of Figures
    Fig.1.1 Framework of smart grid and buildings 7
    Fig.1.2 Framework of smart traffic systems 9
    Fig.1.3 Framework of smart environment 12
    Fig.1.4 Overall framework of big data forecasting in smart cities 14
    Fig.1.5 Citation report of subject retrieval on “TS=(smart cities)AND TS=(big data forecasting OR big data prediction)” 16
    Fig.1.6 The network diagram of documents based on the subject retrieval of “TS=(smart cities)AND TS=(big data forecasting OR big data prediction)” 17
    Fig.1.7 Research direction map of document based on the subject retrieval of “TS=(smart cities)AND TS=(big data forecasting OR big data prediction)” 18
    Fig.1.8 The overlay map of document based on the subject retrieval of “TS=(smart cities)AND TS=(big data forecasting OR big data prediction)” 19
    Fig.1.9 The item density map of document based on the subject retrieval of “TS=(smart cities)AND TS=(big data forecasting OR big data prediction)” 20
    Fig.1.10 The cluster density map of document based on the subject retrieval of “TS=(smart cities)AND TS=(big data forecasting OR big data prediction)” 20
    Fig.1.11 Subject retrieval of annual publication volume of various types of literature on “TS=(smart grid OR smart buildings)AND TS=(big data forecasting OR big data prediction)” 21
    Fig.1.12 Subject retrieval of annual publication volume of various types of literature on “TS=(smart traffic OR smart transportation)AND TS=(big data forecasting OR big data prediction)” 22
    Fig.1.13 Subject retrieval of annual publication volume of various types of literature on “TS=(smart environment)AND TS=(big data forecasting OR big data prediction)” 23
    Fig.2.1 Original meteorological factors series 29
    Fig.2.2 Original system load factors series 31
    Fig.2.3 Original thermal perturbation indoors series 32
    Fig.2.4 Heat map of cross-correlation result based on MI 34
    Fig.2.5 Heat map of cross-correlation result based on Pearson coefficient 36
    Fig.2.6 Heat map of cross-correlation result based on Kendall coefficient 38
    Fig.2.7 Original supply air volume series 39
    Fig.2.8 Original loop pressure loss series 39
    Fig.2.9 Original power consumption series 40
    Fig.2.10 Feature selection result based on Spearman correlation analysis 42
    Fig.2.11 Flowchart of CFS 43
    Fig.2.12 Flowchart of Global Search-ELM 46
    Fig.2.13 Feature selection result based on Global Search-ELM 47
    Fig.3.1 Long-term electricity consumption series of different regions 54
    Fig.3.2 Short-term electricity consumption series 56
    Fig.3.3 Long-term electricity consumption series 57
    Fig.3.4 Separation of short-term electricity consumption series 57
    Fig.3.5 Separation of long-term electricity consumption series 58
    Fig.3.6 Modeling process of ARIMA predictive model 59
    Fig.3.7 Short-term electricity consumption first-order difference series 61
    Fig.3.8 Long-term electricity consumption first-order difference series 61
    Fig.3.9 Short-term series calculation results of autocorrelation coefficient and the partial autocorrelation coefficient 63
    Fig.3.10 Long-term series calculation results of autocorrelation coefficient and the partial autocorrelation coefficient 63
    Fig.3.11 Short-term series forecasting results of the ARIMA models in 1-step 65
    Fig.3.12 Short-term series forecasting results of the ARIMA models in 2-step 65
    Fig.3.13 Short-term series forecasting results of the ARIMA models in 3-step 66
    Fig.3.14 Long-term series forecasting results of the ARIMA models in 1-step 67
    Fig.3.15 Long-term series forecasting results of the ARIMA models in 2-step 67
    Fig.3.16 Long-term series forecasting results of the ARIMA models in 3-step 68
    Fig.3.17 Modeling process of the ARCH predictive model 69
    Fig.3.18 Short-term series predicted residuals series 71
    Fig.3.19 Long-term series predicted residuals series 72
    List of Figures
    Fig.3.20 Short-term series interval forecasting results of the ARIMA models in 1-step 74
    Fig.3.21 Short-term series interval forecasting results of the ARIMA models in 2-step 74
    Fig.3.22 Short-term series interval forecasting results of the ARIMA models in 3-step 75
    Fig.3.23 Long-term series interval forecasting results of the ARIMA models in 1-step 75
    Fig.3.24 Long-term series interval forecasting results of the ARIMA models in 2-step 76
    Fig.3.25 Long-term series interval forecasting results of the ARIMA models in 3-step 76
    Fig.3.26 Modeling process of SARIMA predictive model 77
    Fig.3.27 Short-term series relationship between minimum BIC detection and period 79
    Fig.3.28 Long-term series relationship between minimum BIC detection and period 80
    Fig.3.29 Short-term series forecasting results of the SARIMA models in 1-step 81
    Fig.3.30 Short-term series forecasting results of the SARIMA models in 2-step 82
    Fig.3.31 Short-term series forecasting results of the SARIMA models in 3-step 82
    Fig.3.32 Long-term series forecasting results of the SARIMA models in 1-step 83
    Fig.3.33 Long-term series forecasting results of the SARIMA models in 2-step 83
    Fig.3.34 Long-term series forecasting results of the SARIMA models in 3-step 84
    Fig.3.35 The MapReduce prediction architecture of electricity consumption 85
    Fig.4.1 Connection diagram between smart city and smart building 91
    Fig.4.2 Building energy modeling programs 93
    Fig.4.3 Annual base temperature chart of 10 rooms 94
    Fig.4.4 Daily dry-bulb temperature 95
    Fig.4.5 2-D floor plan of 6 story building 96
    Fig.4.6 3-D display of 6 story building 97
    Fig.4.7 Building model plan 99
    Fig.4.8 Shadow analysis of the building model from daily and annual solar path 100
    Fig.4.9 The steps of modeling 101
    Fig.4.10 Annual hourly load in the building 102
    Fig.4.11 Annual hourly load per area in the building 103
    Fig.4.12 The temperature of the hottest month 103
    Fig.4.13 The temperature of the coldest month 104
    Fig.4.14 Annual direct solar radiation 104
    Fig.4.15 The original data of 60 days load 105
    Fig.4.16 The temperature of 60 days after adjustment of meteorological parameter 105
    Fig.4.17 Indoor thermal perturbation setting of a room in a week 106
    Fig.4.18 The temperature of 60 days after adjustment of indoor thermal perturbation 106
    Fig.4.19 The material change of the exterior wall 107
    Fig.4.20 The temperature of 60 days after adjustment of enclosure structure and material 108
    Fig.4.21 The construction of a 3-floor building with elevator 109
    Fig.4.22 The temperature of 60 days after adjustment of building construction 110
    Fig.4.23 Forecasting results of 1-step strategy 115
    Fig.4.24 Forecasting results of 2-step strategy 115
    Fig.4.25 Forecasting results of 3-step strategy 116
    Fig.4.26 The framework of Hadoop-SVM model 119
    Fig.5.1 The general framework of the chapter 128
    Fig.5.2 D1:(a)Original longitude series;(b)Original latitude series;(c)Original speed series;(d)Original angle series 129
    Fig.5.3 D2:(a)Original longitude series;(b)Original latitude series;(c)Original speed series;(d)Original angle series 130
    Fig.5.4 D3:(a)Original longitude series;(b)Original latitude series;(c)Original speed series;(d)Original angle series 130
    Fig.5.5 The prediction principle of BP network 133
    Fig.5.6 The general framework of this section 134
    Fig.5.7 Trajectory prediction results of the ELM model(single data)for D1 135
    Fig.5.8 Trajectory prediction results of the ELM model(single data)for D2 135
    Fig.5.9 Trajectory prediction results of the ELM model(single data)for D3 136
    Fig.5.10 Trajectory prediction results of the BPNN(single data)for D1 138
    Fig.5.11 Trajectory prediction results of the BPNN(single data)for D2 138
    Fig.5.12 Trajectory prediction results of the BPNN(single data)for D3 139
    Fig.5.13 The general framework of this section 140
    Fig.5.14 Trajectory prediction results of the ELM model(multiple data)for D1 141
    Fig.5.15 Trajectory prediction results of the ELM model(multiple data)for D2 142
    Fig.5.16 Trajectory prediction results of the ELM model(multiple data)for D3 142
    Fig.5.17 Trajectory prediction results of the BPNN(multiple data)for D1 144
    Fig.5.18 Trajectory prediction results of the BPNN(multiple data)for D2 144
    Fig.5.19 Trajectory prediction results of the BPNN(multiple data)for D3 145
    Fig.5.20 The general framework of this chapter 148
    Fig.5.21 Trajectory prediction results of the EWT-ELM model for D1 149
    Fig.5.22 Trajectory prediction results of the EWT-ELM model for D2 150
    Fig.5.23 Trajectory prediction results of the EWT-ELM model for D3 150
    Fig.5.24 Trajectory prediction results of the EWT-BPNN for D1 152
    Fig.5.25 Trajectory prediction results of the EWT-BPNN for D2 152
    Fig.5.26 Trajectory prediction results of the EWT-BPNN for D3 153
    Fig.6.1 Distribution of traffic flow series 161
    Fig.6.2 The input/output data structure of single data 163
    Fig.6.3 The training set and testing set 163
    Fig.6.4 The results of the 1-step prediction for the traffic flow series 164
    Fig.6.5 The results of the 2-step prediction for the traffic flow series 164
    Fig.6.6 The results of the 3-step prediction for the traffic flow series 165
    Fig.6.7 The results of the 4-step prediction for the traffic flow series 165
    Fig.6.8 The results of the 5-step prediction for the traffic flow series 166
    Fig.6.9 Comparison of prediction results based on different prediction steps 166
    Fig.6.10 Interval prediction model flow 168
    Fig.6.11 The division of datasets 169
    Fig.6.12 The results of the 1-step interval prediction for the traffic flow series 171
    Fig.6.13 The results of the 2-step interval prediction for the traffic flow series 172
    Fig.6.14 The results of the 3-step interval prediction for the traffic flow series 172
    Fig.6.15 The results of the 4-step interval prediction for the traffic flow series 173
    Fig.6.16 The results of the 5-step interval prediction for the traffic flow series 173
    Fig.6.17 The structure of the WD 175
    Fig.6.18 The prediction process of the WD-BP-GARCH model for traffic flow series 176
    Fig.6.19 The eight sub-series of the traffic flow series based on the WD 178
    Fig.6.20 The prediction results of 3-step for each sub-series based on the BP prediction model 179
    Fig.6.21 The prediction results of 3-step for each sub-series based on the BP-GARCH interval model 180
    Fig.6.22 The results of the 1-step prediction for the traffic flow series 181
    Fig.6.23 The results of the 2-step prediction for the traffic flow series 181
    Fig.6.24 The results of the 3-step prediction for the traffic flow series 182
    Fig.6.25 The results of the 4-step prediction for the traffic flow series 182
    Fig.6.26 The results of the 5-step prediction for the traffic flow series 183
    Fig.6.27 Comparison of prediction results based on different prediction steps 183
    Fig.6.28 The results of the 1-step interval prediction for the traffic flow series 185
    Fig.6.29 The results of the 2-step interval prediction for the traffic flow series 185
    Fig.6.30 The results of the 3-step interval prediction for the traffic flow series 186
    Fig.6.31 The results of the 4-step interval prediction for the traffic flow series 186
    Fig.6.32 The results of the 5-step interval prediction for the traffic flow series 187
    Fig.6.33 The MapReduce prediction architecture of traffic flow 189
    Fig.7.1 Traffic flow motion diagram 197
    Fig.7.2 The principle of the Elman network 199
    Fig.7.3 The map of Rd1, Rd2, and Rd3 200
    Fig.7.4 The sample distribution of the three traffic flow series 200
    Fig.7.5 The input/output data structure of one-step prediction 201
    Fig.7.6 The results of the 1-step prediction for the traffic flow series 202
    Fig.7.7 The results of the 2-step prediction for the traffic flow series 203
    Fig.7.8 The results of the 3-step prediction for the traffic flow series 203
    Fig.7.9 The results of the 4-step prediction for the traffic flow series 204
    Fig.7.10 The results of the 5-step prediction for the traffic flow series 204
    Fig.7.11 The principle of LSTM network 205
    Fig.7.12 The results of the 1-step prediction for the traffic flow series 206
    Fig.7.13 The results of the 2-step prediction for the traffic flow series 207
    Fig.7.14 The results of the 3-step prediction for the traffic flow series 207
    Fig.7.15 The results of the 4-step prediction for the traffic flow series 208
    Fig.7.16 The results of the 5-step prediction for the traffic flow series 208
    Fig.7.17 The structure of the WPD 209
    Fig.7.18 The principle of WPD-prediction model 210
    Fig.7.19 The sample distribution of the three traffic flow series 211
    Fig.7.20 The eight sub-series of Rd1 traffic flow series based on the WPD 212
    Fig.7.21 The eight sub-series of Rd2 traffic flow series based on the WPD 212
    Fig.7.22 The eight sub-series of Rd3 traffic flow series based on the WPD 213
    Fig.7.23 The input/output data structure of 1-step prediction 213
    Fig.7.24 The results of the 1-step prediction for the traffic flow series 215
    Fig.7.25 The results of the 2-step prediction for the traffic flow series 215
    Fig.7.26 The results of the 3-step prediction for the traffic flow series 216
    Fig.7.27 The results of the 4-step prediction for the traffic flow series 216
    Fig.7.28 The results of the 5-step prediction for the traffic flow series 217
    Fig.7.29 The results of the 1-step prediction for the traffic flow series 218
    Fig.7.30 The results of the 2-step prediction for the traffic flow series 218
    Fig.7.31 The results of the 3-step prediction for the traffic flow series 219
    Fig.7.32 The results of the 4-step prediction for the traffic flow series 219
    Fig.7.33 The results of the 5-step prediction for the traffic flow series 220
    Fig.8.1 Schematic diagram of the prediction process of the proposed prediction models 229
    Fig.8.2 Original AQI series 230
    Fig.8.3 Original air pollutant concentrations series 230
    Fig.8.4 Modeling flowchart of ELM prediction model driven by single data 232
    Fig.8.5 The forecasting results of AQI time series by ELM(single data)in 1-step 234
    Fig.8.6 The forecasting results of AQI time series by ELM(single data)in 2-step 234
    Fig.8.7 The forecasting results of AQI time series by the ELM(single data)in 3-step 235
    Fig.8.8 Modeling flowchart of the ELM prediction model driven by multiple data 236
    Fig.8.9 The forecasting results of AQI series by the ELM(multiple data)in 1-step 236
    Fig.8.10 The forecasting results of AQI series by the ELM(multiple data)in 2-step 237
    Fig.8.11 The forecasting results of AQI series by the ELM(multiple data)in 3-step 238
    Fig.8.12 Modeling flowchart of the ELM prediction model under feature extraction framework 239
    Fig.8.13 Box diagram of original feature data 241
    Fig.8.14 Box diagram of standardized feature data 241
    Fig.8.15 Identification results of PCA 243
    Fig.8.16 Sample series of selected principal components 243
    Fig.8.17 The identification results of the Gaussian KPCA 247
    Fig.8.18 Identification results of the FA 250
    Fig.8.19 Sample series of selected common factors 251
    Fig.8.20 The forecasting results of AQI time series by the PCA-ELM in 1-step 252
    Fig.8.21 The forecasting results of AQI time series by the KPCA-ELM in 1-step 252
    Fig.8.22 The forecasting results of AQI time series by the FA-ELM in 1-step 253
    Fig.8.23 Structure-based parallelized ELM 254
    Fig.8.24 The data-based parallelized ELM 255
    Fig.8.25 The forecasting results of AQI time series by proposed models in 1-step 256
    Fig.8.26 The forecasting results of AQI time series by proposed models in 2-step 257
    Fig.8.27 The forecasting results of AQI time series by proposed models in 3-step 257
    Fig.9.1 Original water level height series {X1} 263
    Fig.9.2 Original water level height series {X2} 263
    Fig.9.3 Fluctuation state of water level series {X1} 264
    Fig.9.4 Fluctuation state of water level series {X2} 264
    Fig.9.5 Modeling flowchart of Naive Bayesian classification predictor model 266
    Fig.9.6 Fluctuation trend prediction result of original water level series {X1} 268
    Fig.9.7 Fluctuation trend prediction result of original water level series {X2} 269
    Fig.9.8 Modeling flowchart of the Elman water level prediction model 269
    Fig.9.9 The forecasting results of water level time series {X1}by the Elman 271
    Fig.9.10 The forecasting results of water level time series {X2}by the Elman 272
    Fig.9.11 Modeling flowchart of the Elman water level prediction model under decomposition framework 273
    Fig.9.12 MODWT results of the original water level series {X1} 274
    Fig.9.13 The EMD results of the original water level series {X1} 275
    Fig.9.14 The SSA results of the original water level series {X1} 276
    Fig.9.15 The forecasting results of water level series {X1}by the MODWT-Elman 278
    Fig.9.16 The forecasting results of water level series {X1}by the EMD-Elman 278
    Fig.9.17 The forecasting results of water level series {X1}by the SSA-Elman 279
    Fig.9.18 Forecasting performance indices of different decomposition layer of the MODWT 281
    Fig.9.19 Forecasting performance indices of different mother wavelet of the MODWT 282
    Fig.9.20 Forecasting performance indices of different types of mother wavelet of the MODWT 282
    Fig.9.21 Forecasting performance indices of different window length of the SSA 283
    Fig.9.22 Forecasting results of water level series {X1} by optimal models in 1-step 284
    List of Figures
    Fig.9.23 Forecasting results of water level series {X1} by optimal models in 2-step 285
    Fig.9.24 Forecasting results of water level series {X1} by optimal models in 3-step 285
    Fig.10.1 The general framework of the chapter 292
    Fig.10.2 Original public noise series 293
    Fig.10.3 Original neighborhood noise series 293
    Fig.10.4 Original traffic noise series 294
    Fig.10.5 Forecasting results of the RF model for D1 296
    Fig.10.6 Forecasting results of the RF model for D2 297
    Fig.10.7 Forecasting results of the RF model for D3 297
    Fig.10.8 Forecasting results of the BFGS model for D1 300
    Fig.10.9 Forecasting results of the BFGS model for D2 301
    Fig.10.10 Forecasting results of the BFGS model for D3 301
    Fig.10.11 The structure of the GRU 303
    Fig.10.12 The framework of the GRU in the section 303
    Fig.10.13 Forecasting results of the GRU model for D1 305
    Fig.10.14 Forecasting results of the GRU model for D2 306
    Fig.10.15 Forecasting results of the GRU model for D3 306
    Fig.10.16 The framework of Spark-RF model 309
    Fig.10.17 Forecasting results of proposed models for D1 311
    Fig.10.18 Forecasting results of proposed models for D2 311
    Fig.10.19 Forecasting results of proposed models for D3 312
    List of Tables
    Table 2.1 Statistical characteristics of the original meteorological factors series 29
    Table 2.2 Statistical characteristics of the original system load factors series 31
    Table 2.3 Statistical characteristics of the original thermal perturbation indoors series 32
    Table 2.4 Cross-correlation coefficient based on MI 34
    Table 2.5 Cross-correlation coefficient based on Pearson coefficient 36
    Table 2.6 Cross-correlation coefficient based on Kendall coefficient 38
    Table 2.7 Statistical characteristics of the original electrical characteristics series 40
    Table 2.8 Spearman coefficient of feature selection 42
    Table 2.9 Correlation coefficient of 8 groups of variables 43
    Table 2.10 Calculation result based on forward selection search 44
    Table 2.11 Calculation result based on backward elimination search 44
    Table 3.1 Cross-correlation coefficient of different regions based on Pearson 55
    Table 3.2 Cross-correlation coefficient of different regions based on Kendall 55
    Table 3.3 Cross-correlation coefficient of different regions based on Spearman 55
    Table 3.4 Correlation coefficient characteristics of ARIMA model 62
    Table 3.5 The forecasting performance indices of the ARIMA model for series 1 64
    Table 3.6 The forecasting performance indices of the ARIMA model for series 2 64
    Table 3.7 The forecasting performance indices of the ARIMA-ARCH model in 1-step 72
    Table 3.8 The forecasting performance indices of the ARIMA-ARCH model in 2-step 73
    Table 3.9 The forecasting performance indices of the ARIMA-ARCH model in 3-step 73
    Table 3.10 The forecasting performance indices of the SARIMA model for series 1 80
    Table 3.11 The forecasting performance indices of the SARIMA model for series 2 81
    Table 3.12 The forecasting performance comparison of different model for series 1 86
    Table 3.13 The forecasting performance comparison of different model for series 2 86
    Table 4.1 The change of structure materials 107
    Table 4.2 Output after modeling 110
    Table 4.3 Output after room temperature calculation 111
    Table 4.4 Output after room load calculation 111
    Table 4.5 Output after calculation of building daily shadow 112
    Table 4.6 Output after calculation of building illumination 112
    Table 4.7 Output after calculation of natural ventilation 113
    Table 4.8 Output after calculation of air-conditioning scheme 113
    Table 4.9 Output after calculation of wind network 113
    Table 4.10 Output after calculation of AHC simulation 113
    Table 4.11 The forecasting performance indices of the SVM model 117
    Table 5.1 Statistical characteristics of the original series 131
    Table 5.2 The forecasting performance indices of the ELM(single data)model 137
    Table 5.3 The forecasting performance indices of the BPNN(single data)model 139
    Table 5.4 The forecasting performance indices of the ELM(multiple data)model 143
    Table 5.5 The forecasting performance indices of the BPNN(multiple data)model 146
    Table 5.6 The forecasting performance indices of EWT-ELM model 151
    Table 5.7 The forecasting performance indices of EWT-BPNN model 153
    Table 5.8 The comprehensive forecasting performance indices of the ELM models 154
    Table 5.9 The comprehensive forecasting performance indices of the BP models 155
    Table 6.1 The mathematical statistical information of original series 162
    Table 6.2 The performance estimating results of the BP prediction model 167
    Table 6.3 The performance estimating results of the interval prediction model 174
    Table 6.4 The performance estimating results of the WD-BP prediction model 184
    Table 6.5 The performance estimating results of the WD-BP-GARCH prediction model 188
    Table 6.6 The performance estimating results of the deterministic prediction models 189
    Table 6.7 The performance estimating results of the interval prediction model 190
    Table 7.1 The specific meanings of Rd1, Rd2, and Rd3 199
    Table 7.2 The performance estimating results of the Elman prediction model 205
    Table 7.3 The performance estimating results of the LSTM prediction model 209
    Table 7.4 The performance estimating results of the WPD-prediction model 217
    Table 7.5 The performance estimating results of the involved prediction models 221
    Table 8.1 Statistical characteristics of the original series 231
    Table 8.2 The forecasting performance indices of the ELM(single data)model 235
    Table 8.3 The forecasting performance indices of the ELM(multiple data)model 238
    Table 8.4 Identification results of PCA 242
    Table 8.5 The identification results of the Gaussian KPCA 246
    Table 8.6 Identification results of the FA 250
    Table 8.7 The forecasting performance indices under feature extraction framework 253
    Table 8.8 The comprehensive forecasting performance indices of proposed models 258
    Table 9.1 Statistical characteristics of the original water level series 265
    Table 9.2 The forecasting performance indices of the Elman model 273
    Table 9.3 The forecasting performance indices of three hybrid models under decomposition framework 279
    Table 9.4 The forecasting performance indices of optimal hybrid models 286
    Table 10.1 Statistical characteristics of the original series 294
    Table 10.2 The forecasting performance indices of the RF model 298
    Table 10.3 The forecasting performance indices of the BFGS model 302
    Table 10.4 The forecasting performance indices of the GRU model 307
    Table 10.5 The comprehensive forecasting performance indices of proposed models 312
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