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智慧城市：大數(shù)據(jù)預(yù)測(cè)方法與應(yīng)用（英文版） 
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9 7 6 8 3 7 1 0 9 3 4 0 7 讀者對(duì)象:智慧城市、智能電網(wǎng)、智能交通和智能環(huán)境等相關(guān)領(lǐng)域的研究學(xué)者、博士研究生、高年級(jí)碩士研究生和相關(guān)部門(mén)的管理人員參考使用  智慧城市作為城市新發(fā)展方向，在智能電網(wǎng)、智能交通、智能環(huán)境等多領(lǐng)域都提出了更高的需求。本書(shū)全面介紹了智慧城市大數(shù)據(jù)預(yù)測(cè)方法的相關(guān)基本理論、關(guān)鍵技術(shù)和應(yīng)用實(shí)例。全書(shū)分為4篇11章，第一篇介紹智慧城市的重點(diǎn)工程，包括智能電網(wǎng)、智能交通、智能環(huán)境；第二篇介紹智能電網(wǎng)大數(shù)據(jù)預(yù)測(cè)及識(shí)別方法；第三篇介紹智能交通大數(shù)據(jù)預(yù)測(cè)方法。 更多科學(xué)出版社服務(wù)，請(qǐng)掃碼獲取。 
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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