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  • 智慧城市:大数据预测方法与应用 刘辉 编 经管、励志 文轩网
  • 新华书店正版
    • 作者: 暂无著
    • 出版社: 科学出版社
    • 出版时间:2020-07-01 00:00:00
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         https://product.suning.com/0070067633/11555288247.html

     

    商品参数
    • 作者: 暂无著
    • 出版社:科学出版社
    • 出版时间:2020-07-01 00:00:00
    • 版次:1
    • 页数:314
    • 开本:其他
    • 装帧:平装
    • ISBN:9787030631947
    • 国别/地区:中国
    • 版权提供:科学出版社

    智慧城市:大数据预测方法与应用

    作  者:刘辉 编
    定  价:198
    出 版 社:科学出版社
    出版日期:2020年07月01日
    页  数:314
    装  帧:精装
    ISBN:9787030631947
    主编推荐

    内容简介

    Smart Cities: Big Data Prediction Methods and Applications is the first reference to provide a comprehensive overview of smart cities with the latest big data predicting techniques.This timely book discusses big data forecasting for smart cities. It introduces big data forecasting techniques for the key aspects (e.g., traffic, environment, building energy,green grid, etc.) of smart cities, and expnull

    作者简介

    精彩内容

    目录
    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
    ……

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