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Big Data Analytics Strategies for the Smart Grid-[2014]-[pdf]-[Carol L. Stimmel]

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    发表于 2017-11-20 14:40:18 | 显示全部楼层 |阅读模式
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    书籍信息:
    标题: Big Data Analytics Strategies for the Smart Grid
    语言: English
    格式: pdf
    大小: 9.3M
    页数: 258
    年份: 2014
    作者: Carol L. Stimmel
    出版社: Auerbach Publications

    简介

    By implementing a comprehensive data analytics program, utility companies can meet the continually evolving challenges of modern grids that are operationally efficient, while reconciling the demands of greenhouse gas legislation and establishing a meaningful return on investment from smart grid deployments.

    Readable and accessible, Big Data Analytics Strategies for the Smart Grid addresses the needs of applying big data technologies and approaches, including Big Data cybersecurity, to the critical infrastructure that makes up the electrical utility grid. It supplies industry stakeholders with an in-depth understanding of the engineering, business, and customer domains within the power delivery market.

    The book explores the unique needs of electrical utility grids, including operational technology, IT, storage, processing, and how to transform grid assets for the benefit of both the utility business and energy consumers. It not only provides specific examples that illustrate how analytics work and how they are best applied, but also describes how to avoid potential problems and pitfalls.

    Discussing security and data privacy, it explores the role of the utility in protecting their customers’ right to privacy while still engaging in forward-looking business practices. The book includes discussions of:

    - SAS for asset management tools
    - The AutoGrid approach to commercial analytics
    - Space-Time Insight’s work at the California ISO (CAISO)

    This book is an ideal resource for mid- to upper-level utility executives who need to understand the business value of smart grid data analytics. It explains critical concepts in a manner that will better position executives to make the right decisions about building their analytics programs.

    At the same time, the book provides sufficient technical depth that it is useful for data analytics professionals who need to better understand the nuances of the engineering and business challenges unique to the utilities industry.


    目录
    THE TRANSFORMATIVE POWER OF DATA ANALYTICS

    Putting the Smarts in the Smart Grid
    Chapter Goal
    The Imperative for the Data-Driven Utility
    Big Data: We’ll Know It When We See It
    What Are Data Analytics?
         The Data Analytics Infrastructure
    Starting from Scratch
         Mind the Gap
         Culture Shift
         A Personal Case Study
         Ouija Board Economics
         Business as Usual Is Fatal to the Utility
         To Be or Not to Be
    Finding Opportunity with Smart Grid Data Analytics

    Building the Foundation for Data Analytics
    Chapter Goal
    Perseverance Is the Most Important Tool
         "It’s Too Hard" Is Not an Answer
    Building the Analytical Architecture
         The Art of Data Management
         Managing Big Data Is a Big Problem
         The Truth Won’t Set You Free
         One Size Doesn’t Fit All
         Solving the "Situation-Specific" Dilemma
         The Build-Versus-Buy War Rages On
          When the Cloud Makes Sense
         Change Is Danger and Opportunity

    Transforming Big Data for High-Value Action
    Chapter Goal
    The Utility as a Data Company
         Creating Results with the Pareto Principle
    Algorithms
         The Business of Algorithms
         Data Classes
         Just in Time
    Seeing Intelligence
         Remember the Human Being
         The Problem with Customers
         The Transformation of the Utility
         Bigger Is Not Always Better
    Assessing the Business Issues
         Start with a Framework

    THE BENEFITS OF SMART GRID DATA ANALYTICS

    Applying Analytical Models in the Utility
    Chapter Goal
    Understanding Analytical Models
         What Exactly Are Models?
         Warning: Correlation Still Does Not Imply Causation
    Using Descriptive Models for Analytics
    Using Diagnostic Models for Analytics
         How Diagnostic Tools Help Utilities
    Predictive Analytics
    Prescriptive Analytics
    An Optimization Model for the Utility
    Toward Situational Intelligence

    Enterprise Analytics
    Chapter Goal
    Moving Beyond Business Intelligence
         Energy Forecasting
         Asset Management
         Demand Response and Energy Analytics
         Dynamic-Pricing Analytics
         Revenue-Protection Analytics
         Breaking Down Functional Barriers

    Operational Analytics
    Chapter Goal
    Aligning the Forces for Improved Decision-Making
    The Opportunity for Insight
         Adaptive Models
    Focus on Effectiveness
         Visualizing the Grid
         Distributed Generation Operations: Managing the Mix-Up
    Grid Management
         The Relationship Between Standards and Analytics
    Resiliency Analytics
    Extracting Value from Operational Data Analytics

    Customer Operations and Engagement Analytics
    Chapter Goal
    Increasing Customer Value
         Customer Service
         Advanced Customer Segmentation
         Sentiment Analysis
         Revenue Collections
         Call Center Operations
         Utility Communications
    What’s in It for the Customer?
         Enhanced Billing and Customer-Facing Web Portals
         Home Energy Management
         Strategic Value

    Analytics for Cybersecurity
    Chapter Goal
    Cybersecurity in the Utility Industry
         The Threat Against Critical Infrastructure
         How the Smart Grid Increases Risk
         The Smart Grid as Opportunity for Dark Mischief
    The Role of Big Data Cybersecurity Analytics
         Predict and Protect
         Cybersecurity Applications
         Proactive Approaches
         Global Action for Coordinated Cybersecurity
         The Changing Landscape of Risk

    IMPLEMENTING DATA ANALYTICS PROGRAMS FOR SUSTAINED CHANGE

    Sourcing Data
    Chapter Goal
    Sourcing the Data
         Smart Meters
         Sensors
         Control Devices
         Intelligent Electronic Devices
         Distributed Energy Resources
         Consumer Devices
         Historical Data
         Third-Party Data
    Working with a Variety of Data Sources
         Data Fusion

    Big Data Integration, Frameworks, and Databases
    Chapter Goal
    This Is Going to Cost
    Storage Modalities
         Hyperscale
         Network-Attached Storage
         Object Storage
    Data Integration
    The Costs of Low-Risk Approaches
    Let the Data Flow
         Hadoop
         MapReduce
         Hadoop Distributed File System
         How Does This Help Utilities?
    Other Big Data Databases
         NoSQL
         In-Memory or Main Memory Databases
         Object-Oriented Database Management Systems
         Time Series Database Servers
         Spatial and GIS Databases
    The Curse of Abundance

    Extracting Value
    Chapter Goal
    We Need Some Answers Here
         How Long Does This Take?
    Mining Data for Information and Knowledge
    The Process of Data Extraction
         When More Isn’t Always Better
          Running for Performance
         Hadoop: A Single-Purpose Batch-Data Platform?
    Stream Processing
         Complex Event Processing
         Process Historians
    Avoid Irrational Exuberance

    Envisioning the Utility
    Chapter Goal
    Big Data Comprehension
    Why Humans Need Visualization
         Walking Toward the Edge
    The Role of Human Perception
         Preattentive Processing
    The Utility Visualized
         Advancing Business Intelligence
         High-Impact Operations
         Improving Customer Value
    Making Sense of It All

    A Partnership for Change
    Chapter Goal
    With Big Data Comes Big Responsibility
         Abandon All Hope, Ye Who Enter Here?
    Privacy, Not Promises
         Consent
         Data Management
         Governance
    Privacy Enhancement
         Enabling Consent
         Data Minimization
         The Role of Metadata
    The Utility of the Future Is a Good Partner

    Glossary

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