the-infoshop.com - The vertical markets research portal
View CartView Cart
Global Information, Inc.
US: +1-860-674-8796
EU: +32-2-535-7543
SG: +65-6223-2436
  Home | Category | Publishers | Custom Research | E-mail Alert | About Us | Contact Us | Site Map |
 

* View All Categories
Geothermal Power Market Research Reports
View Conferences
Japanese Korean Chinese

Market Research Report

Who Has What? Predictive Modeling Using Customer Billing Data

Published by Energy Insights Contact us : +1-860-674-8796
Published 2007/08 Content info Pages: 26
Product code ENER55646
Price From  US $ 4500 Order/Price list
US $ 4500 PDF by E-mail (Single User License)
Delivery Time
PDF by E-Mail
Approx. 1-2 business days
Hard Copy/CD-ROM
Approx. 3-4 business days
If you need expedited delivery, please call us.
Description TOC

Table of Contents

  • Table of Contents
  • Energy Insights Opinion
  • In This Report
  • Situation Overview
    • Applications
    • Method Specifics
      • Predictive Modeling Techniques
        • Logistic Regression
        • Discriminant Analysis
        • CHAID
        • Table: Summary of Predictive Modeling Techniques
      • Accuracy and Error Rates
      • Figure: Type I and Type II Errors
      • General Comments About Modeling
    • Case Study 1: San Diego Gas & Electric and Central AC
      • Background and Setup
      • Logistic Regression Results
      • Table: Summary of Logistic Regression Models Predicting the Presence of Central AC: SDG&E Case Study
      • Figure: Predicted Error Rates and Accuracy for Central AC Using Logistic Regression: SDG&E Case Study
      • Table: Comparison of Logistic Regression Results with Classification Probabilities of 0.5 and 0.4: SDG&E Case Study
      • Discriminant Analysis Results
      • Table: Summary of Discriminant Analysis Models Predicting the Presence of Central AC: SDG&E Case Study
      • CHAID Results
      • Figure: Partial CHAID Tree for Central AC Model: SDG&E Case Study
      • Comparison of Methods for Central AC Data
      • Table: Comparison of Logistic Regression, Discriminant Analysis, and CHAID Models Predicting the Presence of Central AC: SDG&E Case Study
      • Follow-Up Real-World Application
    • Case Study 2: Alliant Energy and Electric Heat
      • Background and Setup
        • Defining the Criterion Variable
      • Overall Modeling Approach
      • Logistic Regression Results
      • Table: Logistic Regression and Discriminant Analysis Models Predicting the Presence of Electric Heat: Alliant Energy Case Study
      • Figure: Predicted Error Rates and Accuracy for Electric Heat Using Logistic Regression: Alliant Energy Case Study
      • Discriminant Analysis Results
      • CHAID Results
      • Figure: CHAID Tree for Electric Heat Model: Alliant Energy Case Study
      • Comparison of Methods for Electric Heat Data
    • Lessons Learned
  • Future Outlook
  • Essential Guidance
    • Actions to Consider
  • Learn More
    • Related Research
    • Synopsis
Related Report
Back to Top
Please inform me when related publications are released
InfoWatch

US: 1-860-674-8796 EU: 32-2-535-7543 SG: 65-6223-2436
The vertical markets research portal
© 2009, the-infoshop.com by Global Information, Inc. All rights reserved.