顧客請求データに基づいた所有機器の予測モデリング

Abstract

This document is about Who Has What? Predictive Modeling Using Customer Billing Data

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