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Who Has What? Predictive Modeling Using Customer Billing Data

Published by: IDC

Published: Aug. 21, 2007 - 26 Pages


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

Abstract

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

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