Experience you can trust.1

RECAP Technology Tour

Date: 2004

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RECAP System

KEMA’s RECAP system is used to estimate usage and cost by end

use and to develop customer-specific recommendations.

Our on-line tools can also present usage history, conduct usage

benchmarking, and perform certain rate analyses.

The recommendation can address measure retrofits, operations such

as temperature setpoint adjustments, maintenance practices, normal

and early replacement of appliances, and the promotion of programs,

events, rate options, products, and services.

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Our Robust Methodology Mature and tested methodology

– Approximately 5 million reports processed

– Uses billing and weather information

– Can estimate in absence of billing data

Fully customizable set-up values

– Can adjust values specific to regions

– Provide ongoing calibration service

Engineering models

– Beyond simple lookup of typical values

– Specific models for each end use

Regression model used to assist in estimation of weather sensitive loads

Complex statistical reconciliation of estimated results to actual bills

Automated and manual quality control

Our Robust Methodology

Cooling, Heating, and Baseload

Cooling, Heating, and Baseload

Cooling, Heating, and Baseload

Reconciliation

Billing History Data Regional Weather

Customized Engineering Set-

up Data

Customer Survey Data

Weather Analysis Appliance Models

(Engineering/Behavioral)

Regression Analysis Expected Appliance

Usage

Reconciliation

Cooling, Heating, and Baseload

Customer Report Marketing Information

Database

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Our Robust Methodology The customer survey data and temperature data are used with a set of engineering algorithms to

develop an initial estimate of usage by end use. The algorithms utilize basic engineering principles

and a large set of parameters that can be set for different clients, regions, and customer types.

The engineering estimates are just the starting point if usage history is available. RECAP also uses

a regression analysis of monthly usage, heating degree-days, and/or cooling degree-days to produce

an estimate of the weather-sensitive usage.

The regression-based estimates and the engineering-based estimates are processed through a

statistical reconciliation routine to derive an end-use breakdown of the actual household usage. The

reconciliation routine uses log normal distribution theory and professional judgment on the relative

precision of each end-use estimate.

Various quality control conditions are checked throughout the process and the user is provided with

feedback and instructions if the survey responses appear to be inconsistent with the usage data.

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Missing Values

We have developed other analysis options for situations when sufficient usage

history is not available or when the customer wants a quick analysis without

logging in.

These options include customer-provided estimates of average bill amounts

(this is less effective when the gas and electric bill is combined) or estimating

the amount of usage based on the survey and weather data.

It is also possible to offer customers the option of entering their own usage

history, although most would prefer that the utility make this information

available.