Proposal For Customer Information Database Workshop

A Proposal to Establish an "Energy Data Clearing House"

By Michael Parti and Charles Hubay	


This proposal is being submitted jointly by Applied Econometrics, Inc. 
(AEI) and Decision Sciences Research Associates (DSRA).  We plan to merge 
our firms to combine our joint strengths in energy analysis and data base 
construction and maintenance.  Our proposal is that our merged firm will 
administer a central repository of energy-related data sets, and transfer 
these data to CPUC-authorized participants in California's competitive 
energy market.   Our motivation for this proposal is our belief that the 
development of competition in California's energy market requires fast 
and equal access to accurate information about California's energy 
consumers.  We are independent of all of the UDC and ESP retailers, the 
regulators and all the other actors in the California energy community.

We propose that the CPUC choose our merged firm to serve as an Energy 
Data Clearing House, a middle-entity between the UDCs and the other firms 
that require energy market information.  In practice, the Energy Data 
Clearing House would receive data sets from energy companies (including 
UDCs), and then proceed to clean these raw data sets, standardize them 
into a common record format, and assemble the required documentation so 
that these data could be distributed for use by analysts and market 
planners working on behalf of both UDCs and ESPs.  We believe that this 
strategy would provide a healthy and low-cost information environment 
necessary for fostering the virtues of competition in California's energy 
markets.

A very important function of the proposed Energy Data Clearing House 
would be to administer privacy rules and to document to the CPUC the 
level of customer information that is released to all market 
participants.  Centralized control and reporting of this type could serve 
to minimize the misuse of energy customer data for non-authorized 
purposes.

To obtain the initial database we propose that the UDCs transfer to the 
Energy Data Clearing House all the data sets that have been paid for by 
ratepayers.  The primary data would of course be customer billing data, 
but we propose that the UDCs should also supply various types of  
supplementary customer information, including customer-level and 
system-level demand data, specialized customer surveys obtained as part 
of equipment saturation studies or DSM evaluation efforts as well as UDC 
weather data (from permitted sources).  After an initial data collection, 
the data holdings would require periodic (perhaps quarterly or 
semiannually) updating.

The virtue of this data organization method is that the UDCs can transfer 
the required data to the  Energy Data Clearing House only once in tape 
form without meeting the sometimes difficult data request specifications 
required to transfer data to a potentially large number of data users.  
The Energy Data Clearing House would also solve the problems of data 
comparability among UDC sources, fully document the data holdings and 
then transfer these standardized data to  qualified and authorized 
parties at a low cost, maintaining the level of customer confidentiality 
as required by the rules adopted by the CPUC.

The establishment of an Energy Data Cleaning House would serve to reduce 
an unwanted responsibility for the UDCs, and would provide a fast 
turn-around time and equal access to cleaned and standardized data for 
any entity that has a need for such information, including the  UDCs, 
ESPs and the consulting firms they may engage to assist them.  In 
addition, it would eliminate any suspicion that the UDCs would take 
unfair advantage of the marketing information that they collected under a 
protected regulatory environment.  As part of this process, we propose 
that the Energy Data Clearing House would set up reporting procedures to 
document and inform the CPUC about all data requests fulfilled by the 
Energy Data Clearing House.  Making this information available to the 
CPUC and other interested parties would further act to make the 
information environment fair for all.

We feel strongly that the combined experience and data processing 
facilities proposed by the merger of our firms would be ideal for 
implementing the Energy Data Clearing House as outlined in this proposal. 
 We are very experienced in working with electric industry data sets, and 
we have the capability to maintain a very large data base of utility 
customer information as well as the necessary tape drives and other data 
transfer devices to communicate data across  a wide range of computing 
environments from mainframes to microcomputers.  In addition, we have 
extensive experience with, and commitment to, maintaining customer 
confidentiality, as required for this assignment; and we know how the 
data have to be cleaned, organized, and documented to make useful 
analytical databases.

In addition to the issue of providing fair and convenient access to 
information, the most important task will be to implement the 
CPUC-mandated levels of customer confidentiality.  We can imagine a 
system based on a hierarchy of customer information disclosure 
procedures.  This may involve providing access to customer data 
identified only by geographic area (weather zones, zip codes or census 
tracts), customer service addresses, or providing access to customer data 
with names, addresses and phone numbers, based on evidence that these 
customers have given permission for the release of these data. 

An appropriate level of secrecy for the customers who wish their 
confidentiality maintained would be that given in the section on 
Confidentiality Protections B.1. (page 5 in our copy).  This section 
stipulates that a level of secrecy of at least ten customers in each 
aggregation category would be adequate unless a particular customer's 
identity could be determined from the data.  This is similar to the 
Federal Census Bureau's Suppression Rules for censoring data that could 
possibly identify particular individuals or households.

We imagine that the Energy Data Clearing House would be funded from two 
sources:  
(1) An information distribution fee would be paid by UDCs to Energy Data 
Clearing House to be used for data standardization and documentation.  In 
return, each UDC would receive all of the UDC information in a cleaned, 
documented, standardized form that would be useful for load and marketing 
analysis.  This would be combined with a report containing summary 
statistics showing loads broken down by relevant customer 
characteristics.
(2) A fee would be paid for the sale of information to ESPs as well as 
Marketing and Energy Research firms. We propose that these fees be based 
on the number of customer records used for each data extract or 
deliverable product. This would be combined with a report containing 
summary statistics showing loads broken down by relevant customer 
characteristics.

Each summary report would also contain a complete process description 
showing the steps taken in cleaning the data set and constructing its 
elements.  The fees would be on a well-documented cost basis with a 
modest overhead charge.

The Energy Data Clearing House services would be tailored to the needs of 
the individual data user in that information would be available at any 
allowable level of aggregation, and could be processed using options 
ranging from simple summary statistics to our sophisticated econometric 
and engineering analyses.  Most important, no matter what the level of 
aggregation, the required secrecy of the individual customer information 
will be maintained as we have always maintained it during our 20 years of 
work in the energy field.

To sum up, the Energy Data Clearing House services would add value to all 
the data sets because of our sophisticated data cleaning techniques; our 
expertise with all the major mainframe and PC database packages; our 
understanding of the needs of analysts/planners; and our experience with, 
and commitment to, confidentiality.  In addition, this service could 
begin immediately since we already have experienced staff.  We do not 
have to acquire and train staff for this database construction and 
maintenance task, since this is our primary business.  Finally, since 
this is our primary business, the needs of data users come first.  We are 
prepared to expedite any requests for data.
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