7-i

CHAPTER 7. ENERGY USE ANALYSIS

TABLE OF CONTENTS

7.1 INTRODUCTION ........................................................................................................... 7-1

7.2 LOAD MODEL FOR LIQUID-IMMERSED DISTRIBUTION 

TRANSFORMERS .......................................................................................................... 7-2

7.2.1 Inputs to Hourly Load Model .......................................................................................... 7-3

7.2.1.1 Utility Information ......................................................................................... 7-3

7.2.1.2 Initial Peak Distribution Transformer Loading.............................................. 7-5

7.2.1.3 Hourly Price-Load Model .............................................................................. 7-5

7.2.1.4 Distribution Transformer Load Simulation ................................................... 7-7

7.3 MODEL FOR DRY-TYPE DISTRIBUTION TRANSFORMER LOADS .................... 7-8

7.3.1 Overview of Monthly Load Model .................................................................................. 7-9

7.3.2 Monthly Load Simulation ................................................................................................ 7-9

7.3.3 Inputs to Monthly Load Model ...................................................................................... 7-10

7.3.3.1 Customer Data ............................................................................................. 7-10

7.3.3.2 Initial Peak Distribution Transformer Load ................................................. 7-10

7.3.3.3 Distribution Transformer Loss Factor ......................................................... 7-11

7.3.3.4 Coincident Peak Load .................................................................................. 7-12

LIST OF TABLES

Table 7.2.1 Definition of Electricity Market Module Regions in the National Energy 

Modeling System ................................................................................................. 7-4

LIST OF FIGURES 

Figure 7.2.1 Electricity Market Module Regions in the National Energy Modeling 

System .................................................................................................................. 7-4

Figure 7.2.2 Distribution of Initial Peak Loading Used in the Hourly Load Analysis ............ 7-5

Figure 7.2.3 Binned Load Versus Marginal Price for Electricity Market Module Region 

Southeastern Electric Reliability Council ............................................................ 7-6

Figure 7.2.4 Average Joint Probability Distribution for Commercial Customers, 1998-

2000...................................................................................................................... 7-8

Figure 7.2.5 Average Joint Probability Distribution for Industrial Customers, 1998-

2000...................................................................................................................... 7-8
7-1

CHAPTER 7. ENERGY USE ANALYSIS

7.1 INTRODUCTION

The U.S. Department of Energy (DOE) characterized energy use and end-use load for 

distribution transformers. Estimates of energy use enabled evaluation of energy savings 

associated with operating distribution transformers at various efficiency levels. The 

characterization of end-use load enabled evaluation of the impact of load on electricity demand. 

DOE’s analysis produced a distribution of results for a range of installation types, operating 

conditions, and climate locations intended to represent the diversity of the application and 

performance of distribution transformers.

 Distribution transformers consume energy via both no-load losses and load losses. Noload losses, which are constant over time, occur whenever a transformer is energized by power 

lines. Load losses vary with the square of the load the transformer is serving. There are two types 

of distribution transformers: liquid-immersed and dry. Liquid-immersed transformers are owned 

primarily by electric utilities. Utilities pay marginal costs for the power used to generate 

electricity, costs that can vary by the hour. DOE therefore developed a statistical simulation 

model to estimate the hourly load characteristics of liquid-immersed transformers and to develop 

a correlation between hourly loads and system loads. Dry-type transformers are owned by 

commercial and industrial (C&I) establishments, which are billed for electricity according to a 

tariff. For dry-type distribution transformers, DOE used empirical estimates of load 

characteristics to estimate monthly average (root mean square) loads and peak coincident loads. 

This chapter first describes transformer losses and then presents the details of the load 

characterization models DOE that developed for liquid-immersed and dry-type distribution 

transformers. 

 The no-load losses experienced by distribution transformers arise primarily from the 

switching of the magnetic field in the transformer core material. Those losses, which are roughly 

constant, occur whenever the transformer is energized (i.e., connected to a live power line). Load 

losses, also known as resistance or I2

R losses, vary in response to the changing load on the 

transformer. Load losses are proportional to the load squared plus a relatively small temperature 

correction (<15 percent for loads less than the rated load). DOE uses the following formula, 

which incorporates both load and no-load losses, to estimate the energy used by a distribution 

transformer. 

ET =∈NLL+ E +∈LL �E

Emax � �

2

 

Where: 

∈NLL = the no-load loss rate,

E = the total energy used by a transformer experiencing instantaneous load,

∈LL = the load loss rate, and 

Emax = the expected peak load on the transformer. 
7-2

The characteristics of distribution transformer loads required for DOE’s life-cycle cost 

(LCC) analysis also depend on the way the user’s electricity is priced. Because approximately 95 

percent of liquid-immersed distribution transformers are owned by electric utilities, the 

appropriate electricity price for those transformers is the cost of production, which varies hourly. 

For those types of transformers, DOE’s analysis was based on hourly load and price data. The 

electricity use of dry-type distribution transformers, which are installed primarily in commercial 

and industrial buildings, is billed monthly. For those types of transformers, DOE developed an 

analysis based on monthly, building-level data. 

7.2 LOAD MODEL FOR LIQUID-IMMERSED DISTRIBUTION 

TRANSFORMERS 

 This section describes the hourly load model DOE developed in support of its LCC 

analysis for liquid-immersed distribution transformers. 

The operating cost savings associated with improved transformer efficiency are equal to 

the energy savings (reduction in losses) times the price of energy. For liquid-immersed 

distribution transformers, the appropriate price is the marginal production cost of electricity. This 

production cost, which varies regionally and temporally, correlates strongly with the magnitude 

of the total electric system load. Because the load on an individual transformer also correlates 

somewhat with system load, there is some correlation between transformer load losses and the 

price of electricity. To capture those correlations, DOE developed a statistical model based on

hourly electric system load data, marginal hourly electric system production prices, and a joint 

probability distribution between transformer and system load levels. The steps in the operation of 

the hourly load simulation program are described below. 

1. The program selects a transformer owner from a list of utilities that own electricity 

distribution equipment. 

2. The program determines a sample weight for the selected utility, based on total 

kilowatt-hours sold. 

3. The program selects the customer type (residential or C&I) served by the transformer 

and the appropriate sample weight for that customer type. The weight is assigned 

based on the fraction of that utility’s electricity sales to that customer type. 

4. The program goes through a loop to calculate the hourly transformer loads and 

system marginal prices for the selected transformer. System prices, and their 

dependence on system load, are determined from historical data. Prices differ by 

region and season. The transformer load is estimated based on a joint distribution 

function that predicts the transformer load for a given system load. The individual 

steps in the loop are as follows. 

a. Choose a system load value from the system load distribution function. 

b. Estimate the system price for that system load. 

c. Estimate the transformer load for that system load. 

5. For each simulation, the program provides output to be used in calculating the LCC. 

The output includes a transformer identification (ID), the utility ID, customer 

category, and transformer load losses and operating costs. 
7-3

7.2.1 Inputs to Hourly Load Model 

 The following sections describe the inputs used in simulating the hourly load for liquidimmersed distribution transformers. 

7.2.1.1 Utility Information 

The LCC analysis for liquid-immersed distribution transformers uses two types of 

information related to electric utilities. The first is drawn from the Energy Information 

Administration’s (EIA’s) Form 861 database.1 Form 861 provides, through its Form 2, the annual 

sales in megawatt-hours for each utility to the residential, commercial, and industrial sectors. 

Form 861’s Form 4 lists all the utilities that own electricity distribution equipment and the 

county in which that equipment is located. Based on those data, DOE created a list of utilities 

that own transformers and assigned a sample weight to each based on the electricity sales of that 

utility. 

The second type of utility information used in the LCC analysis is hourly system loads 

and prices. DOE developed regional system loads and prices for the set of regions defined in the 

EIA National Energy Modeling System (NEMS) Electricity Market Module (EMM).2 The 

regions represent both national reliability regions and, where they exist, integrated wholesale 

electricity markets, as illustrated in Figure 7.2.1. Each region in turn comprises a number of 

electric utility control area operators (CAOs), some of which may also be utility companies. 

DOE obtained hourly load and system lambda data (for regions without wholesale markets) or 

day-ahead market price data (for market regions) from the Federal Energy Regulatory 

Commission (FERC) Form 714 database.3 DOE aggregated the hourly data to produce regional 

time series for the EMM regions. Appendix 7-B contains the list of entities, along with their 

designated CAO and EMM regions, for which DOE obtained the FERC data used to create the 

hourly time series. 
7-4

Figure 7.2.1 Electricity Market Module Regions in the National 

Energy Modeling System 

 The numbered regions in Figure 7.2.1 are described in Table 7.2.1. 

Table 7.2.1 Definition of Electricity Market Module Regions in the National Energy 

Modeling System 

Index Abbreviation Definition

1 ECAR East Central Area Reliability Coordination Agreement

2 ERCOT Electric Reliability Council of Texas 

3 MAAC Mid-Atlantic Area Council 

4 MAIN Mid-America Interconnected Network 

5 MAPP Mid-Continent Area Power Pool

6 NY New York

7 NE New England 

8 FL Florida Reliability Coordinating Council

9 SERC Southeastern Electric Reliability Council

10 SPP Southwest Power Pool 

11 NPP Northwest Power Pool 

12 RA Rocky Mountain Power Area

13 CA California
7-5

7.2.1.2 Initial Peak Distribution Transformer Loading 

DOE used a distribution of values for initial peak loading to characterize the annual peak 

load served by each distribution transformer in its simulation. The initial peak loading is the ratio 

of the transformer’s peak load in the first year of operation to the transformer’s rated load, before

accounting for any new load growth that occurs later.

4 DOE selected a distribution of initial peak 

loadings that had a median of 85 percent, a minimum of 50 percent, and a maximum of 130 

percent. Standard engineering practice for sizing distribution transformers selects a transformer 

based on the expected annual peak of the load being served, with some provision for load 

growth. Given the provision for future growth, initial peak loading usually is less than 100 

percent. In practice, however, there usually is some error in estimating the peak load that will be 

served, and engineers generally use a discrete set of transformer ratings that are imperfectly 

matched with the expected peak load. Distribution transformers generally are manufactured in 

discrete kilovolt-ampere (kVA) ratings and, on average, the next-larger kVA rating is 50 percent 

larger than the next-lower kVA rating (measured relative to the smaller size). Therefore, the

initial peak loading can be as high as 130 percent, because for short periods a transformer can be 

loaded to more than 130 percent of nameplate capacity.5 Figure 7.2.2 illustrates the distribution 

of initial peak loading that DOE used.

Figure 7.2.2 Distribution of Initial Peak Loading Used in the 

Hourly Load Analysis 

7.2.1.3 Hourly Price-Load Model

The price-load model relates the marginal cost of meeting the next load increment to the 

current system load. The marginal cost is interpreted as the time-varying marginal price of 

electricity for a system. The Department estimated the relationship between system loads and 

system marginal prices for each region based on hourly data collected by FERC Form 7143

 and, 

where appropriate, day-ahead market data from independent system operators. FERC data 

provide hourly system load and lambda values, where the system lambda is defined 

0.00

0.02

0.04

0.06

0.08

0.10

0.12

50% 60% 70% 80% 90% 100% 110% 120% 130%

Peak Load Probability

Transformer Peak Loading
7-6

approximately as the operating cost of the generating unit on the dispatch margin. For regions 

that have integrated wholesale electricity markets, DOE used the day-ahead market data that

include the hourly system load and the market-clearing price. DOE used data for 2008, the most 

recent year for which data from all sources were available. 

The correlation between hourly system prices and loads is illustrated in Figure 7.2.3. This 

figure shows a scatter plot of price versus load for the SERC region. The data pairs (price and 

load values in each hour) are sorted into bins based on load level. Those bins are represented by 

different colors in the figure. 

Figure 7.2.3 Binned Load Versus Marginal Price for Electricity 

Market Module Region Southeastern Electric 

Reliability Council 

DOE estimated the marginal system price within each bin as follows. 

pj = p̅j + δj

Where:

j = the bin index, 

p̅j = the average value of the prices in bin j and, 

δj = a random increment within bin j. 

 In general, both the average price and the range of hourly prices increases with system 

load. To capture the increase in price volatility as a function of system load, DOE added a 

random increment δ to the average marginal price p̅ for each load bin j. To estimate the 

increment δj, DOE used a probability distribution function (PDF) calculated independently for 

each bin j. The PDF for the increment is assumed to be triangular and centered at zero, with the 

0

50

100

150

200

250

0 20000 40000 60000 80000 100000 120000 140000 160000

Lambda $/MWh

Load MWh
7-7

distribution parameters for each bin determined by the data. The approach is described in more 

detail in Appendix 7-A. 

Within the LCC spreadsheet, system loads are represented using a load distribution 

function. This function is calculated by counting the number of times the load level falls inside 

each load bin. The bin sizes are variable and depend on region; the number of bins is constant 

and equal to fifteen. 

7.2.1.4 Distribution Transformer Load Simulation

 DOE estimated the loads on individual liquid-immersed distribution transformers for both 

residential and non-residential customers by creating hourly proxy transformer loads. The 

important quantities for the LCC analysis are the number of hours the transformer is subject to a 

given load level and the correlation between transformer loads and system loads. The first is 

important for determining the total load losses, and the second for accurately estimating the 

economic value of load losses. To estimate the coincident between peak transformer load and 

system load, DOE constructed a joint probability distribution function (JPDF) based on a dataset 

consisting of several hundred hourly whole-building loads. For this analysis, a proxy system load 

was constructed by summing all the available building loads. DOE then estimated the JPDF by

defining a set of bins for both the proxy system load and individual load time series, and 

counting the number of values that fell into each bin. The system load bins used in the JPDF are 

the same as the load bins used in the hourly price-load model. DOE created a separate JPDF for 

residential, commercial and industrial customers. Figure 7.2.4 and figure 7.2.5 show separate 

color plots of the JPDF for commercial and industrial customers. The figure shows the system 

load bins on the horizontal axis and the transformer load bins on the vertical axis, with different 

colors representing the probability that, in a given hour, the system load and transformer loads 

will fall into the given bin. The figure shows that, for low system loads, transformer loads are 

distributed broadly, whereas for higher system loads transformer loads are more tightly 

correlated with system load.
7-8

Figure 7.2.4 Average Joint Probability Distribution for 

Commercial Customers, 1998-2000 

Figure 7.2.5 Average Joint Probability Distribution for 

Industrial Customers, 1998-2000 

7.3 MODEL FOR DRY-TYPE DISTRIBUTION TRANSFORMER LOADS 

This section describes the modeling approach DOE used to estimate the loading for drytype distribution transformers. Given that this type of equipment is owned primarily by C&I

entities, which are billed monthly for electricity, DOE developed appropriate methods to 

estimate the impacts of higher transformer efficiency on monthly energy losses and demand. 

1

2 

3 

4 

5 

6 

7 

8 

9 

10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Transformer Load Bins

Proxy System Load Bins

0.020-0.025

0.015-0.020

0.010-0.015

0.005-0.010

0.000-0.005

1 

2

3 

4 

5 

6

7 

8 

9 

10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Transformer Load Bins

Proxy System Load Bins

0.030-0.040

0.020-0.030

0.010-0.020

0.000-0.010
7-9

7.3.1 Overview of Monthly Load Model 

DOE defined a customer sample for dry-type distribution transformers based on buildinglevel data drawn from the EIA’s Commercial Buildings Energy Consumption Surveys (CBECS) 

for 1992, 1995, and 2003. In its analysis, DOE assumed that each building has a distribution 

transformer, and used building monthly electricity consumption and demand data as inputs to a 

statistical model that estimates the transformer-level data. DOE determined the economic value 

of no-load and load losses by the marginal price of electricity for each building, as determined by 

the electricity tariff. In this analysis, DOE used a previous, detailed study of commercial building 

energy prices,

6 which showed that every building’s electricity costs can be represented as a 

marginal price for energy and a marginal price for demand. Both prices vary by region and 

season.

Distribution transformer losses contain a constant component (the no-load or core losses) 

and a component that depends on the square of the load on the transformer (the load or coil 

losses). The economic value of transformer losses is a function of the load on the transformer 

and the timing of that load with respect to variable energy costs and building peak demand. To 

the extent that there is a correlation between transformer losses and variable energy costs, the 

cost of the electricity supplying the transformer losses will be different from the average cost of 

electricity. The LCC analysis for dry-type distribution transformers uses a statistical model of the 

monthly transformer loss factors, along with a correlation between individual transformer and 

whole-building loads, to estimate changes in monthly electricity consumption and peak demand 

and the corresponding electricity cost savings for C&I customers. 

7.3.2 Monthly Load Simulation 

 The monthly load simulation model embedded in the LCC spreadsheet proceeds as 

follows. 

1. A customer (building) is selected from the sample in the spreadsheet; if the building’s

annual peak load is smaller than the rated capacity of the distribution transformer 

design under consideration, the building is dropped from the sample.

2. An initial peak loading is assigned to the transformer. 

3. The program begins a loop on the monthly electricity consumption and demand data 

for the building. For each month, the program: 

a. calculates the load factor (LF), which is equal to the ratio of the average load 

to the peak load for that month; 

b. estimates the transformer loss factor (LSF) as a function of the LF; and

c. estimates the transformer coincident peak load (CPL) as a function of the LF. 

4. The monthly load data are passed to the controlling loop and used in the LCC 

analysis to calculate the operating cost savings from reduced load losses.
7-10

7.3.3 Inputs to Monthly Load Model 

 The following sections describe the inputs to DOE’s monthly load model, which include 

customer data, initial peak distribution transformer load, transformer loss factor, and coincident 

peak transformer load. 

7.3.3.1 Customer Data

 The customer sample for the dry-type distribution transformer LCC analysis was drawn 

from the 1992 and 1995 CBECS.7,8 Those survey years were used because they include data on 

monthly building-level electricity consumption and demand. All 1992 and 1995 samples that 

provided a complete year of monthly data were combined into a single sample. Weights for the 

full sample were determined by scaling the original building weights to match the floorspace for 

the corresponding building categories given in the most recent CBECS (from 2008). The 

building categories used to define the sample weights were based on building activity, census 

division, and building size. 

DOE had no comparable sample to provide monthly data for industrial customers. To 

represent the fraction of distribution transformers that are installed in industrial buildings, DOE 

assumed that (1) industrial buildings share the load characteristics of the large buildings defined 

in CBECS, and (2) industrial buildings utilize transformers in a way that is comparable to 

similarly sized warehouse-type buildings. In the previous final rule for distribution transformers,

9

DOE assumed that monthly demand and usage for large industrial and commercial customers are 

similar. It verified this assumption by comparing load factor distributions of C&I customers for a 

utility in the southeastern United States. DOE found that the differences among customer classes 

were much smaller than those within each class.

9

DOE used floorspace data from the EIA’s 2006 Manufacturing Energy Consumption 

Survey10 to estimate the total floorspace of industrial buildings that would contain distribution 

transformers covered by this rulemaking. This floorspace was added to the CBECS-based 

floorspace for large commercial buildings to determine total weights for each building in the 

customer sample. 

 Buildings having annual peak loads less than the distribution transformer capacity 

specified in the design under consideration were screened out of the sample. The customer 

sample contains a range of building sizes having a wide range of annual peak loads. Although 

larger buildings undoubtedly contain multiple distribution transformers, DOE currently has no

quantitative information on how the number of transformers in a building scales with either the 

building floorspace or the building annual peak load. Thus, to account for the effect of multiple 

distribution transformers in a single building, DOE used a simple approach whereby it multiplied

the building sample weight by the number of floors in the building. 

7.3.3.2 Initial Peak Distribution Transformer Load 

Initial peak load is the annual peak load on the distribution transformer in the first year of 

operation divided by its rated capacity. The Institute of Electrical and Electronics Engineers 
7-11

(IEEE) has a Draft Guide for Distribution Transformer Loss Evaluation11 that defines a similar 

measure of peak transformer loading called an “equivalent annual peak load,” which accounts for 

changes in peak load throughout the life of a transformer. IEEE’s Draft Guide, which refers to 

the initial peak loading as “initial transformer loading,” uses values of 0.9 and 0.95 in its 

example calculations. Rather than applying the IEEE’s equivalent annual peak load, DOE 

accounted for annual changes in peak load in the LCC calculation by applying an annual rate of 

change in distribution transformer load. DOE characterized a range of initial peak loads by 

defining a distribution of initial peak loads. 

Distribution transformers generally are manufactured in discrete kVA ratings that 

represent their power-handling capacity. On average, each higher kVA rating is 50 percent larger 

than the previous kVA rating (measured relative to the smaller rating). Distribution transformers 

can be loaded above their kVA rating (or nameplate capacity) for short periods. However, 

transformers are often sized conservatively to avoid the possibility of an overload, especially for 

low-voltage, dry-type transformers. DOE received stakeholder comments that medium-voltage, 

dry-type distribution transformers are loaded more heavily than are low-voltage, dry-type 

distribution transformers.

12 DOE therefore selected higher initial peak loading for 

medium-voltage, dry-type distribution transformers than for low-voltage dry-types. If electrical 

engineers accurately size dry-type transformers conservatively with a 10-percent safety margin

relative to the nameplate capacity, initial peak load ranges from 60 percent to 90 percent. The 

high end of the range is the maximum initial peak load that allows for a 10-percent margin of 

safety. The low end of the range reflects the threshold peak load at which the next-smaller kVA 

rating will provide 90 percent peak loading. In response to comments from interested parties, 

DOE adjusted this assumption for medium-voltage, dry-type distribution transformers, selecting

initial peak loadings that were 10 percent higher than for the low-voltage, dry-type distribution 

transformers. Thus the distribution of initial peak load for low-voltage, dry-type distribution 

transformers has a constant probability between 60 percent and 90 percent of nameplate 

capacity; the distribution for medium-voltage, dry-type distribution transformers has a constant 

probability between 70 percent and 100 percent of nameplate capacity. 

7.3.3.3 Distribution Transformer Loss Factor

 For a distribution transformer, the loss factor (LSF) is the ratio of the annual average load 

losses to the peak value of load losses. The LSF is equal to the average of the square of the 

transformer load divided by the square of the peak transformer load. 

In DOE’s analysis, the characteristics of distribution transformer load for C&I building 

owners are the energy and demand savings associated with load losses. The energy savings 

depend on the LSF, which is proportional to the average value of the squared load. To estimate 

the load loss factor for each building, DOE used an expression that relates LSF to load factor 

(LF): 

LSF = α*LF + (1-α)*LF2
7-12

where α is a parameter with α < 0.5. The LF, which is available from the CBECS data, is equal to 

the ratio of the average hourly load to the peak load. DOE estimated a probability distribution for 

the parameter α based on hourly building load data from the End-Use Load and Consumer 

Assessment Program (ELCAP)

6

 survey and additional confidential data from interested parties. 

7.3.3.4 Coincident Peak Load 

 Coincident peak load (CPL) captures the coincidence between a distribution 

transformer’s load and the building’s peak load. For a building that has a single distribution 

transformer, the coincidence would be perfect, and the CPL would equal one. In practice, the 

degree of coincidence depends on how distribution transformers are installed in the building. To 

model the diversity within transformer loads and total building loads, DOE constructed a 

statistical model that predicts the CPL as a function of a building’s load factor. The statistical 

model is based on data for monthly LFs and LSFs calculated using hourly building load data 

from the ELCAP dataset6

 and other data. The modeling approach is discussed in more detail in 

Appendix 7-A. 
7-13

REFRENCES

1. U.S. Department of Energy-Energy Information Administration. Form EIA-861: Annual 

Electric Power Industry Database. (2008). at 

<http://www.eia.doe.gov/cneaf/electricity/page/eia861.html>

2. Energy Information Administration - Office of Integrated Analysis and Forecasting. The 

National Energy Modeling System (NEMS): An Overview. (U.S. Department of Energy, 

2009). at <http://www.eia.doe.gov/oiaf/aeo/overview/> 

3. U.S. Department of Energy-Federal Energy Regulatory Commission. Form No. 714 - Annual 

Electric Control and Planning Area Report. (U.S. Department of Energy-Federal Energy 

Regulatory Commission, 2008). at <http://www.ferc.gov/docs-filing/forms/form714/overview.asp> 

4. M. E. de Oliveira, A. Padilha-Feltrin & F. J. Candian. Investigation of the Relationship 

between Load and Loss Factors for a Brazilian Electric Utility. in (IEEE, 2006). 

doi:10.1109/TDCLA.2006.311390 

5. U.S. Department of the Interior - Bureau of Reclamation. Permissible Loading of OilImmersed Transformers and Regulators. (1991). 

6. Pratt, R. G., Williamson, M. A., Richman, E. E. & Miller, N. E. Commercial Equipment 

Loads: End-Use Load and Consumer Assessment Program (ELCAP). (1990). 

7. U.S. Department of Energy-Energy Information Administration. 1992 Commercial Buildings 

Energy Consumption Survey (CBECS). (1992). at 

<http://www.eia.doe.gov/emeu/cbecs/92microdat.html>

8. U.S. Department of Energy-Energy Information Administration. 1995 Commercial Building 

Energy Consumption and Expenditures (CBECS). (1995). at 

<http://www.eia.gov/emeu/cbecs/microdat.html>

9. U.S. Department of Energy-Office of Building Technologies. in Distribution Transformers 

Energy Conservation Standard Technical Support Document (U.S. Department of EnergyOffice of Building Technologies, 2007). at 

<http://www.regulations.gov/#!documentDetail;D=EERE-2006-STD-0099-0005> 

10. U.S. Department of Energy-Energy Information Administration. 2006 Manufacturing Energy 

Data Tables (MECS). (2006). at 

<http://www.eia.doe.gov/emeu/mecs/mecs2006/2006tables.html> 

11. Institute of Electrical and Electronic Engineers (IEEE). Draft Guide for Distribution 

Transfomer Loss Evaluation. (IEEE, 2001). 

12. Hopkinson, P. ANOPR for Distribution Transformers, EERM/STD-00-550, SNOPR and 

ANOPR Comments, comment No. 65, p. 3. (2004).