16 USACERL TR FE-94/22

User Input Requirements

As indicated by the structure of the models presented in Tables 1 and 2, the user is
required to provide a temperature forecast for a period of time equal to the desired
steam load forecast horizon. Thus, for a 24-hour steam load prediction, a 24-hour
temperature forecast is required. CEPLOAD, the prototype boiler load allocation
expert system, includes a data screen to accept hourly temperature forecast inputs.
For the expert system to use the model correctly, the user must input updated
temperature forecasts frequently.

One method for inputting the forecast data is for the user to enter 24 hours of
temperature forecasts once each day (in addition to actual temperature readings,
which should be input every hour). As an option, the user can modify the temperature
forecast values at any time to reflect updated information, such as the movement of
an unexpected weather front toward the base. However, modifying temperature
forecast values may increase the likelihood of sudden changes in the recommendations
of the allocation expert system.

Forecast Model Input Facility

A model input facility (input specification form) was developed, which will greatly aid
the user in the maintenance of the statistical forecast portion of the boiler load
allocation program. This input facility is used to change the model specification (i.e.,
terms such as lagged dependent and independent variables and autoregressive error
terms) or the coefficients of any variables. The input facility is replicated in Table 3.

The input facility can be used to easily update or change the forecast model specification
if facility conditions change or the model is applied to a new facility. In general, forecast
model development should be viewed as a maintenance function to be performed
periodically to ensure that the model retains its validity. The user is strongly cautioned,
however, that even small changes to a regression equation can have a dramatic impact on
the ability of the model to forecast accurately. Changes to the regression equation should
be made only after a comprehensive data analysis. An example of a completed model
input specification form for the Picatinny regression equation is provided in Table 4.



USACERL TR FE-94/22

37

=| Boiler Parameter Definition

Boiler #1 Name/Description [ ]

Fuel Options
Cicoat || Specify Coal Firing Petformance

ElGas || Specify Gas Firing Performance

Ooa Specify Oil Fiting Performance

Nameplate Rating thousand Ibs steam/hr
% rating minimum % rating full % rating
eam [too dice Svetoad

2 Jrours from cold start to minimum steam
2 Jheurs from cold start to nameplate rating

Cycling limits ff] allowable ON/OFF cycles per year

zB __joworr cycles already completed [New noir]
Accept

Figure A5. Boiler parameter definition screen.

the entry for the next lowest value must be calculated, to allow the program to interpolate
the correct efficiency.

Clicking the Accept button closes the Firing Performance screen and returns control to the
Boiler Parameter Definition screen.

The Nameplate Rating field, which is immediately below Fuel Options, pertains to the
capacity of the boiler, expressed in thousand pounds of steam per hour. The rating is used
as the reference point from which the other boiler operating points are determined. The
minimum rating, full rating, and maximum rating overload, expressed in percentage of
the nameplate rating, are entered in the next three fields. These values define the
operating limits of the boiler. They must correspond to the boiler efficiency defined using
the Specify (Fuel) Firing Performance button.



USACERL TR FE-94/22

33

Temperature Forecast °

Day 1 ~ Today
v=
=
vm
=
zm
= a
v=

Figure A2. Temperature forecast screen.

cursor moves to that field, allowing the user to enter the forecasted temperature for that
hour.

A button labeled Next is displayed in the lower right corner of the screen. Clicking on it
advances the next day. The next screens are for Day 3, 0000-2300 and Day 4, 0000-2300.
On the last day, the Next button changes to Go Main, which allows the user to complete
the session of updating entries. On the second and subsequent screens, a Previous button
appears, which allows the user to move back through the screens to the first (where the
Previous button disappears).



10 USACERL TR FE-94/22

3 Development of the Model

Forecasting Boiler Loads

The process of forecasting the boiler load data began with selection of appropriate time
periods. This process was directed primarily by the availability of data. As discussed in
Chapter 2, data sections were constructed to avoid incomplete steam load or temperature
data. These data series could be presented graphically for each time period. Figure 1
presents such a plot for Picatinny Arsenal from 15 October to 15 December 1988. The x-
axis presents the time (in hours), the first y-axis presents steam flow (in thousands of
pounds per hour), and the second y-axis presents the temperature (in degrees Fahren-
heit). A strong negative correlation was observed, as expected, between temperature and
steam load for all time periods.

For each time period a basic model was constructed, consisting of an unlagged TEMP
variable, a one-period lag of the dependent variable (denoted as FLOWI-1)), and a
constant term. These terms were selected because they are highly significant for all
time periods. Therefore, these variables explain a large part of the behavior of the
steam load data. Working from this base model, additional terms were added
sequentially. The percentage of variance in the data that is explained by the
regression equation—known as R-squared—was measured after each model term was
added, and the T-statistic was computed to test the significance of each variable.
Further guidance was given by the BIC statistic, which measures the explanatory
power of the regression equation while penalizing model complexity. The BIC statistic
is a complex, nonlinear function that is useful only for comparisons of different models.
During the modeling process, the addition of certain variables may cause other
previously significant variables to lose their significance, causing them to be dropped
from the model. This occurrence is typically caused by multicollinearity—several
variables attempting to explain the same behavior in the data. In some cases, the
addition of autoregressive error terms would radically change the structure of the
model, causing several independent variable lags to lose significance. The mix of
explanatory and autoregressive error terms proved to be a sensitive parameter during
model building. Consequently, constructing the dynamic regression model became a
matter of trying many different combinations of variables.



20 USACERL TR FE-94/22

The expert system selected was Kappa PC 2.1, a development environment that both
USACERL and the contractor, the Institute of Gas Technology (IGT), are familiar with
and have used in the past. It provides the basic operating program that accepts user
input, defines boiler operating parameters, and evaluates facility status. Additional
required programming was implemented with Visual Basic 2.0, another development
environment familiar to the researchers.

A two-level programming approach was taken to handle the intensive calculations
required to store and review the various possible boiler allocation combinations. Although
Kappa PC is a capable environment, the fact that it is a list (string) processing program
means certain sets of operations are not handled quickly or efficiently in comparison to _
others. For example, arrays can be simulated by lists of entries in multivalued variables,
but accessing the fifth element in variable A is much slower than accessing A(5). The
calculation for identifying the optimal combinations of boilers and boiler operating points
took minutes on a 486/33 computer in the worst case (where as many combinations as
possible were reviewed) after the various functions were optimized. Before optimization,
the process was taking tens of minutes. Switching to the other language brought these
calculations to seconds or tens of seconds to complete. This approach was required to help
ensure that the calculations required for the forecast of a given hour are completed before
the hour has passed.

Boiler Load Optimization Process

The goal of a boiler load allocation expert system is to define an optimum setting for each
boiler to produce the required steam demands for a facility—the sum of each individual
boiler output. The expert system will enable the end user (boiler operator) to define the
boiler facility, and does not require a specific support person to perform setup. The boiler
efficiency specification screen was set up to require boiler efficiency at operating points
spaced at 10 percent increments of the boiler's rated capacity. The boiler performance
entries were used directly rather than attempting to fit the entered points along a curve.
In particular, this allows for boiler performance that has two or more relative peaks in
efficiency.

A direct result of this approach is that determining the optimal combination of boiler
operation and boiler loads—the greatest efficiency at the lowest overall cost—cannot be
accomplished either by simple inspection or techniques such as linear programming.
Linear programming requires that the constraints on independent parameters can be
described by lines. Solutions are found by evaluating intersections of the various lines for
optimum values. Solutions with even the simplest curves are inherently more complex.



USACERL TR FE-94/22

The next two fields are for entering the minimum amount of time required for the boiler
to start cold and reach minimum steam flow, and to move from a cold start to full-rated
output. The last two fields are for specifying the boiler’s allowable ON/OFF cycles within
a single year, and the number of ON/OFF cycles already completed.

Three control buttons are located at the lower right corner of the Boiler Parameter
Definition screen. The Next Boiler button advances to the next higher numbered boiler;
it is only displayed if there is a higher-numbered boiler to define. The Previous Boiler
button moves the user to a lower-numbered boiler; it is displayed only if there is a lower-
numbered boiler than the one displayed. The New Boiler button adds another entry for
a new boiler, up to a maximum of five. For example, if a second boiler must be defined
after having reset the configuration to one boiler, the New Boiler button is clicked to set
up a second boiler form for entry.



REPORT DOCUMENTATION PAGE Cee eprowed

OMB No. 0704-0188

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the lime Tor reviewing instructions, searching xisting dala Sources,
gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of
this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for information Operations and Reports, 1215
Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188), Washington, DC 20503.

T. AGENCY USE ONLY (Leave Blank) | 2. REPORT DATE 3 REPORT TYPE AND DATES COVERED
September 1994 Final
4. TITLE AND SUBTITLE 5. FUNDING NUMBERS
CEPLOAD: A Load-Allocation Program for Army Central Energy Plants 4A162784
AT45
EQ-XK3
6. AUTHOR(S)
Chris Dilks, Ralph Moshage, John A. Kinast, Richard Biederman, and Christopher
F. Blazek
7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 18. PERFORMING ORGANIZATION
U.S. Army Construction Engineering Research Laboratories (USACERL) REPORT NUMBER
P.O. Box 9005 FE-94/22
Champaign, IL 61826-9005
‘9. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSORING 7 MONITORING
U.S. Army Center for Public Works (USACPW) AGENCY REPORT NUMBER
ATTN: CECPW-FU-M
Bldg. 358

Fort Belvoir, VA 22060-5516

17. SUPPLEMENTARY NOTES
Copies are available from the National Technical Information Service, 5285 Port Royal Road, Springfield, VA 22161.

12a. DISTRIBUTION / AVAILABILITY STATEMENT 12b. DISTRIBUTION CODE

Approved for public release; distribution is unlimited.

13. ABSTRACT (Maximum 200 words)

Significant energy savings may be achieved through improved coordination of boiler operation in Army central energy
plants. Historically, plant operators have tended to run their facilities conservatively to cover the uncertainty of
imminent loads while ensuring the reliability of the plant. Because a properly adjusted boiler's operating efficiency
depends primarily on its current load, and because most boilers produce their peak efficiencies in the range of 80
percent to 100 percent of their rated capacity, a preferred operating method would maintain each boiler's load as close
as possible to the point of maximum efficiency. Most Army heating and cooling loads are related to the weather. An
accurate forecast of loads into the near future should make it possible to adjust the boilers to handle those loads more
efficiently. Given a reliable forecast model for future loads and an evaluation of boiler operating parameters, an
optimum boiler load allocation strategy may be developed. Such a strategy could help the Army improve energy
efficiency and reduce the operating costs.

The overall objective of this research is to develop a computer-based expert system to help central energy plant
personnel optimize boiler operations based on accurate load forecasts. This report documents the development of an
accurate load-forecasting model and a prototype expert system called CEPLOAD, which can use the model to help
energy plant personnel optimize boiler load allocation.

14. SUBJECT TERMS 15. NUMBER OF PAGES
energy conservation central heating plants 41
CEPLOAD 16. PRICE CODE
boilers
17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 30. LIMITATION OF ABSTRACT
OF REPORT OF THIS PAGE OF ABSTRACT
Unclassified Unclassified Unclassified SAR
ISN 7540-07-280-5500 ‘Standard Form 298 (Rev. 2-88)

Prescribed by ANSI Std 239-18
298-102



USACERL TR FE-94/22

31

The fifth field shows the current outdoor temperature. The user may update this value
by placing the cursor in the box and typing the number.

The right half of the screen is dominated by rectangular fields showing the status of the
boilers. A maximum of five rectangles are shown—one for each boiler defined for the
facility. Each rectangle or boiler information display shows four information elements—
one in each corner:

1. The upper left element is the boiler identification, indicated by its number.

2. The upper right element is the type of fuel specified for the boiler. The assignable
values are COAL, GAS, and OIL. The value may be updated from the Availability
screen (described below).

3. The lower right element is the desired capacity setpoint for the boiler, expressed in
thousands of pounds per hour. The value is updated when the user issues the
Evaluate command.

4. The lower left element is the boiler status. It can display one of five values: ON,
OFF, STARTING, STANDBY, or UNAVAILABLE. The value may be updated by
clicking on the button to the immediate left of the boiler information display. This
displays a pop-up menu, in which the user indicates the boiler status by selecting the
current mode. Pressing the button again removes the pop-up menu and returns the
display to normal.

In addition to the change in description when a boiler's status is changed, the colors of the
boiler information display also change to reflect the new mode. The ON mode is indicated
by white text on a red background. The OFF mode is indicated by white text on a blue
background. The STARTING mode is indicated by black text on a cyan background. The
STANDBY mode is indicated by black text on a yellow background. The UNAVAILABLE
mode is indicated by white text on a black background.

Below the boiler information displays is a checkbox for selecting and deselecting the
manual mode of operation. If the checkbox is selected (checked with an "X"), the manual
mode is selected when the Evaluate button is pressed. This prompts the program to
allocate the existing load (entered at the top left corner of the screen) among the boilers
indicated to be on. The manual mode allows the user to determine which combination of
boilers will most effectively meet a given load. If the checkbox is deselected (empty), the
program will execute a completely automated analysis, determining forecasted loads and
the optimal combination of boilers and load points.



USACERL TR FE-94/22

17

Table 3. Dynamic regression model input specification form.

Time Dep.Var. Indep.Var. Auto Indep.Var
Period FLOW TEMP Regressive Constant WEEKEND
unlagged - x - x x
- x x x - x
2 x x x - x
3 x x x - x
4 x x x - x
6 x x x - x
12 x x x - x
24 x x x - x
-48 x x x - x
-72 x x x - x
-96 x x x : x
WHERE: x = possible coefficient

The “Weekend Effect”

A “weekend effect” may exist for certain installations. Some facilities may experience
reduced steam consumption on weekends due to lower activity or personnel levels. This
effect was not observed during the modeling process for Fort Benjamin Harrison or
Picatinny Arsenal, but the authors believe it may be significant enough at some
installations to warrant modification of the forecast methodology. Fortunately, a relatively
simple procedure exists for addressing the weekend effect. A dummy variable may be
created for Saturday and Sunday observations. A dummy variable takes one of only two
possible values: zero for nonoccurrences (Monday through Friday), or one for occurrences
(Saturday and Sunday). To implement this procedure, the user would be required to
create a new variable and to code each hourly observation as either 0 or 1, depending on

Table 4. Example model specification form for Picatinny Arsenal model.

Time Dep.Var. Indep. Var. Auto Indep.Var
Period FLOW TEMP Regressive Constant WEEKEND
unlagged - 0.09404 : 6.52 -
A 0.913717 - - - -
2 - - - - -
3 - - - - -
4 0.051614 0.034971 - - -



USACERL TR FE-94/22

2.

10.

11.

12.

13.

14,

15.

16.

17.

18.

19.

Load just experienced by the facility when forecasting values, or the current
expected load for manual mode.

Current outdoor temperature, which is used in forecasting loads.

The number of boilers defined.

Minimum boiler capacity, expressed in percentage of rated capacity, for first boiler.
Rated capacity for first boiler, in 1000 lb/hr steam.

Maximum boiler capacity, expressed in percentage of rated capacity, for first boiler.
Efficiency of first boiler operating at 20 percent of rated capacity.

Efficiency of first boiler operating at 30 percent of rated capacity.

Efficiency of first boiler operating at 40 percent of rated capacity.

Efficiency of first boiler operating at 50 percent of rated capacity.

Efficiency of first boiler operating at 60 percent of rated capacity.

Efficiency of first boiler operating at 70 percent of rated capacity.

Efficiency of first boiler operating at 80 percent of rated capacity.

Efficiency of first boiler operating at 90 percent of rated capacity.

Efficiency of first boiler operating at 100 percent of rated capacity.

Efficiency of first boiler operating at 110 percent of rated capacity.

Efficiency of first boiler operating at 120 percent of rated capacity.

Efficiency of first boiler operating at 130 percent of rated capacity.

Lines 5 through 19 are repeated for all boilers (up to five). If there is only one boiler, then
a total of 19 lines are contained in BLR-CALC.DAT. For a five-boiler facility, a total of 79
lines are contained in BLR-CALC.DAT.



USACERL TR FE-94/22

19

4 Development of the Prototype CEPLOAD
Expert System

Overview

CEPLOAD incorporates the load-forecasting model described in Chapter 3 to help boiler
operators run heating plants at optimal efficiency. The operator inputs temperatures
(forecasts and actual), and the expert system develops recommendations for allocating the
heating load among available boilers. The program is intended to supplement the
operator’s expertise by taking much of the guesswork out of boiler load allocation.

Operation of CEPLOAD is outlined in the Appendix.

System Requirements

The boiler load application program requires a desktop microcomputer running a 386-class
processor at 20 MHz with no less than 4Mb of random access memory (RAM). Microsoft
Disk Operating System (MS-DOS) 5.0 and Microsoft Windows 3.1 are required, as is a
VGA" card and display configured for 640x480 pixels. The computer's system clock must
be current to correctly indicate the time for using the forecasted temperature data. The
computer's hard drive must have room for MS-DOS, Windows 3.1, the boiler load expert
system, the individual boiler load application programs, and their associated runtime
modules.

Operating Environment
Windows 3.1 was selected as the program’s operating environment primarily because of
the better user interface and the greater capabilities of the development tools. Many

applications are now available only for Windows, and those originally developed in the
MS-DOS or other environments have been significantly enhanced for Windows.

* VGA: video graphics adaptor.



32 USACERL TR FE-94/22

In the lower left quadrant of the main screen are control buttons that provide access to the
other parts of the program. The function of each button is summarized below.

Evaluate—This button causes the program to write the necessary files and execute the
subprogram for determining the optimum boiler combinations.

Availability—This button accesses the screen that allows the user to indicate the
availability of staff, equipment, and fuel for a given boiler.

Forecast Model—This button accesses the screen that accepts user input pertaining to the
forecast model coefficients determined by analysis of previous facility load data.

Exit—This button initiates shutdown of the expert system, exiting back to Windows.
Before shutting down, the program writes the current information about the boiler facility
back to the appropriate .KAL and .BIN files for later use.

Temp Forecast—This button accesses the series of screens for entering the temperatures
forecast for the current and next 3 days.

Load Forecast—This button accesses the screen that will display the information about
the forecasted facility loads. It is not functional in the prototype program, but will be in
the full working version.

Cost Factors—This button accesses the screen that allows the user to enter the costs for
each of the fuels (coal, gas, or oil) in dollars per million Btu (British thermal unit) energy
input.

Boiler Definition—This button accesses the screen that allows the user to enter the
operating parameters of the boilers in a facility.

The following sections describe the screens displayed by each button.

Temperature Forecast

The temperature forecast screens are displayed in sequence when the user presses the
Temp Forecast button. An example is shown in Figure A2. The temperature forecast
screens begin with the screen for the current day, with 24 fields for accepting entries
corresponding to the hours from midnight (0000 hours) through 11 p.m. (2300 hours).
Entries are made by using the mouse to point to a field, then clicking on it. The text



USACERL TR FE-94/22

14

"9861 [ludy—youew ‘Auunedig 32 Mo}j jenyoe pue Mo}; peyo|peid jo uosWedwioD °z aunBi4

Oost

MO}-4 PE}IPeld mold enjoy

(sunoy) euwtL

oool “00S
T

T

OL

08

06

oO}
OL
Oct
Oel
Opt
ost
Og}
OZk
0st

“JU/SQ] COO} ‘MOlA WSIS



6 USACERL TR FE-94/22

Approach

Historical boiler load and outdoor temperature data from two Army installations were
used with commercial forecasting software to develop a load forecasting model. The
model was then tested against a different set of historical data to evaluate the model’s
validity. The validated model was then integrated into a prototype expert system,
which was developed using a commercial expert system software shell. A process for
optimizing boiler load allocation was also developed and incorporated into the expert
system. The working prototype was then documented for future development and
enhancement.

Scope

Although the specific results of this project apply to large central heating plants, large
chiller plants for district cooling could also be modeled (with their own forecasting
algorithms) for more efficient operation.

Mode of Technology Transfer

The prototype expert system will be developed further and refined, then field-tested
by energy plant personnel at several demonstration sites. The final product will be
transferred to Army users with a software user’s guide. The expert system will also
be publicized in the Public Works Digest. Future enhancements to the expert system
may include an interface for importing data from a computerized boiler log program
currently being developed by USACERL for the Naval Air Emissions Tracking System
(NAETS).



|

USACERL TR FE-94/22

29

Appendix: Operating CEPLOAD

Program Setup and Startup

Installation and operation of CEPLOAD requires the user to be familiar with basic
Windows operations and file management functions. It is assumed that the reader is
familiar with all basic Windows-related terminology (e.g., “click”). For more information
on the operating environment, see the Microsoft Windows User's Guide.

To set up the files that make up CEPLOAD, establish a subdirectory on your computer’s
hard drive (e.g., c:\BOILALL). Copy the files to the subdirectory from the installation
disk. Create a program group (window) using the windows file manager (optional), then
create a program item (icon) for the program. (You may locate the program item in an
existing window [e.g., Applications] if you prefer.)

Once installed, the program is started by a Windows command KAPPA-Runtime
BLRALLOC.BIN, where KAPPA-Runtime is the appropriate name of the runtime module

for KappaPC.

Another way to start the program is to start the KappaPC development environment,
execute a File Open command with the BLRALLOC.KAL, then enter the command
RunApplication() at the KAL Interpreter window.

General Operation

CEPLOAD’s basic mode of operation is event-driven: it waits for input from the user
before acting, and does not require ongoing input in any particular order. The basic
functions are controlled through the main status display. Each type of operation is
contained in its own screen. The screen displays are described in the following sections.



26

USACERL TR FE-94/22

BLR-TEMP.DAT

The file containing the forecasted temperatures entered by the user through
BLRALLOC.KAL is BLR-TEMP.DAT. It consists of the forecasted temperatures entered
starting with Day 1 (now) at midnight, and proceeding through 11 p.m. on Day 4 (3 days
later), for a total of 96 values. Each temperature is written as a numeric value on one line
by BLRALLOC.KAL. The values are read by BLR-CALC.EXE. The program determines
which value to actually begin using by looking at the system clock on the computer.



USACERL TR FE-94/22

Contents

List of Figures and Tables

1

Introduction
Background .... 0.0... cece eect eee e eens eeeeees
Objectives 2.0... cect teen ene e eset ene eeens
Approach
Scope .......

Mode of Technology Transfer

Forecast Model Development .................. 000 cece cece neces
Data Considerations .......
Forecast Methodology . .

Development of the Model ............00.... 00... ccc ee eee eee
Forecasting Boiler Loads ......... 2.0.2. c eee eee cette eee eee
User Input Requirements .
Forecast Model Input Facility : see
The “Weekend Effect” 2.0.0.0... ccc eee eee eee

Development of the Prototype CEPLOAD Expert System
Overview... 66. cette eee eee e eee e teens
System Requirements
Operating Environment . . an
Boiler Load Optimization Process ............--. eee eee eee eee ene
Forecasting Loads .. 0.6... cece eee eee
File Formats .. 2... 0... ccc ccc eee ete eens

Conclusions and Recommendations ................... se eee eee ee
Different Facilities Need Different Models .

Historical Data and Model Accuracy .....
Weather Forecasts and Model Accuracy

Appendix: Operating CEPLOAD ....... 0... cece eee cee e nee ene



2 USACERL TR FE-94/22

Foreword

This study was conducted for U.S. Army Center for Public Works (USACPW) under
Project 4A162784AT45, “Energy and Energy Conservation”; Work Unit EQ-XK3,
“Energy and Energy Conservation.” The technical monitor was Dennis Vevang,
CECPW-EM.

The work was performed by Energy and Utility Systems Division (FE), Infrastructure
Laboratory (FL), U.S. Army Construction Engineering Research Laboratories
(USACERL). The USACERL principal investigator was Ralph Moshage. A portion of
this work was conducted under contract to USACERL by John A. Kinast, Christopher
F. Blazek, and Richard Biederman of the Institute of Gas Technology (IGT).
Contributions to this report by Frank Johnson, CECER-FEP, and John Spoonamore,
Science Department Chairman at Centennial High School, Champaign, IL, are
gratefully acknowledged. Donald F. Fournier is Acting Chief, CECER-FE and Dr.
David M. Joncich is Acting Chief, CECER-FL. The USACERL technical editor was
Gordon L. Cohen, Information Management Office.

LTC David J. Rehbein is Commander and Acting Director, USACERL, and Dr. Michael
J. O’Connor is Technical Director.



15

USACERL TR FE-94/22

“9861 saquie0aq S1—18q0190 SI ‘AuUNedid 12 MO}! JeNIoe YIM MO} pero|peud Jo UosedWOD “¢ eunBl4

Oost

MOj4 Payolpald

(sunoy) awit,
ooot

Moly [enjoy

00s
T

MH Aird yee’ .

— Ost

002

4U/Sq| OOO} ‘MO|4 WeE}S



USACERL TR FE-94/22

1 Introduction

Background

Significant energy savings may be achieved through improved coordination of boiler
operation in Army central heating facilities. Historically, plant operators have tended
to run their facilities conservatively to accommodate the uncertainty of imminent loads
while ensuring the reliability of the plant. Because a properly adjusted boiler's
operating efficiency depends primarily on the load it is experiencing, and because most
boilers produce their peak efficiencies in the range of 80 percent to 100 percent of their
rated capacity, a preferred operating method would maintain each boiler's load as close
as possible to the point of maximum efficiency. The conservative operating approach
may have multiple boilers operating when fewer could accommodate the load more
efficiently, even if one is set on standby, or "banked."

Most loads experienced by Army central energy plants are heating and cooling loads,
which are related to the weather. Therefore, it is possible to forecast the loads that
should be experienced within the near future in time to adjust the boilers for those
loads. Given a reliable forecast model for future loads and an evaluation of boiler
operating parameters, an optimum boiler load allocation strategy may be developed.
The Army could improve the efficiency and reduce the operating costs of large central
heating facilities at many installations by implementing such a strategy.

Due to the dynamics of the problem, however, and the large number of factors to be
considered in a methodology, an automated system for helping plant personnel identify
the optimal boiler load allocations is considered essential for implementing the
concept.

Objectives

The overall objective of this research is to develop a computer-based expert system to
help central energy plant personnel optimize boiler operations based on accurate load
forecasts. The objective of this phase of the work was to develop an accurate load-
forecasting model and a prototype expert system that can use the model to help energy
plant personnel optimize boiler load allocation.



USACERL TR FE-94/22

21

The format of the data being entered by the user requires a different method to determine
optimum points. The optimization process used in this work consists of exhaustive
enumeration through the possible combinations of boilers and boiler efficiencies. For
example, a two-boiler setup has a range of possible operating loads for each boiler, which
allows the total to be equal to demand for the entire facility. To discover the optimal
combination of the two, one boiler is iterated through its operating range. The second
boiler delivery point, and the efficiencies for both, are then computed. The maximum
combined (plant) efficiency, and the boiler load points that produced them, are found
through the iteration.

The actual number of optimization points found depends on the operating mode of the
boiler load allocation program. If the single-point mode is desired, the number of boilers
used is the number of boilers that were indicated by the user as being on. The expert
system’s optimization module finds the point of maximum efficiency (or minimum cost)
and returns it to the main routine. If the maximum capacity delivered by the boilers
indicated to be on is insufficient for the load, or if the minimum capacity delivered by the
boilers indicated to be on is over that of the facility load, the program notifies the user by
displaying a message.

When the type of operation to be considered is the analysis based on forecasted
information, the number of boiler and load-point combinations tried is based on the total
number of available boilers. If the facility has five boilers, which is the most that
CEPLOAD can accommodate, a total of 31 combinations (2°-1) are compared to determine
the best of the individual optimum values. This process is repeated for each hour of
forecasted load required.

To determine the optimum combination that provides the best operation over an extended
period, the individual boiler combination optimums are searched for the number of hours
being considered. The efficiencies (or fuel costs) are accumulated across the hours, and the
optimum load allocation is determined from these values. Once found, the values are
returned to the main program.

Forecasting Loads

As implemented in the prototype expert system, the load forecast model consists of the
calculations required using each forecast parameter coupled with the appropriate values
from the historical log. Currently, this log consists of the previous facility loads, previous
outdoor temperatures, load estimates for the previous 96 hours, and the current outdoor
temperature. Using these values, the autoregression terms are calculated, and the
forecasted load for the current hour is determined.



US Army Corps

of Engineers
Construction Engineering
Research Laboratories

USACERL Interim Report FE-94/22

September 1994

CEPLOAD: A Load-Allocation Program for

Army Central Energy Plants

by

Chris Dilks, Ralph Moshage, John A. Kinast, Richard Biederman, Christopher F. Blazek

Significant energy savings may be
achieved through improved coordination
of boiler operation in Army central energy
plants. Historically, plant operators have
tended to run their facilities
conservatively to cover the uncertainty of
imminent loads while ensuring the
reliability of the plant. Because a
properly adjusted boiler's operating
efficiency depends primarily on its
current load, and because most boilers
produce their peak efficiencies in the
range of 80 percent to 100 percent of
their rated capacity, a preferred
operating method would maintain each
boiler's load as close as possible to the
point of maximum efficiency. Most Army
heating and cooling loads are related to
the weather. An accurate forecast of
loads into the near future should make it
possible to adjust the boilers to handle
those loads more efficiently. Given a
reliable forecast model for future loads

Boiler Load Allocation Program

[iss Viseore

7/19/93 Date of Last ‘Status: ON Load: 70

[11:02:47 AM| Time of Last

(7o.ee065 |Net Ett. = ‘Status: ON Load: 73

oe

Temperature, Boiler 3 Fuel: Gas

‘Status: OFF Load: 0
] Manual mode

and an evaluation of boiler operating parameters, an
optimum boiler load allocation strategy may be devel-
oped. Such a strategy could help the Army improve
energy efficiency and reduce the operating costs.

The overall objective of this research is to develop a
computer-based expert system to help central energy
plant personnel optimize boiler operations based on
accurate load forecasts. This report documents the

SELECTER

DTIC

JAN:O 4'1995 §

development of an accurate load-forecasting model and a
prototype expert system called CEPLOAD, which can use
the model to help energy plant personnel optimize boiler
load allocation.

Approved for public release; distribution is unlimited.



USACERL TR FE-94/22

helps the user to understand how one variable (steam load) depends on exogenous
variables (e.g., temperature). Another reason for using dynamic regression modeling
was its successful application for similar purposes by other investigators (Spoonamore,
August 1991; Lin and Carnahan, September 1991).

The only drawback to dynamic regression modeling, is that, because the forecasts are
conditioned on unknown exogenous variables (temperature, in this study), a forecast
of the exogenous variables must also be made. This study proceeded under the
assumption that reliable temperature forecasts can be acquired. Fortunately, this
assumption is reasonable because 24- and 48-hour weather forecasts are widely
available. Their accuracy, however, will affect the degree of precision achieved by the
forecast model.

Dynamic regression models may contain three types of model terms. The first type is
independent (explanatory) variables—temperature, in this report—which may be
specified as concurrent or lagged variables. The boiler load forecast model may contain
an unlagged TEMP variable as well as one or several lags of the temperature variable,
denoted as TEMPI-x]. These independent variables and their associated lags are
selected for their ability to increase model fit to the historical data and enhance the
potential for forecasting accuracy.

Second, dynamic regression models may also contain lagged variables representing
previous values of the dependent variable. These optional variables, denoted as
FLOWL-x] for current purposes, provide information about the relationship between
the dependent variable and itself in previous periods. Models incorporating lagged
dependent variable terms may become extremely complex due to phase-interaction
effects between lagged dependent variables and lagged independent variables.
Another effect that should be noted is that predictions built using a model with lagged
dependent variables must use predictions, rather than actual data, to prepare
forecasts. For example, a model that contains a dependent variable lagged by six
periods will require the use of predicted data if the forecast horizon exceeds six
periods. These predictions, however, can be provided by the forecast model.

The third component is the autoregressive error term, an optional component,
computes the error between predicted and realized values of the dependent variable
in various periods. Autoregressive error terms, also known as Cochrane-Orcutt terms,
are always stated with their associated lag. For example, an AUTO[-24] term would
cause the model to include the forecast error that occurred 24 periods previously.
Significant correlations in the pattern of forecast errors can indicate that the historical
data contain information that could be used predicting the future. Autoregressive
error terms are often useful for modeling influences that are not sufficiently explained



USACERL TR FE-94/22

2 Forecast Model Development

Data Considerations

The steam load and temperature data used in this study were derived from two
sources: Fort Benjamin Harrison, IN, and Picatinny Arsenal, NJ. The Fort Harrison
data were for 1 January 1990 to 19 August 1991. These data series included a number
of breaks and discontinuities, so the data series were subdivided into smaller segments
of continuous readings. The Fort Harrison data contained information on three steam
lines: alpha, beta, and delta. Unfortunately, only the beta line proved useful for
forecasting. Alpha and delta steam lines were not considered in the forecast model
preparation. (The study in which these data were recorded is documented in the
Facilities Engineering Applications Program Technical Report FEAP-TR-FE-93/15,
Steam Dispatching Control System Demonstration at Fort Benjamin Harrison, by
Christopher C. Dilks, Ralph E. Moshage, and Mike C.J. Lin ({USACERL, July 1993]).

The Picatinny data were for 1988 and 1989. Steam load information was contained
in the log for boilers 4, 5, and 6. However, boiler number 4 produced steam erratically
and inconsistently, so forecast models were evaluated using only the steam load
information from boilers 5 and 6. Concurrent temperature information was available,
but several days of data were missing from the Picatinny files.

The data from each facility were divided into time periods of about 2 months in
duration. The length of the time period was selected for three reasons: (1) testing
showed that time periods longer than 2 months do not significantly improve model
estimation or forecasting accuracy, (2) time periods shorter than 4 weeks may not
adequately capture the full range of temperature/load interactions for an entire
"weather period,” and (3) comparisons of various 2 month periods can provide useful
insight into seasonal effects.

Forecast Methodology

The forecast method selected for this application is known as dynamic regression
modeling, a multivariate forecasting method that is appropriate when strong
explanatory factors (known as exogenous variables) are present. Regression analysis



28

USACERL TR FE-94/22

the model’s ability to adapt to changes in data is limited. Consistently inaccurate
temperature forecasts will thwart the steam load predictions.

It is recommended that this model be applied primarily to 24-hour forecast periods, in
recognition of the difficulty in obtaining consistently accurate hourly temperature forecasts
for periods longer than 1 day into the future. However, the availability of improved
temperature forecasts would allow the preparation of 48-hour, and even 72-hour steam
load forecasts. The user is strongly advised to verify the accuracy of temperature forecasts
being used in the boiler load allocation model. The program is written to automatically
check on the temperature forecast errors (i.e., actual versus predicted temperatures) and
report to the user if the forecasts are consistently erroneous. The user should monitor the
system’s accuracy statistic, and should switch to a different source for temperature
predictions if the accuracy statistic is frequently violated.



USACERL TR FE-94/22

5 Conclusions and Recommendations

Different Facilities Need Different Models

This study has verified that an accurate forecast model can be developed for the prediction
of boiler steam load. Using data from two facilities for multiple time periods, a series of
dynamic regression models was developed. These models contained an unlagged
temperature variable, a one-period lag of the dependent variable, and a constant term. In
addition, other lags of the independent and dependent variable were specified, depending
on the facility. The existence of different model terms for different facilities leads to the
conclusion that one model type is not suitable for all facilities. Although the dynamic
regression procedure is believed to be appropriate for steam load prediction at any facility,
the terms and coefficients of the regression equation will vary significantly. It is recom-
mended that a comprehensive forecasting effort be conducted for each facility. This
requirement, however, is not severe because software packages are readily available for
such purposes.

Historical Data and Model Accuracy

The accuracy of the forecast model is highly dependent on the historical data on which the
forecast is based. For example, the suspect data from Fort Benjamin Harrison, discussed
in Chapter 3, led to a reduced forecast confidence. This problem could be remedied by
gathering more reliable data and refitting the forecast model. It is recommended that
periodic "maintenance" be performed on a forecast model to verify its accuracy. Any
significant facility modifications would be cause to refit the model. For example, the
construction or closure of a large building on the base may affect steam consumption
patterns.

Weather Forecasts and Model Accuracy

A reliable weather forecast is essential for the model to work correctly. The model
requires an hourly temperature forecast to compute the predicted steam loads, which in
turn affects the boiler load allocation algorithms. Although the model is "dynamic," and
therefore capable of responding to changes in the dependent and independent data series,



4 USACERL TR FE-94/22

List of Figures and Tables

Figures
1 Sample of steam flow and temperature data for Picatinny .....----+ 1
2 Comparison of predicted flow and actual flow at Picatinny,
March-April 1988 0.6... eee eee eee ee eens 14
3 Comparison of predicted flow with actual flow at Picatinny, 15
October—-15 December 1988
Leb n eee eee eee ee eee eee eee ete etter eet eee 15
Al Boiler load allocation main screen ......-- ee eee rere eters 30
A2 Temperature forecast screen ......-- 6-2 eee e reer rere t eres 33
A3 Availability screen 2.6... eee ete eee teens 34
Aa Forecast model screen ....-. 20.2 e cece eee eee eters 35
AS Boiler parameter definition screen... 6... - seers rere reer 37
Tables
1 Recommended dynamic regression equation for Fort
Benjamin Harrison .... 2.0... eee ce eee eee e eee rents 12
2 Recommended dynamic regression equation for Picatinny
Arsenal... 0. cee cee eee terete tense etree 13
3 Dynamic regression model input specification fOrM .... eee eee eee 17
A Example model specification form for Picatinny Arsenal

model .... 0... cee eee eee eee een eee ne ee 17


18

USACERL TR FE-94/22

whether the observation took place on a weekday or weekend. The forecast model would
then include a variable that would not have any effect during the week, but would
influence the steam load prediction on weekends (positively or negatively, depending on
the sign of the coefficient). This dummy variable could also be useful for describing other
large movements of personnel away from base, such as battalion field exercises or troop
excursions. The effect of such temporary personnel movement is analogous to the
weekend effect, so such a variable may be useful for a variety of possible conditions.



USACERL TR FE-94/22

35

The Accept button at the lower right of the screen allows the user to indicate that the
currently displayed status on boiler and resource availability is correct. Clicking Accept
saves the settings and returns program control to the main screen.

Forecast Model

The Forecast Model Parameters screen is displayed when the Forecast Model button on
the main screen is clicked. It is shown in Figure A4.

The model’s parameters are displayed by category in columns as follows: Flow, Tempera-
ture, Autoregressive, Constant, and Weekend. Each parameter is also sorted by the lag
term within rows, with the most recent time periods at the top. For example, the
parameter associated with the fourth time period lag for temperature is found in the fifth
row from the top in the second column. Fields for accepting values are only present when
realistic. For example, there is no Flow, term because the model is for determining the

Forecast Model ;
° po
+ psn |p | |
7B ee |e po
am ] Je | |
“4 foostera | foesas71 | ] e
*f Je )e ] |
2p md } ] a
* fp } ee Je |
“fp |p | @ | |
2p] e | ©] xe]
* f |e |p | | |

Figure A4. Forecast model screen.



36 USACERL TR FE-94/22

steam flow in time period 0 (current time period). There are no constant terms other than
constant because only one constant would be needed, if any.

Entries are made by using the mouse to point to a field, then clicking on it. The text
cursor moves to that field, allowing the user to enter the forecasted temperature for that
hour. The entries for each field should be either a number or 0—the latter meaning that.
the term did not have any appreciable meaning for the forecast model. Attempting to
enter anything other than a number causes the program to display an error message and
return to the prior value.

The button in the Constant column at the bottom of the screen is labeled Accept. Like the
other Accept buttons, clicking it indicates that the user is satisfied with the parameters
as displayed, and the program returns control to the main menu.

Boiler Definition

The Boiler Parameter Definition screen is accessed by clicking the Boiler Definition button
on the main display. The user enters the characteristics of the facility’s boilers on this
screen, as shown in Figure A5. Assuming that a new installation is being defined, a
hidden function resets the boiler definitions to the default of one boiler. The function is
hidden and password-protected to ensure that the previously entered boiler definitions are
not inadvertently cleared. Clicking with the right mouse button on the Accept button
displays a small dialog box in the center of the screen, asking for a password. The
password written into the program is "USACERL Reset", 13 characters total and case-
sensitive. If anything but this is entered, the program returns to the Boiler Parameter
Definition screen without clearing. If the password is accepted, a second dialog box
displays two options: to continue resetting boiler parameters, or to abort and not reset
them. Only after right-clicking on Accept, entering the proper password, and selecting the
clear option are the boiler definitions reset.

Once reset, or when updating values, the topmost field is used for entering the description
of the boiler (such as a location or manufacturer's name). As noted previously, the three
fuel options are coal, gas, and oil. Clicking the checkboxes selects or deselects the fuel
types, to indicate what the boiler is capable of burning. When a particular fuel type is
selected, the Specify (Fuel) Firing Performance button can be clicked to display a column
of input fields for boiler efficiency at various operating points. There is a field for entry of
boiler efficiency at 10 percent increments, from 20 percent to 130 percent of the rated
boiler capacity. Not all of the entries need to be filled, but any entries made must be
contiguous and must span the minimum and maximum value range entered for the boiler.
If the boiler’s usable range ends in something other than an even 10 percent increment,



24

USACERL TR FE-94/22

BLR-FCST.DAT

The forecast model coefficients for flow, temperature, autoregression, weekend (effect), and
the constant are stored in the BLR-FCST.DAT file. It consists of one line per entry with
the specific coefficients ordered in lag periods from most recent to least recent. The
constant is at the end of the file. The coefficient groupings are ordered as follows: flow,
temperature, autoregression, weekend, and constant. This ordering results in the
following sequence:

Flow-p;, Flow-o,, Flow-y3, Flow-o4, Flow-9g, Flow-ia, Flow-24, Flow-4g, Flow-7, Flow-gg,
Temp-o9, Temp-p;, Temp-2, Temp-o3, Temp-p4, Temp-o, Temp-jp, Temp-4, Temp-sg,
Temp-y, Temp-yy Auto-,, Auto-y, Auto-p;, Auto-y,, Auto-og, Auto-jo, Auto-y,, Auto-s,
Auto-72, Auto-sg, Weekend-y, Weekend-y, Weekend-o, Weekend-o3, Weekend-o4,
Weekend-yg, Weekend-1, Weekend-»,, Weekend- yg, Weekend-z, Weekend-9g, Constant

This file is written by BLRALLOC.KAL and read by BLR-CALC.EXE.

BLR-HIST.DAT

The BLR-HIST.DAT file contains the historical log of the previous 96 hours of data. It
uses a comma-delimited, ASCII’ format that consists of the last date-time of the previous
forecast on the first line, and 97 lines containing the values of forecasted flow, actual flow,
and temperatures from the previous 96 hours and the current values.

The date-time is formatted as

#YYYY-MM-DD HH:MM:SS#

with the time represented in 24-hour clock format. For example, 12 July 1993 at
11:17:23 a.m. would be written as #1993-07-12 11:17:23#.

Each hourly entry for forecasted flow, actual flow, and temperatures is separated by
commas. For example, a forecasted flow of 123,000 lb of steam, an actual flow of
125,000 Ib of steam, and a temperature of 29 °F would be stored as

123,125,29

* ASCII: American Standard Code for Information Interchange.



i. General Comments. (Indicate what you think should be changed to make this report and future
reports of this type more responsive to your needs, more usable, improve readability, etc.)

5. If you would like to be contacted by the personnel who prepared this report to raise specific questions
or discuss the topic, please fill in the following information.

Name:

Telephone Number:

Organization Address:

6. Please mail the completed form to:

Department of the Army

CONSTRUCTION ENGINEERING RESEARCH LABORATORIES
ATTN: CECER-IMT

P.O. Box 9005

Champaign, IL 61826-9005



11

USACERL TR FE-94/22

“Kuupedid 40} eyep aunyeseduiay pue Mojj WeE}S JO aidwes *} eunbi4

4 saeiBap ‘einyesedua

Ooch

aineiediuayt Mol wea]S

UI
telere} (sinou) out 00S 0

08

06

0g

ay OOF

ost

00¢

“1U/Sq| OOO} ‘Mola WEIS



USACERL TR FE-94/22

13

periods. These lags included the 7th lag, 10th lag, and 12th lag. These unusual lags
are difficult to understand intuitively, and justification for their inclusion in any model
would be difficult. Given the varying significance of additional model terms, it was
decided to recommend only the basic model that appears in Table 1. For these reasons,
the success of the forecast model was not overwhelming. Given data of better quality,
a superior regression model could probably be developed for Fort Harrison.

Picatinny Arsenal

The results of the forecasting effort were much better for Picatinny Arsenal. A
dynamic regression model was built that displayed a very high level of fit to historical
data and excellent consistency between different time periods. No affinity existed in
the model for autoregressive error terms, and very few lags were found to be
significant other than the ones specified in the regression equation. This indicates
that the independent temperature variable provides sufficient information on which
to build forecasts. Table 2 presents the recommended regression equation for
Picatinny Arsenal.

This model includes an unlagged TEMP independent variable and a one-period lag of
the dependent variable. Four-period lags of the dependent and independent variable
are also specified. This model performed very well, producing an R-squared value of
0.97 to 0.98 and a BIC statistic of 3.35 to 3.13. The model is compact and shows great
promise for forecasting ability.

Figure 2 shows the forecasting ability of the recommended dynamic regression model
for 1 March to 30 April 1988, the time period for which the forecast model was
developed. Figure 3 shows the forecasting

Table 2. Recommended dynamic regression
equation for Picatinny Arsenal.

Variable Coefficient
TEMP -0.09404

FLOWL-1] 0.913717
FLOW(-4] 0.051614
TEMPI-4] 0.034971
CONSTANT 6.52

NOTE 1: Coefficients shown in Table 2 reflect
model applied to data from time period 1
March-30 April 1988.

NOTE 2: Coefficients of model terms are not
directly comparable in the absence of stan-
dardized regression coefficients.

ability of the same model for a different time
period—15 October to 15 December 1988.
Figure 3 shows slightly greater forecast error
than Figure 2, which is to be expected, but it
also shows that the forecast model did an
excellent job. Such a comparison was made
with a number of other time periods, and the
forecast model continued to perform well.
Therefore, it can be concluded that the fore-
cast equation is well suited for all-year boiler
load prediction at Picatinny Arsenal.



USER EVALUATION OF REPORT

REFERENCE: USACERL Interim Report (IR) FE-94/22, CEPLOAD: A Load-Allocation Program
Sor Army Central Energy Plants

Please take a few minutes to answer the questions below, tear out this sheet, and return it to USACERL.
As user of this report, your customer comments will provide USACERL with information essential for
improving future reports.

1. Does this report satisfy a need? (Comment on purpose, related project, or other area of interest for
which report will be used.)

2. How, specifically, is the report being used? (Information source, design data or procedure,
management procedure, source of ideas, etc.)

3. Has the information in this report led to any quantitative savings as far as manhours/contract dollars
saved, operating costs avoided, efficiencies achieved, etc.? If so, please elaborate.

4. What is your evaluation of this report in the following areas?

a. Presentation:

b. Completeness:

c. Easy to Understand:

d. Easy to Implement:

e. Adequate Reference Material:

f. Relates to Area of Interest:

g. Did the report meet your expectations?

h. Does the report raise unanswered questions?



34 USACERL TR FE-94/22
=| Boiler and Resource Availability
FUEL STAFF MATERIALS”
Boiler No. 1 Bl Available available
‘First Boiler @ Gas
Boiler No. 2
. on Available Wi Available
Second Boiler as
Boiler No. 3
ore @s W available B Available
Third Boiler ee
Boiler No. 4
omen’ « Bi Available ® Available
©
Fourth Boiler | © 5%
Figure A3. Availability screen.
Availability

The Boiler and Resource Availability screen is displayed when the Availability button on

the main screen is clicked, as shown in Figure A3.

The screen is arranged in columns and rows. Available boilers are listed along the left
side of the screen. To the right of each boiler listing are fields for fuel type, staff
availability, and materials availability. If a boiler can use more than one type of fuel,
more than one fuel button is displayed (up to three). The Staff checkbox indicates whether
staff is available to start a boiler. The Materials checkbox indicates whether all materials
needed to start a boiler are on hand. If any materials are not available, the checkbox is

deselected.



22 USACERL TR FE-94/22

For projecting loads into the future to estimate the need for additional boiler capacity, the
procedure uses the historical loads and the loads forecasted for the hour immediately be-
fore the hour being forecasted. For example, a forecast for a load 3 hours ahead uses the
forecasted load 2 hours ahead with the coefficient for FLOW_,, and the temperature fore-
casted 2 hours ahead with the coefficient for TEMP.,. In this fashion, the load can be values.

One implication of this forecasting procedure is that, beyond a certain horizon, values are
computed using forecasted rather than actual loads and temperatures. In addition,
autoregression terms may become meaningless for forecast horizons beyond the
subsequent period. For this reason, models that do not rely on autoregression terms (such
as the models developed in this study) are expected to be more accurate for forecasting loads.

To accommodate the autoregression terms (if they are used), the forecast routines presume
that the difference between forecasted loads and actual loads in the future is 0.

File Formats

The files used in the prototype boiler load allocation program serve three functions. The
first is a record of the parameters used to specify facility configuration. This is done by
using the SaveApplication() function within KappaPC, the expert system shell used to
develop the prototype, which ensures that the basic definition is retained. The second
function of the files is as an historical log of the temperatures, loads, and forecasted loads,
which are used with the forecasting model to determine the expected loads. The third
function is for communication between the two operating programs (KappaPC and Visual
Basic). The specific files and their formats are described in the sections that follow.

BLR-CALC.DAT

The file containing the basic information that BLR-CALC.EXE uses is called
BLR-CALC.DAT. It consists of the basic information for calculating forecasted loads Gf
desired) and determining the optimum boiler load allocation. It also contains the
specifications for each boiler in the facility. This file is written by BLRALLOC.KAL and
read by BLR-CALC.EXE.

BLR-CALC.DAT consists of the following lines:

1. Number of hours to forecast loads, determined by the maximum of the number of
hours required to turn on a boiler in the facility. This value is set to 0 for a run in
manual mode, where the program determines the proper allocation for a predefined
boiler setup.



The contents of this report are not to be used for advertising, publication,
or promotional purposes. Citation of trade names does not constitute an
official endorsement or approval of the use of such commercial products.
The findings of this report are not to be construed as an official
Department of the Army position, unless so designated by other authorized
documents.

DESTROY THIS REPORT WHEN IT IS NO LONGER NEEDED

DO NOT RETURN IT TO THE ORIGINATOR



USACERL TR FE-94/22

Main Screen

The boiler load allocation program uses the interface established by KappaPC through
multiple screens. Upon starting the program, the primary screen is displayed. An
example is shown in Figure Al.

In the upper-left quadrant of the screen, the data entry and related fields are displayed.
The first field shows the last load experienced by the facility (if the forecast mode is to be
used) or the current load to be distributed among the boilers. Placing the cursor within
the box allows the user to update the value by typing the number. The next two fields
show the date and time when the load field was updated, both of which change
automatically when load is entered. These fields quickly allow the user to look at the
screen and know when the load value was last updated. The fourth field in the upper-left
quadrant shows the net efficiency of the facility in meeting the entire load as distributed
across the boilers. This value is updated when the user executes the Evaluate command.

Boiler Load Allocation Program ‘

Availability

Cex]

Forecast Model

Hi

Boiler Definition

1000 Ib steam/hr i Boiler 1 Fuel: Gas
Date of Last Status: ON Load: 70
Fisjss [Pate

Fg
Load Boiler 2 Fuel: Gas
79.8065 |Net elt. x bi] Status: ON Load: 79
Temperature, F Boiler 3 Fuel: Gas
Status: ON Load: 46
oe Boiler 4 Fuel: Gas

Evaluate Temp Forecast

Status: OFF Load: 0

(& Manual mode

Figure A1. Boiler load allocation main screen.



Chiet of Engineers
ATIN: CEHECIMLH (2)
ATIN: CEHECIMAP @)
ATIN: CECG

ATIN: CECC-P

ATIN: CECW

ATIN: CECPWZC
ATEN: DET I 79908

Us Amy Engr Distt
ATTN: Libary (40)

Us Army Engr Division
ATI: Library (12)

US Army Europe
ATTN: AEAEN-EH 09014
ATTN: AEAEN-ODCS 09014

SETAF
ATIN: AESE-END 09613
ATTN: AESE-EN 08630
‘Supreme Alied Command
‘ATTN: ACSGEB 09703
‘ATTN: SHIHB/ENGR 09705

INSCOM
‘ATTN: IALOG4 22060
ATTN: IAV-DPW 22186

USA TACOM 48997-5000,
ATIN: AMSTAXE

Defonse Distibuion Region East
‘ATTN: DORE‘! 17070

HO XVII Airborne Corps 28307
ATIN: AFZA-DPW-EE

‘4th Infanty Div (MECH) 80013-5000
ATIN: AFZOFE

US Ary Material Command (AMC)
‘Axara, VA 22333-0001
ATIN: AMCENF

Instattions: (19)

FORSCOM
Forts Gilem & McPherson 30060
ATIN: FCEN
‘Installations: (23)

This publication was reproduced on recycled paper.

USACERL DISTRIBUTION

{th Inanty Daision (Light)
ATTN: APVR-DE 99505,
ATIN: APVR.WF-DE 99703,

‘TRADOG
For Morvoe 23851
ATIN: ATBO-G

Instatations: (20)

Fort Belo 22060
ATIN: CETEC-IMT
ATTN: CECC-R 20314-1000
ATIN: Engr Srategic Sues Cr
[ATIN: Water Resources Suppor Ctr
[ATIN: Austalan Liaison Offce

USA Natick RDBE Canter 01760
ATIN: STRNC-OT
ATIN: ORDNAF

US Amy Mattias Toch Lab
ATIN: SLOMT-DPW 02172

LUSARPAC 96858
ATIN: DPW
ATIN: APENA

SHAPE 09705
ATTN: Inastcture Branch LANDA

‘Area Enginoer, AEDC-Area Ofice
‘Amold Ai Force Staton, TN. 37389

HQUSEUCOM 09128
ATIN: ECM-LIE

ANMRG 02172
ATIN: DRXMRLAF
ATTN: DRXMRWE

CEWES 30180
ATTN: Library

CECAL 09755
ATIN: Lirary

USA AMCOM
[ATIN: Facies Engr 21719
ATIN: AMSNC-EH 61209
[ATIN: Fetes Engr (3) 85619,

USAARMC 40121
‘ATIN: ATZIC-EHA

Mitary rte Mgmt Command
‘ATTN: MTEAGBEHP 07002
ATIN: MT-LOF 20315
ATIN: MTE-SUFE 20461
ATIN: MTWHE 94626

Fort Leonard Wood 65473,
ATIN: ATSE-DACAB @)
ATIN: ATZATE-SW
ATIN: ATSE-CFLO
ATIN: ATSE-DACAL

Miltary Dist of WASH
Fort McNair
‘ATTN: ANEN 20319

USA Engr Acviy, Capital Area
(ATTN: Ubrary 20011

Us Amy ARDEC 07806
ATIN: SMCARASE

Enge Societies Lirary
ATIN: Accuistons 10017

Defense Nuclear Agency
ATTN: NADS 20305

Defense Logistics Agency:
ATTN: DLAWI 2204

Water Rood Army Medical Cr 20807

‘National Guard Bureau 20310
ATTN: NGB-ARI

US Mitary Academy 10096,
ATI: MAENA
-ATIN: Facil Engineer
LATIN: Googrephy & Err Engrg

Naval Fass Engy Command

ATTN: Facilities Engr Command (8)

TIN: Dison Oticos (11)

[ATIN: Pubic Works Conor @)

ATTN: Naval Constr Battaion Cr 93043

ATTN: Naval Facilities Engr Service Center 99043-4328

‘8h US Amy Korea
ATTN: DPW (12)

USA Japan (USAR)

ATIN: APALEN-ES 96343
‘ATTN: HONSHU 96343
ATIN: DPWOkinawa 96376

416th Engineer Command 60628
ATIN: Gibson USAR Ci

Us Amy HSC

Fort Sam Houston 78234
ATIN: HSLO-F

Frasimons Army Medical Or
ATTN: HSHG-DPW 0085

‘Tyndall AFB 32403
ATTN: HOAFCESA Program Ofc
ATTN: Engg & Svc Lab

USA TSARCOM 63120
ATIN: STSASF

‘American Pubic Works Assoc. 64104-1808

US Army Envr Hygiene Agency
ATIN: HSHBME 21010

US Govt Printing Office 20401
[ATIN: Rec SecDeposit Sec (2)

‘Natl Institute of Standards & Tech
‘ATTN: brary 20808

Defense Tech nfo Center 22904
ATTN: DTIC-FAB (2)

28

4 US. GOVERNMENT PRINTING OFFICE: 1994—3510-S/00070



USACERL TR FE-94/22

25

This file should be built using a suitable text editor. For purposes of setup, the actual and
forecasted flows should be the same initially, with the program taking over recording of
the updated values as it is running. This file is read and updated by BLR-CALC.EXE.
BLR-LOAD.DAT

The file BLR-LOAD.DAT contains a total of 72 forecasted flows, starting with the current
hour. Each forecasted flow is written to its own line by BLR-CALC.EXE.

BLR-RSLT.DAT

The file containing the results of the boiler analysis is BLR-RSLT.DAT. This file is written
by BLR-CALC.EXE and read by BLRALLOC.KAL. It consists of the following ASCII
lines:

1. Maximum efficiency/minimum operating cost for the operating point.

2. The first boiler included in the combination, or 0 if no boiler fits.

3. The percentage of rated load for the first boiler in the combination, or 0 if no boiler
fits.

4. The second boiler included in the combination, or 0 if only one boiler fits.

5. The percentage of rated load for the second boiler in the combination, or 0 if only one
boiler fits.

6. The third boiler included in the combination, or 0 if only two boilers fit.

7. The percentage of rated load for the third boiler in the combination, or 0 if only two
boilers fit.

8. The fourth boiler included in the combination, or 0 if only three boilers fit.

9. The percentage of rated load for the fourth boiler in the combination, or 0 if only
three boilers fit.

10. The fifth boiler included in the combination, or 0 if only four boilers fit.

11. The percentage of rated load for the fifth boiler in the combination, or 0 if only four
boilers fit.



USACERL TR FE-94/22

by independent variables and their lags. However, as strong explanatory variables are
added to a given model, the importance of autoregressive error terms should decline.
Autoregressive error terms prevent the model from ever straying too far from actual
conditions. However, the autoregressive error term is really an error-correction term
rather than a variable to measure causality in the data.



12 USACERL TR FE-94/22

For each data set, a best-fit model was found. In most cases, differences in the best-fit
model were observed between time periods for the same base. However, a common-
denominator model was found for each base, i.e., a’model that works well for all time
periods. The finding of a common-denominator model is important because it indicates
that a forecast model can be constructed to serve as an all-season boiler load estimator
for a given facility. Another significant finding was that the recommended model
specification is significantly different for Picatinny than for Fort Benjamin Harrison.
This suggests that a generic boiler load prediction equation for all facilities cannot be
found, and that dynamic regression model parameters must be estimated for each
facility at which the boiler load allocation expert system is to be used.

Fort Benjamin Harrison

The forecast model for Fort Benjamin Harrison proved the less successful of the two
data series. Although an adequate forecast model was constructed, the R-squared
value (0.95 to 0.97) and BIC statistic (414 to 606) were significantly inferior to those
recorded for the Picatinny model (R-squared value of 0.97 to 0.98 and BIC statistic of
3.35 to 3.13). This relative lack of accuracy may have resulted from the fact that only
one of the three steam lines at Fort Harrison was used for forecasting, while the others
were ignored (as explained in Chapter 2). Several attempts were made to forecast
using an aggregate of the steam lines, but in all cases a better fit to the historical data
was achieved using only the beta steam line. Apparently, some valuable steam
load/temperature information was obscured as a result of the intermittent operation
of the alpha and delta lines. Nevertheless, a dynamic regression model with
acceptable fit was produced, and is shown below in Table 1.

The recommended model for Fort Harrison

Table 1. Recommended dynamic regression includes an unlagged TEMP independent
equation for Fort Benjamin Harrison. variable, a one-period lag of the dependent
Variable Coefficient variable, and a one-period lag of the TEMP
TEMP -112.420498 independent variable. A two-period lag of the
FLOWL-1] 0.701146 dependent variable and a 24-hour lagged

autoregressive error term are also included in

TEMP[-1] 101.263721 .

the model. The dependence of this model on
FLOW(-2] 0.243519 .

an autoregressive error term suggests that
AUTOL-24] 9.982310 the independent temperature variable is not
—CONSTANT 1367.94 providing sufficient —_ leading-indicator
NOTE: Coefficients shown in Table 1 information. Thus, the model is attempting to

reflect model applied to data from time

period 16 February-18 March 1991. compensate by correcting for the previous

day's error. Furthermore, additional variable
lags were significant for some but not all time