Hydronic Heating Retrofits for 

Low-Rise Multifamily Buildings: 

Boiler Control Replacement and 

Monitoring

Jordan Dentz, Hugh Henderson, and 

Kapil Varshney

ARIES Collaborative

October 2013
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iii

Hydronic Heating Retrofits for Low-Rise Multifamily Buildings: 

Boiler Control Replacement and Monitoring

Prepared for: 

The National Renewable Energy Laboratory 

On behalf of the U.S. Department of Energy’s Building America Program

Office of Energy Efficiency and Renewable Energy 

15013 Denver West Parkway 

Golden, CO 80401 

NREL Contract No. DE-AC36-08GO28308 

Prepared by: 

Jordan Dentz, Hugh Henderson, and Kapil Varshney

ARIES Collaborative

The Levy Partnership, Inc., 1776 Broadway, Suite 2205 

New York, NY 10019 

NREL Technical Monitor: Michael Gestwick

Prepared Under Subcontract No. KNDJ-0-40347-02 

October 2013 
iv

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v 

Contents

List of Figures ............................................................................................................................................ vi

List of Tables ............................................................................................................................................. vii

Definitions................................................................................................................................................. viii

Acknowledgments ..................................................................................................................................... ix

Executive Summary.................................................................................................................................... x

1 Introduction........................................................................................................................................... 1

2 Relevance to Building America’s Goals............................................................................................. 2

3 Site Description .................................................................................................................................... 3

4 Retrofit Strategies and Monitoring Plan............................................................................................. 7

4.1 Building 3.............................................................................................................................8

4.2 Building 4...........................................................................................................................11

4.3 Building 55.........................................................................................................................12

4.4 Data Collection ..................................................................................................................13

4.5 Equipment ..........................................................................................................................15

5 Methodology ....................................................................................................................................... 17

6 Results and Analysis ......................................................................................................................... 18

6.1 Building 3...........................................................................................................................18

6.2 Building 4...........................................................................................................................20

6.2.1 Outdoor Reset ........................................................................................................21

6.2.2 Nighttime Setback ..................................................................................................23

6.3 Building 55.........................................................................................................................24

6.3.1 Boiler Runtime and Gas Use Correlation ..............................................................25

6.3.2 Nighttime Setback ..................................................................................................27

6.4 Utility Bill Analysis ...........................................................................................................30

6.5 Cost Effectiveness ..............................................................................................................34

6.6 Other Impacts .....................................................................................................................35

7 Conclusions ........................................................................................................................................ 36

8 Future Work......................................................................................................................................... 37

9 References .......................................................................................................................................... 39
vi 

List of Figures

Figure 1. Exterior view of Building 4 and typical basement boiler room.............................................. 3

Figure 2. Pre-existing boiler controllers (a, b) and local radiator controller (c, d)............................... 4

Figure 3. Approximate previously existing boiler reset schedules ....................................................... 5

Figure 4. Aerial photo showing divisions of buildings and number of units ....................................... 5

Figure 5. Typical space heating system diagram .................................................................................... 6

Figure 6. Controller Web interface ............................................................................................................ 9

Figure 7. Mixing valve controller (top) and boiler controller (bottom) set points and system 

temperatures....................................................................................................................................... 10

Figure 8. Sample apartment temperature sensor data.......................................................................... 10

Figure 9. Building 3 system configuration ............................................................................................. 11

Figure 10. Building 4 system configuration ........................................................................................... 12

Figure 11. Building 55 system configuration ......................................................................................... 13

Figure 12. Building 3 system temperature versus outdoor temperature ............................................ 19

Figure 13. Building 3 system temperature versus average indoor temperature................................ 20

Figure 14. Building 4 boiler performance October 20, 2011................................................................. 21

Figure 15. Boiler loading, Building 4....................................................................................................... 22

Figure 16. Gas use, Building 4................................................................................................................. 22

Figure 17. System temperatures and boiler loading March 27, 2012, Building 4............................... 23

Figure 18. No impact of nighttime setback, Building 4 ......................................................................... 24

Figure 19. Boiler supply target and outdoor temperature in Building 55 ........................................... 25

Figure 20. Plot of actual and predicted gas use .................................................................................... 26

Figure 21. Plot of monthly gas use, Building 55.................................................................................... 27

Figure 22. Plot of boiler loading—comparing days with and without setback through March 6 ..... 28

Figure 23. Operating temperatures in Building 55 before night setback ............................................ 29

Figure 24. Operating temperatures in Building 55 with night setback................................................ 29

Figure 25. Building 3 dependence of energy consumption on OAT (diamonds and green line = 

post-retrofit; squares and brown line = pre-retrofit)....................................................................... 30

Figure 26. Building 4 dependence of energy consumption on OAT (diamonds and green line = 

post-retrofit; squares and brown line = pre-retrofit)....................................................................... 30

Figure 27. Building 55 dependence of energy consumption on OAT (diamonds and green line = 

post-retrofit; squares and brown line = pre-retrofit)....................................................................... 31

Figure 28. Wegowise gas use graph for 3 Columbia Terrace............................................................... 32

Figure 29. Wegowise gas use graph for 4 Columbia Terrace............................................................... 33

Figure 30. Wegowise gas use graph for 55 Columbia Street ............................................................... 33

Figure 31. Diagram of local zone control scheme for Building 55....................................................... 38

Unless otherwise indicated, all figures were created by ARIES.
vii 

List of Tables

Table 1. Existing and Planned Retrofit Controllers ................................................................................. 7

Table 2. Pre- and Post-Retrofit Boiler Controller Settings...................................................................... 8

Table 3. Summary of Monitoring Data Points in Building 3.................................................................. 14

Table 4. Summary of Monitoring Data Points in Building 4.................................................................. 15

Table 5. Summary of Monitoring Data Points in Building 55................................................................ 15

Table 6. Materials and Equipment........................................................................................................... 16

Table 7. Data Collection Periods for Each Building .............................................................................. 18

Table 8. Actual and Predicted Gas Use by Utility Billing Period.......................................................... 26

Table 9. Dependence of Energy Consumption on OAT (Slope) and Coefficient of Determination 

(R2

) ........................................................................................................................................................ 31

Table 10. Pre- and Post-Retrofit Heating and Total Energy Consumption and Reduction (therms)32

Table 11. Energy and Cost Savings Calculations for Heating Season 2011–2012............................. 34

Unless otherwise indicated, all tables were created by ARIES. 
viii

Definitions

ARIES Advanced Residential Integrated Energy Solutions Building America team

CAST Cambridge Alliance for Spanish Tenants

DHW Domestic hot water

F Fahrenheit

HRI Homeowners’ Rehab, Inc.

OAT Outdoor air temperature

TRV Thermostatic radiator valve

WWSD Warm weather shutdown
ix

Acknowledgments

The Advanced Residential Integrated Energy Solutions (ARIES) Collaborative would like to 

recognize the support of the U.S. Department of Energy’s Building America Program and 

Michael Gestwick of the National Renewable Energy Laboratory for technical guidance. This 

project would not have been possible without the support and participation on Jane Carbone and 

Beverly Craig of Homeowners’ Rehab Inc. (HRI), Cambridge, Massachusetts; Martha Abrams 

and Ivan Leslie of Abrams Management, Boston, Massachusetts; and Kevin Reid of Boston 

Cooling and Heating, Norwood, Massachusetts. Thanks are also due to Andrew Proulx, 

EnerSpectives, Inc., Concord, Massachusetts, Victor Zelmanovich, Intech 21, Port Washington, 

New York, and Michael Keber, Tekmar Controls, Vernon, British Columbia. 
x 

Executive Summary

The ARIES Collaborative, a U.S. Department of Energy Building America research team, 

partnered with NeighborWorks America affiliate HRI of Cambridge, Massachusetts, to 

implement and study improvements to the central hydronic heating systems in one of the 

nonprofit’s housing developments. The heating control systems in the three-building, 42-unit 

Columbia Cambridge Alliance for Spanish Tenants (CAST) housing development were upgraded 

in an effort projected to reduce heating costs 15%–25%. 

HRI recognized that heating fuel use per square foot per heating degree day in the development 

was excessive compared to its other properties of similar construction. Although a poorly 

insulated thermal envelope contributes to high energy bills, adding insulation to the exterior 

walls is not a cost-effective or practical option for Columbia CAST, given the desire to maintain 

the building’s historic exterior. Adding insulation on the interiors of exterior walls was also 

impractical, as it would disrupt the residents. The more cost-effective and readily available 

option was improving heating system efficiency. 

Efficient operation of the heating system faced several obstacles, including inflexible boiler 

controls, failed thermostatic radiator valves, and disregard by residents of recommended 

thermostat set points. Boiler controls in three buildings were replaced with systems that offer 

temperature setbacks and one that controls heat delivery based on apartment temperatures in 

addition to outdoor temperatures. A utility bill analysis shows that after implementing control 

techniques overall weather-normalized energy consumption for heating was reduced by 10.1%–

18.3% and the average savings was 15.2%. 

This report summarizes the status of the project at the conclusion of the 2011–2012 heating 

season. During this first season various control settings and system configurations were altered 

as the systems were adjusted to maximize comfort and energy savings. The performance of the 

systems will be monitored during the 2012–2013 season and an updated final report will be 

prepared. 
1 

1 Introduction 

Heating cost reductions can be achieved in several ways, including improving the boiler control 

strategy, giving the resident or building manager the ability to more precisely modulate the 

temperature according to need (instead of opening a window), and altering the distribution of 

heat in the building in ways that better reflect demand. 

A number of older studies exist, documenting the benefits of outdoor reset control in multifamily 

buildings compared to the aquastat-controlled constant water temperatures (sometimes with 

controls that turn off the boiler when outdoor temperatures exceed a certain threshold) that 

typified the previous generation of multifamily heating systems (Hewett and Peterson 1984; 

Peterson 1986). Outdoor reset controls alone can improve the overall performance of the heating 

system, but they are very sensitive to commissioning. If the compensation curve is not adjusted 

properly, the overall heating energy consumption can be higher than that of a boiler controlled at 

a constant water temperature. Adding thermostatic radiator valves (TRVs) to radiators has been 

shown to reduce the system’s dependence on commissioning, but under- or overheating at low 

loads can occur as TRVs age. Additionally, correct use of TRVs depends on proper use by the 

tenants (Liao and Dexter 2004). It has been shown that the overall performance of a heating 

system is highly dependent on the algorithm for determining the boiler temperature set point. 

Inferential models that, in the absence of real-time data, predict the average indoor temperature 

based on a simplified physical model, have been shown to be effective at increasing heating 

system efficiency (Liao and Dexter 2004; Liao and Dexter 2005). Now another shift in control 

strategies is underway, one based on measured real-time average indoor temperatures in 

combination with outdoor temperatures (Center for Energy and the Environment 2006; 

CNT Energy 2010; Gifford 2004). 

New wireless technologies are available to cost-effectively monitor indoor space temperatures, 

centralize and automate thermostat set points, and, with the requisite level of control points in 

place, dynamically adjust heat distribution patterns. Control system manufacturers have 

produced case studies claiming benefits from outdoor reset boiler controls with indoor space 

temperature-based cutoffs of 25%–40% of heating fuel use. However, existing conditions and 

control algorithms are typically not well documented in these case studies. No known thirdparty, independent studies exist quantifying the effects of these improvements. 
2 

2 Relevance to Building America’s Goals

There is a large stock of multifamily buildings in the Northeast and Midwest with space heating 

provided by centralized hot water or steam. According to the 2005 American Housing Survey, 

there are about 3.2 million occupied hydronically heated, low-rise multifamily housing units in 

the United States (U.S. Census Bureau 2006). Nearly 90% of these homes are in the Northeast or 

Midwest; with a large portion being rental units (40%) or occupied by the elderly (24%). Most 

hydronically heated homes are older, with only 1% being classified as new construction (built 

within the past four years) in the 2005 American Housing Survey data. Many of these housing 

units are candidates for improved boiler controls as described in this project. Vendors using 

established technologies are currently well suited to offer these systems on a widespread basis, 

should they prove to be cost effective and easily implemented. 

Typically, residents of these buildings do not pay for heat directly (heat is not submetered). 

Losses from these systems are often higher than would be expected for buildings with centralized 

heat provided by a boiler serving multiple units (a significant number of apartments are 

overheated much of the time). Upgrades to these heating systems often include the installation of 

new, higher performance boilers, yet heating costs sometimes remain high because spaces are 

too warm and the thermal distribution systems are inefficient. The major underlying problems 

are: (1) outmoded and inefficient boiler control strategies, and (2) the inability to regulate the 

amount of heat provided at the point of use (the radiator). 

Research is needed to establish optimum boiler control selection and operating strategies for 

older, multifamily buildings, verify the estimated savings associated with this technology, and 

characterize the factors that impact potential energy savings. Future work could include eQuest 

modeling to more broadly generalize the results. 

No existing Building America guidelines address this topic. Three planned guideline documents 

to which this research might contribute are: 

• Centralized Heating Systems In Multi-Family Buildings – for new homes 

• HVAC Controls – for existing homes 

• Hydronic Space Heating Improvements – for existing homes 

In this project, the relative effectiveness of various control strategies to improve hydronic space 

heating performance in three low-rise multifamily buildings is evaluated. The research questions 

addressed are: 

1. What are the energy savings and comfort benefits of retrofitting central boiler controllers 

using outdoor reset and indoor apartment temperature data into existing hydronically 

heated multifamily buildings? 

2. How does a control system incorporating apartment temperature data compare in cost and 

performance with well-tuned outdoor reset strategies, including those that incorporate 

nighttime setback? This will be addressed by comparing the three buildings in the study. 

The results of this work could be included in a future measures guidelines on hydronic heating 

system retrofits for multifamily buildings.
3 

3 Site Description

Homeowners’ Rehab Inc.’s (HRI) Columbia Cambridge Alliance for Spanish Tenants (CAST) 

housing development is a 42-unit complex of three, three-story masonry buildings in Cambridge, 

Massachusetts (Figure 1). Gas, which is used for space heating, water heating, cooking, and in 

one building for laundry, accounts for about 80% of the annual property energy expenditures. 

Each apartment is metered for electricity directly by the utility, so individual apartment 

electricity use is not available. 

Figure 1. Exterior view of Building 4 and typical basement boiler room

Gas use in the buildings is higher than in other buildings in the area owned by HRI. Gas use for 

space heating alone is more than 0.6 therms/ft2

/yr (more than 0.8 therms/ft2/yr overall).1 Other 

HRI buildings with gas heat use 0.36–0.65 therms/yr overall. While a poorly insulated thermal 

envelope contributes to the problem, insulating the masonry walls from the exterior is not an 

option because of cost and historic preservation restrictions. Insulating the walls on the interior is 

also not an option because of the cost and disruption created by interior construction work. Other 

envelope efficiency measures may be considered in the future, but are unlikely to solve the 

heating distribution problem. 

The buildings are heated with multiple boilers and controllers that reset supply water temperature 

based on outdoor temperature (Figure 2). The building operators are obligated under local 

ordinance to maintain a minimum space temperature in each apartment of 68°F during the day 

and 64°F at night (Massachusets Department of Public Health, 2012). Each apartment has one or 

two nonelectric actuator zone valve controllers to regulate water flow through the baseboard 

heaters (Figure 2 (c) and (d)). These valves, when functional (many are failed), allow the resident 

 

1 Analysis of the building by Wegowise.com indicates that prior to the retrofit their heating consumption was 11–12 

Btu/ft2

/heating degree day, rating as “poor,” with 4–5 considered “great” and 8 considered “good.”
4 

some control over heating and are marked with temperatures, although their calibration is 

unknown. 

(a) (b) 

(c) (d) 

Figure 2. Pre-existing boiler controllers (a, b) and local radiator controller (c, d)

Figure 3 shows the approximate boiler reset schedules previously used in two of the buildings. 

Building 4, which had a more modern Tekmar controller, was set more aggressively to reduce 

the hot water supply temperature. The 1990s vintage Weil McLain controller was set on a less 

aggressive slope. As a result, on April 7, 2011 when outdoor temperatures were 45°–50°F, the 

supply temperature was about 30°F higher in Building 3. This is consistent with the finding 

(based on utility bill analysis) that Building 3 consumed 17% more space heating fuel per square 

foot than Building 4. The large data points in Figure 3 represent the settings on that day. The 

other points define the schedules for the respective controllers.
5 

Figure 3. Approximate previously existing boiler reset schedules

An aerial view of the property indicating divisions between buildings is shown in Figure 4. Each 

building comprises three attached sections, each with its own address number. Each section 

contains one or two apartments on each of three floors. Heating system risers are located under 

each line of radiators in the front and back sides of the buildings with one riser serving each 

radiator in the first-floor apartment and a second riser serving radiators on both the second and 

third floors.

Figure 4. Aerial photo showing divisions of buildings and number of units2

 

2 Note that each building is composed of three attached sections, each with its own address; the buildings are 

referred to in this report using one of these addresses (3, 4, or 55).
6 

Each building has a boiler room in the central portion of the basement. Buildings 3 and 4 (each 

row of building sections is referred to by its lowest address number) both have three 87% annual 

fuel utilization efficiency 175,000 Btu/h input boilers supplying space heating. Building 55 has 

two 87% annual fuel utilization efficiency space heating boilers. Each building also has an 

additional boiler dedicated to supplying domestic hot water (DHW). Figure 5 shows a system 

diagram typical of the three buildings. 

Figure 5. Typical space heating system diagram
7 

4 Retrofit Strategies and Monitoring Plan

Boiler controls were replaced in all three Columbia CAST buildings as part of the retrofit. 

Table 1 describes the pre-existing control systems and retrofit measures. Table 2 describes the 

pre- and post-retrofit boiler controller settings.

Table 1. Existing and Planned Retrofit Controllers

Buildings by Addresses

Building 3

(18 apartments)

Building 4

(15 apartments)

Building 55

(9 apartments)

Boiler

Quantity

and Age

(3) 8 years (3) 3 years (2) 1.5 years 

Original Boiler 

Controller Weil-McLain System 1 Tekmar 264 Weil-McLain System 1

Boiler Return 

Water 

Temperature

Boilers designed to 

operate at a return water 

temperature ≥ 140°F. 

This limits the potential 

efficiency of the 

controller’s outdoor 

temperature reset 

function.

Boilers designed to 

operate with a return 

water temperature 

≥ 140°F. This limits the 

potential efficiency of 

the controller’s outdoor 

temperature reset 

function.

Boilers have a built-in 

bypass to allow 

building return 

water temperature as 

low as 60°F. 

Retrofit 

Controls 

Added new boiler 

controller (Intech 21) 

and indoor temperature 

sensors in every 

apartment to limit 

heating when average 

indoor temperatures 

reach a set point.

Replaced controller 

with new boiler 

controller (Tekmar 

274) capable of night 

setbacks. No indoor 

temperature sensors.

Replaced controller 

with new outdoor reset 

controller (Tekmar 

274) capable of night 

setbacks. No indoor 

temperature sensors.

Retrofit 

Mixing Valve 

Added new 3-way 

mixing valve and 

controller (Intech 21) 

Added new 3-way 

mixing valve and 

controller (Tekmar 

362—eventually 

abandoned

No mixing valve added 

Note that this report summarizes the status of the project at the conclusion of the 2011–2012 

heating season. As can be seen in Table 2, during this first season various control settings and 

system configurations were altered as the systems were adjusted to maximize comfort and 

energy savings. The performance of the systems will be monitored during the 2012–2013 season 

and an updated final report will be prepared. 
8 

Table 2. Pre- and Post-Retrofit Boiler Controller Settings

Parameter 

(°F) Building 3 Building 4 Building 55

Year 2010–2011 2011–2012 2010–2012 2011–2012 2010–2011 2011–2012

Night Setback 0 0 0 0 

3/23/12: 5 0 

0

2/2/12: 10 

2/7/12: 5

Reset Curve 

Boiler 

Maximum

180 170 180 180 180 170-180 

Reset Curve 

OAT 

Minimum

0 10 10 10-15 0 10-15 

Reset Curve 

Boiler 

Minimum

145 130 70 70 145 70 

Reset Curve 

OAT 

Maximum

70 50 70 70-82 70 70 

Minimum 

Boiler 

Temperature

Unknown 110-140 130 110 

3/7/12: 120 Unknown 110 3/7.12: 120 

Indoor Cutoff 

Day n/a 73 n/a n/a n/a n/a

Indoor Cutoff 

Night n/a 68 n/a n/a n/a n/a

Warm 

Weather 

Shutdown 

(WWSD) Day

65 55 65 65 65 65 

WWSD Night 65 45 65 65 65 65

4.1 Building 3

In Building 3, the new boiler control system allows for remote tracking and control of all 

parameters, as well as setbacks.

3 It also incorporates wireless temperature sensors in all 

apartments that provide input into the control algorithm. When the average of the indoor sensor 

readings exceeds the indoor set point by the dead band (set to 2.5°F), the controller reduces heat 

delivered to the building by up to 100%. The system utilizes available hardware from Intech 21, 

 

3 Setbacks are implemented by setting back the building supply water temperature.
9 

a company that specializes in self-healing wireless networks and heating system controls. The 

central controller communicates with an offsite server that stores logged temperature and boiler 

operation data and makes these historical data available on a website. The Web-based system 

allows remote operation and modification of the control parameters and provides real-time 

access to apartment temperature data so that building operators can ensure the legally required 

minimum heating temperature is provided to each apartment, without requiring an excessively 

large safety factor. One risk of this system is that tenants using supplemental heating (or cooling) 

could affect temperature sensor readings inadvertently or intentionally in an effort to obtain more 

(or less) heat. None of this behavior was observed, and the averaging of all apartment 

temperatures minimizes the impact any one apartment can have on the system.

Access to these data also assists in diagnosing heating system problems and addressing tenant 

complaints. Figure 6 shows the controller Web interface; Figure 7 shows the boiler and mixing 

valve control modes, set points, and temperatures; and Figure 8 shows sample apartment 

temperature data. Building management and the HVAC contractor were made aware of these 

functions but worked through ARIES researchers to obtain system data in order to prevent 

changes that could have affected the research. A three-way mixing valve was added to the 

heating system piping (Figure 9). This successfully maintained return water temperatures above 

the levels that could damage the existing noncondensing boilers (140°F). 

Figure 6. Controller Web interface
10 

Figure 7. Mixing valve controller (top) and boiler controller (bottom) set points 

and system temperatures

Figure 8. Sample apartment temperature sensor data
11 

Figure 9. Building 3 system configuration

4.2 Building 4 

In Building 4, new boiler controls allow for remote monitoring of all parameters. The controls 

permit setbacks and outdoor reset. A three-way mixing valve was added to the heating system 

piping (Figure 10) to maintain return water temperatures above the levels that could damage the 
12 

existing noncondensing boilers (140°F); however, as discussed below this did not function as 

intended and was abandoned. 

Figure 10. Building 4 system configuration

4.3 Building 55

The new controller in Building 55 is capable of setbacks. The boilers in Building 55 have a builtin bypass to mix hot water from the boiler outlet with colder return water (as low as 60°F) from 

the building prior to entry to the boiler sections when needed to prevent thermal shock and 
13 

condensation of flue gases in the boiler. Therefore the addition of a new three-way mixing valve 

was unnecessary (Figure 11).

Figure 11. Building 55 system configuration

4.4 Data Collection

Data to evaluate energy savings and comfort impacts were collected under a number of control 

scenarios for each building. Table 3 summarizes the data collection in Building 3. The data were 

logged approximately every 15 minutes. Location codes in the table refer to Figure 9. 
14 

Table 3. Summary of Monitoring Data Points in Building 3

Data Point 

Name Description Location Engineering Units

TZi Temperature in Apartment Using 

Networked Sensors

All 18 

apartments °F 

TBP Temperature of Bypass Water B1 °F

TAO Temperature Outdoor Air 

(used by controller) Outside °F 

TWS Temperature of Hot Water Supplied to Building S1 °F

TWR Temperature of Hot Water Returned 

From Building R1 °F 

TBR Temperature of Hot Water Entering the Boilers S2 °F

SB1-3 Cumulative Runtime on Boilers Each of 3 

boilers Hours

VLV Mixing Valve Position VLV % open

NG Natural Gas Usage From Monthly 

Utility Billing Wegowise Therms

One networked temperature sensor is installed in a central location on an interior wall in each 

apartment in Building 3 (data point TZi). These sensors provide information on comfort levels 

and evenness of heat distribution throughout the building from apartment to apartment. Accuracy 

was checked by a handheld temperature sensor at three locations in each apartment in November 

2011 and March 2012 and the system was calibrated accordingly. 

Firing or runtime for each single-stage boiler is totaled for each 15-minute period (SB1-3). This 

is compared to monthly gas use to corroborate the boiler input rates. This information is used to 

predict gas use for more finely resolved time intervals (daily, hourly, 15-minute) than are 

available from the utility bills.

The Columbia CAST buildings are enrolled in Wegowise.com, a system that automatically 

tracks monthly utility use and costs and uploads the data to a website that permits analysis and 

comparison with other buildings. This system is used to access monthly gas consumption (NG). 

The Tekmar systems were configured to collect 1-minute data using a Mac Mini server. The 

sensors are read every few seconds and averaged into 1-minute intervals. Boiler plant operation 

percentage indicates the runtime of the boiler. Since the boilers are single stage, runtime is 

proportional to capacity. 

Table 4 and Table 5 summarize the data collection in Building 4 and Building 55, respectively. 

Location codes in the tables refer to Figure 10 and Figure 11, respectively.
15 

Table 4. Summary of Monitoring Data Points in Building 4

Data Point 

Name Description Location Engineering Units

TBS4 Boiler Supply Temperature S °F

TBR4 Boiler Return Temperature R °F

TAO4 Temperature Outdoor Air

(used by controller)

Outside 

building °F 

TWS4 Supply Temperature to Building T2 °F

TWR4 Return Temperature From Building T1 °F

TBT4 Target Supply Temperature for Boilers 274 Set pt °F

SB4 Boiler Plant Operation Percentage4 0, 33, 66, 100 %

NG Natural Gas Usage From Monthly Utility Billing Wegowise therms

Table 5. Summary of Monitoring Data Points in Building 55

Data Point 

Name Description Location Engineering Units

TBS55 Boiler/System Supply Temperature S °F

TBR55 Boiler/System Return Temperature R °F

TAO55 Temperature Outdoor Air 

(used by controller)

Outside 

building °F 

TBT55 Target Supply Temperature for Boilers Set point °F

SB55 Boiler Plant Operation Percentage 0, 50, 100 %

NG Natural Gas Usage From Monthly Utility Billing Wegowise therms

Temperature sensors log the temperature in individual boiler returns in Building 55 (TBR55), 

where the two boilers have built-in bypass valves to mix hot water from the boiler outlet with 

colder return water from the system prior to entry to the boilers when needed to prevent 

condensation of flue gases in the boiler. When this return temperature is higher than the main 

building return temperature, it indicates that the valve is open. 

4.5 Equipment

Table 6 describes the materials and equipment being used to collect the data described above. 

 

4 The % indicates the runtime of the boiler. Because the boilers are single stage, runtime is proportional to capacity.
16 

Table 6. Materials and Equipment

Measurement Equipment 

Space Temperatures, 15-Minute Intervals 

Temperature sensors in each 

apartment (Building 3) connected to 

wireless communications network 

Outdoor Temperature

Data logged via Internet-enabled 

controller (Building 3) or via 

standalone data logger (Buildings 4 

and 55) 

Boiler Runtimes

Mixing Valve Position, 15-Minute

Intervals

Boiler Firing Times

Supply, Return, and Boiler Water 

Temperatures

Return Boiler Water Temperatures, 

Building 55

Data collected via standalone data 

logger 

Natural Gas Usage Utility bills via Wegowise.com
17 

5 Methodology

In order to estimate the reduction in heating energy consumption due to the newly installed 

control systems, a regression technique was used. Regression is a statistical technique that 

estimates the dependence of a variable of interest (such as energy consumption) on one or more 

independent variables, such as ambient temperature, and can be used to estimate the effects on 

the dependent variable of a given independent variable while simultaneously controlling for the 

influence of other variables. This procedure can also be used to provide a deeper understanding 

of how and when energy is used. In addition to estimating energy savings, the uncertainty in 

energy savings calculations can also be calculated.

In order to obtain accurate predictions, the sample of energy data used for a regression model 

should be representative of the overall heating season. For energy consumption, the baseline 

modeling period should cover most of the full range of operating conditions. For this project, we 

obtained monthly energy consumption data from energy bills that differ month-to-month, not 

only because of the weather, but also because the number of days in the months may differ. To 

cope with this situation, we divided total energy use (dependent variable) in each month by the 

number of days in each month to obtain the average therms per day. Note that linear regression 

assumes that the x-values (outdoor temperatures) are known exactly, with no measurement 

errors. There are various types of linear regression models used for estimating energy 

consumption or savings. In this work, a three-parameter heating change point model was used as 

described below (Regression for M&V: Reference Guide, 2011). 

Typically multifamily buildings provide space heating only if the outdoor air temperature (OAT) 

falls below a certain threshold known as the WWSD. Therefore, heating energy use may be 

proportional to ambient temperature, yet only below the WWSD temperature; if the OAT goes 

above the WWSD, the heating energy use does not continue to decrease. Energy associated with 

DHW use is similar across all seasons. Under these circumstances, a three-parameter changepoint linear regression has a better fit than a simple regression model. Because of the physical 

characteristics of buildings, the data points have a natural two-line angled pattern to them. 

The following equation was used to calculate energy consumption using a three-point model: 

 

Y = The value of the dependent variable (Energy use) 

Yc = Temperature-independent energy use

m = The linear dependence on the independent variable (slope) 

T = The value of the independent variable (ambient temperature)

Tc = Change-point temperature

(T - Tc)- = Indicates that the values of the parenthetic term are set to 0 when they 

are positive

Pre-and post-boiler controller specifications are provided in Table 2. The change point 

temperature (Tc) was taken as 65°F for all buildings pre-retrofit (the WWSD) and for buildings 4 

and 55 post-retrofit. Tc was taken as 55°F for Building 3 post-retrofit (the post-retrofit WWSD 

for Building 3). Ambient hourly temperature data are used from the Logan Airport weather 

station in Boston, Massachusetts. 
18 

6 Results and Analysis

Each of the three buildings was operated in a variety of control states over the course of the 

heating season (see Table 7). This section describes the control strategies employed and the 

results in terms of boiler run time, system temperatures and other factors for each building. 

6.1 Building 3

The new control strategy for Building 3 incorporated two major changes from the previously 

existing controller.

1. Reset schedule. The original Weil McLain controller had the following settings: Supply 

temperature of 180°F at 0°F outdoor temperature, decreasing to 145°F at 70°F outdoor 

temperature with a WWSD of 65°F for both day and nighttime. The new controller was 

set to a return temperature of 170°F at 10°F outdoors, and 130°F at 50°F outdoors with a 

WWSD of 55°F daytime and 45°F nighttime. Controlling to return instead of supply 

water temperature is thought to result in a more stable system.

2. Indoor cutoff. The new controller cut off the boilers when the average indoor temperature 

(from apartment temperature sensors) was above 73°F daytime and 68°F nighttime. This 

function was enabled on April 5, 2012. 

Table 7. Data Collection Periods for Each Building

Time Period Building 3 Building 4 Building 55

September 27, 2011 Constant return water 

temperature

Constant high 

temperature Outdoor reset

February 2, 2012 Outdoor reset Added nighttime 

setback

February 17, 2012 Added outdoor reset

March 23, 2012 Added nighttime 

setback

April 5, 2012 Added indoor cutoff

May 25, 2012 End of heating season

Figure 12 shows the system temperature for the three different periods with Intech control:

• January 1 to February 16 (black crosses): During this period, outdoor reset was not 

enabled. The system was operated with a fixed return water temperature that differed 

only during daytime (180°F) and nighttime (170°F) due to a nighttime setback. The two 

black horizontal groups of data points reflect these two system set points. 

• February 17 to April 4 (red stars): Outdoor reset was enabled on February 17. The system 

temperatures are significantly lower during this period. However, because a number of 
19 

other adjustments to controller settings were made during February and March, a clear 

trend line is not evident.

Figure 12. Building 3 system temperature versus outdoor temperature

• April 5 to May 31 (blue diamonds): During this period, control settings were fixed and 

indoor cutoff was added. A trendline is evident, indicating the outdoor reset. Many data 

points are at the bottom of the plot during this period because of the greater incidence of 

WWSD during this time of year. 

Figure 13 shows the system temperature compared to the average indoor temperature from the 

apartment temperature sensors. The red data points (stars) are from the period prior to indoor 

cutoff; the blue data points (diamonds) are during indoor cutoff. The lack of blue data points to 

the right of approximately 74°F indoor temperature confirms that this function was operating: 

there is no boiler operation above 74°F average indoor temperature. Indoor temperature cutoff 

was set at 73°F during the day and 68°F at night, with a 2.5°F dead band. 

Intech, Bldg 3 - 02/01/12 to 05/31/12

0 20 40 60 80 100

Outdoor Temperature (F)

60

80

100

120

140

160

180

System Temperature (F)

Jan 1 to Feb 16 (Constant Temp)

Feb 17 to Apr 4 (Reset)

Apr 5 to May 31 (Reset w/Indr Cutoff)
20 

Figure 13. Building 3 system temperature versus average indoor temperature

6.2 Building 4

The new control strategy for Building 4 incorporated two major changes from the previously 

existing controller.

1. Reset schedule. The original Tekmar controller had the following settings: Supply 

temperature of 180°F at 10°F outdoor temperature, decreasing to the 130°F minimum 

boiler supply temperature at about the 48°F outdoor temperature observed on April 7, 

2011. The new controller was set to 180°F at 10°F outdoors (increased to 15°F partway 

through the winter), with the same curve characteristics as the previous controller, 

however with a 2°F lower indoor target temperature (70°F instead of 72°F). The 

minimum boiler supply temperature was set at 140°F and reduced to 110°F over the 

course of the season.

2. Nighttime setback. The new controller incorporated nighttime setback, implemented on 

March 23, 2012 with a 5°F setback.

The Building 4 data (Figure 14) show the boiler supply and return temperatures, which are set to 

a higher temperature because the mixing valve controlled the system temperature. Note that 

the 140°F minimum is not always achieved and the boiler firing pattern is irregular. The cause of 

this was a conflict between the boiler controller and the mixing valve controller. This was 

resolved on February 2, 2012 by fixing the valve in the open position, disabling the valve 

controller, and putting the boiler controller in charge of controlling supply water temperature. 

This was possible because it was determined that, after reviewing return water temperatures, the 

risk to the boilers of sub-140°F return water was low.

Intech, Bldg 3 - 02/01/12 to 05/31/12

60 65 70 75 80

Indoor Temperature from Host (F)

60

80

100

120

140

160

180

System Temperature (F)
21 

Figure 14. Building 4 boiler performance October 20, 2011

6.2.1 Outdoor Reset

Prior to February 2 the system was operating at constant high temperature control without 

outdoor reset. When the mixing valve controller was disabled and the outdoor reset schedule 

implemented on February 2, the boiler loading was reduced. The resulting data are more 

organized in a linear trend as compared to the scattered data before that date (Figure 15). The 

cause of the occasional data above the trend line is unknown. 

The outdoor reset schedule (after February 2) had a noticeable impact on gas use as compared to 

constant high temperature. In Figure 16, the solid line is a best fit of data from August 2011 to 

January 2012 (before reset) and the dashed line is the best fit from February to May 2012. The 

stars are February and March (no night setback); the, triangles are April and May (with night 

setback). 

Tekmar, Bldg 4/6 - 10/20/11

22: 0: 2: 4: 6: 8: 10: 12: 14: 16: 18: 20: 22: 0:

19 20 21

0

50

100

150

Temp (F)

Supply Return Target Outdoor

22: 0: 2: 4: 6: 8: 10: 12: 14: 16: 18: 20: 22: 0:

19 20 21

1 Blr

2 Blrs

3 Blrs
22 

Figure 15. Boiler loading, Building 4

Figure 16. Gas use, Building 4

BLDG 4 (100% = 1 Blr all day)

0 20 40 60 80 100

Avg Outdoor Temperature (F)

0

200

400 Boiler Loading (%)

Reset (after Feb 2)

No Reset

20 40 60 80

Outdoor Temperature (F)

0

10

20

30

40

50

Gas Use (therms/day)
23 

6.2.2 Nighttime Setback

Nighttime setback was implemented in March. The first evidence of noticeable setback was on 

March 27 at 10:00 p.m. (hour 22 in Figure 17).

Figure 17. System temperatures and boiler loading March 27, 2012, Building 4

There was little evidence of the 5°F nighttime setback having an impact on boiler runtime. Both 

the days with and the days without setback fall roughly along the same boiler loading trend line 

(Figure 18).

Tekmar, Bldg 4/6 - 03/27/12 (TAO mn/mx = 29.6/ 48.5)

22: 0: 2: 4: 6: 8: 10: 12: 14: 16: 18: 20: 22: 0:

26 27 28

100

120

140

160

180

Temp (F)

BLR Supply BLR Return SYS Target SYS Supply SYS Return

22: 0: 2: 4: 6: 8: 10: 12: 14: 16: 18: 20: 22: 0:

26 27 28

1 Blr

2 Blrs

3 Blrs
24 

Figure 18. No impact of nighttime setback, Building 4

6.3 Building 55

The new control strategy for Building 55 incorporated two major changes from the previously 

existing controller.

1. Reset schedule. The original Weil McClain controller is assumed to have had the same 

settings found in Building 3: Supply temperature of 180°F at 0°F outdoor temperature, 

decreasing to 145°F at 70°F outdoor temperature. The new controller was set to be 180°F 

at 10°F outdoors, and 70°F at 70°F outdoors with a minimum boiler supply temperature 

of 110°F for most of the winter. 

2. Nighttime setback. The new controller incorporated nighttime setback, implemented on 

February 2, 2012 with a 10°F setback and then adjusted to a 5°F setback on February 8. 

The plot of the supply target temperature versus outdoor temperature (Figure 19) illustrates the 

reset schedule. The second shorter line below the primary line is a result of the week with 10°F 

night setback. 

BLDG 4 (100% = 1 Blr all day)

0 20 40 60 80 100

Avg Outdoor Temperature (F)

0

50

100

150

Boiler Loading (%)

Reset (after Feb 2)

Reset & Setback
25 

Figure 19. Boiler supply target and outdoor temperature in Building 55

6.3.1 Boiler Runtime and Gas Use Correlation

The data in Figure 20 and Table 8 show that the gas use predicted from boiler runtime is in good 

agreement with the actual measured gas use (from the utility bills).

Bldg 55

30 40 50 60 70 80

Outdoor Temperature (F)

100

120

140

160

Boiler Supply Target (F)
26 

Figure 20. Plot of actual and predicted gas use

Table 8. Actual and Predicted Gas Use by Utility Billing Period 

Period

Actual/Measured 

Gas Use 

(therms)

Predicted Gas 

Use 

(therms)

April 26, 2012–May 25, 2012 334 312

March 25, 2012–April 25, 2012 537 508

February 28, 2012–March 24, 2012 556 523

January 27, 2012–February 27, 2012 866 858

December 28, 2011–January 26, 2012 942 923

November 26, 2011–December 27, 2011 714 733

October 25, 2011–November 25, 2011 594 494

Note: Predicted using 1.75 MMBtu/h and assuming that DHW is 4.5 therms/day

200 400 600 800 1000

Actual Gas Use (therms)

200

400

600

800

1000 Predicted Gas Use (therms)

Predicted = [1.75 therms/h] × [boiler hours] + 

[4.5 therms/day] × [days]
27 

6.3.2 Nighttime Setback

Both the gas data and the boiler runtime data show little to no impact of the nighttime setback. 

For comparison purposes, there are 27 days of good data with setback (from February 9 to March 

6). On March 7, the minimum system temperature was changed by an HVAC technician from 

110°F to 120°F. This confounded the analysis of the setback subsequent to that date. 

The plot in Figure 21 shows no impact of night setback. The pre-setback periods (green 

diamonds) fall on the same gas use trend line as the post-setback periods (orange circles). In 

Figure 22, boiler loading on the pre-setback days (black diamonds) falls on the same trend line as 

the post-setback days (red diamonds). 

Figure 21. Plot of monthly gas use, Building 55

-

5 

10 

15 

20 

25 

30 

35 

0 20 40 60 80

Gas Use (therms/day)

Outdoor Temperature (F)

Bldg 55

Feb-12 to May-12

Aug-11 to Feb-12
28 

Figure 22. Plot of boiler loading—comparing days with and without setback through March 6

The plot of operating temperatures and boiler loading before and after implementation of night 

setback may be found in Figure 23 and Figure 24, respectively. The boiler loading plot after 

night setback clearly shows a small gap in boiler runtime during the setback, but an increase in 

runtime during the morning boost. 

BLDG 55 (100% = 1 Blr all day)

0 20 40 60 80 100

Avg Outdoor Temperature (F)

0

20

40

60

80

100

Boiler Loading (%)

Moderate Setback (after Feb 8)

No Setback (before Feb 2)
29 

Figure 23. Operating temperatures in Building 55 before night setback

Figure 24. Operating temperatures in Building 55 with night setback

Tekmar, Bldg 55 - 02/01/12 (TAO mn/mx = 39.7/ 55.5)

22: 0: 2: 4: 6: 8: 10: 12: 14: 16: 18: 20: 22: 0:

31 1 2

80

100

120

140

160

180

Temp (F)

BLR Supply BLR Return Target

22: 0: 2: 4: 6: 8: 10: 12: 14: 16: 18: 20: 22: 0:

31 1 2

1 Blr

2 Blrs

Tekmar, Bldg 55 - 03/06/12 (TAO mn/mx = 26.2/ 46.8)

22: 0: 2: 4: 6: 8: 10: 12: 14: 16: 18: 20: 22: 0:

5 6 7

80

100

120

140

160

180

Temp (F)

BLR Supply BLR Return Target

22: 0: 2: 4: 6: 8: 10: 12: 14: 16: 18: 20: 22: 0:

5 6 7

1 Blr

2 Blrs Evening 

setback

Morning 

boost 
30 

6.4 Utility Bill Analysis

ARIES obtained gas billing data for each building for each month for the pre-retrofit season 

(May 2010 to May 2011) and the post-retrofit season (June 2011 to May 2012). Figure 25

through Figure 27 show the dependence of space heating energy consumption in the building on 

outdoor air temperature for each season. Error bars show variability in the measurements. Total 

predicted savings from the retrofit are shown in Table 10. 

Figure 25. Building 3 dependence of energy consumption on OAT (diamonds and green line = 

post-retrofit; squares and brown line = pre-retrofit) 

Figure 26. Building 4 dependence of energy consumption on OAT (diamonds and green line = 

post-retrofit; squares and brown line = pre-retrofit) 
31 

Figure 27. Building 55 dependence of energy consumption on OAT (diamonds and green line = 

post-retrofit; squares and brown line = pre-retrofit) 

In order to estimate energy savings due to the new control system, energy consumption for the 

2011–2012 heating season was weather-normalized with 2010–2011 OATs. 

Based on the three parameter change point linear regression model described above, the pre- and 

post-retrofit slopes and intercepts for all three buildings’ energy usage were determined (Table 

9). Figure 25 through Figure 27 show gas use in therms as a function of OAT for each building 

before and after the retrofit. Data are weather normalized. The graphs show that post-retrofit (the 

green load line), less fuel was required for space heating at a given OAT compared to pre-retrofit 

(the red load line).

Table 9. Dependence of Energy Consumption on OAT (Slope)

and Coefficient of Determination (R2

) 

Post-Retrofit (2011-12) Pre-Retrofit (2010-11) 

Equation R2 Equation R2

Building 3 y = –1.906x + 10.365 0.9771 y = –1.443x + 8.662 0.9771 

Building 4 y = –1.259x – 5.081 0.9652 y = –1.357x – 4.300 0.9699 

Building 55 y = –0.883x – 1.076 0.9973 y = –0.699x + 6.215 0.9924 

Table 10 presents heating energy and total energy use before and after the retrofit for all three 

buildings. Heating energy use decreased significantly after implementing the new boiler controls. 
32 

Table 10. Pre- and Post-Retrofit Heating and Total Energy Consumption and Reduction 

(therms)

Building 3 Building 4 Building 55

Heating Total Heating Total Heating Total 

Pre-Retrofit (2010–2011) 10,755 14,897 6,802 11,158 5,754 7,820 

Post-Retrofit (2011–2012) 

(Normalized With 

2010–2011 OAT)

8,789 12,916 6,119 10,135 4,864 6,996 

Total Reduction 1,966 1,981 684 1,023 890 824 

% Reduction 18.3% 13.3% 10.1% 9.1% 15.5% 10.5% 

The above analysis agrees with the weather-normalized energy analyses provided by Wegowise, 

which all show a noticeable drop in gas use for the post-retrofit season (2011–2012) (Figure 28

through Figure 30).

Figure 28. Wegowise gas use graph for 3 Columbia Terrace
33 

Figure 29. Wegowise gas use graph for 4 Columbia Terrace

Figure 30. Wegowise gas use graph for 55 Columbia Street
34 

6.5 Cost Effectiveness

Table 11 summarizes the major statistics for each building. The control retrofits are estimated to 

have saved on average about 15.2% of the space heating gas use, or 3,539 therms (or $4,389 at 

$1.24/therm) for all three buildings in 2011–2012. The cost to install the wireless sensors, boiler 

controls, and a Web-based system was $21,574 for all three buildings. The simple payback is 

projected to be approximately 4.9 years. Note that the economics shown in Table 11 are based on 

2011–2012 data and do not necessarily reflect the true savings potential when the control 

systems are fully operational for the complete heating season.

Table 11. Energy and Cost Savings Calculations for Heating Season 2011–2012

Building Information 3 Columbia 4 Columbia 55 Columbia Total

Number of Apartments Served 18 15 8 41 

Number of Bedrooms 45 40 28 113 

Floor Area (ft2) 15,524 13,672 9,955 39,151 

Gas Use for Space Heating

Post-Retrofit 2011–1212 

(therms) 8,789 6,802 4,864 19,772 

Pre-Retrofit 2010–2011 (therms) 10,755 6,119 5,754 23,311 

Economics

Savings (% space heating gas) 18.3% 10.1% 15.5% 15.2% 

Savings (therms) 1,966 684 890 3,539 

Savings ($) $2,438 $848 $1,103 $4,389 

Equipment Costs $10,610 $1,235 $579 $12,424 

Labor Costs $6,750 $1,200 $1,200 $9,150 

Total Costs $17,360 $2,435 $1,779 $21,574 

Cost per Apartment $964 $162 $222 $526 

Payback (years) 7.1 2.9 1.6 4.9 
35 

6.6 Other Impacts

The retrofit control systems are expected to provide other nonenergy benefits to building 

residents and management, including: 

• Occupant comfort: The heating control system should improve comfort by ensuring 

adequate heat and reducing overheating of apartments. Achieving optimum savings may 

result in some apartments being cooler than occupants have become accustomed to, but 

still within the legal limits (Gifford 2004). A survey was conducted to gauge occupant 

satisfaction with the heating system. It will be repeated in 2012–2013 and the results 

compared in a follow-up report. 

• Occupant health and safety: Reducing overheating reduces drying of indoor air and limits 

the need to open windows to allow excess heat to escape in winter, which causes cold 

drafts.

• System reliability: Potential elimination of nonelectric radiator valve controllers removes 

an unreliable component of the existing system (Gifford 2004). 

• Code compliance: The system employed in Building 3 enables the property manager to 

more precisely and reliably comply with minimum/maximum heat laws for rental 

apartments through online monitoring and logging of apartment temperatures. 

• Building and equipment maintainability: Web-enabled visibility of apartment 

temperatures (Building 3) and boiler/valve status permits maintenance personnel to more 

rapidly detect and react to maintenance issues and complaints. 
36 

7 Conclusions

This report summarizes the progress of the research. Controllers in all three buildings have been 

installed and the data have been collected for the 2011–2012 heating season. The following 

progress has been made in answering the project’s research questions:

Question 1: What are the energy savings and comfort benefits of retrofitting central boiler 

controllers using outdoor reset and indoor apartment temperature data into existing hydronically 

heated multifamily buildings? 

Answer: A billing analysis based on this year’s utility bills (with new control system) and the 

previous years’ utility bills (with old control system) showed that the new control system saved a 

significant amount of space heating energy. After implementing control techniques, the overall 

weather-adjusted reduction in space heating gas consumption was 10.1% to–18.3% and averaged 

15.2%. The simple average payback is projected to be approximately 4.9 years. It should be 

noted that for the 2011–2012 heating season, the new control system was implemented 

gradually, only becoming fully functional in all buildings in April 2012. Therefore, it is expected 

that weather-normalized savings will be greater in future heating seasons. Comfort effects will be 

more closely analyzed using indoor temperature data collected in the 2012–2013 heating season. 

Question 2: How does a control system incorporating apartment temperature data compare in 

cost and performance with well-tuned outdoor reset strategies, including those that incorporate 

nighttime setback?

Answer: The data to date show significant benefits from an aggressive outdoor reset schedule, 

but a negligible effect of a 5°F nighttime setback of supply water temperature in the two 

buildings in which it was tested. An indoor temperature cutoff control feature based on average 

apartment temperatures appears to be having the desired effect of reducing fuel consumption in 

one building; however, this feature was tested for only a small portion of the season when OATs

were relatively warm. Additional testing in the 2012–2013 season is needed to evaluate its 

effectiveness under a broader range of conditions. 
37 

8 Future Work

In 2013, the operation and performance of the three systems will be analyzed and compared for a 

full 2012–2013 heating season. Indoor temperature data will be collected from all three buildings 

and a resident survey will be conducted to evaluate comfort. 

Future work may also include a study of the potential additional energy savings possible by 

integrating automated control of individual space heating zones into one building coupled with 

the new central boiler controls. In this scheme, thermostats would direct valves that control the 

flow of hot water to individual zones based on space temperatures in those zones. This would 

reduce heating energy use (i.e., overheating) by delivering heat only to where it is needed in the 

amounts needed. 

This zone-based control research would address the question: How can local zone controls be 

cost-effectively integrated into a hydronic heating control system and what are the associated

energy savings and comfort benefits? 

Figure 31 is a diagram of this control scheme for Building 55, where the local zone control could 

be evaluated. New space temperature sensors would be installed in eight apartments. The 

temperature sensors would communicate with a controller in the basement that would control 

new valves installed in the basement risers in place of the current balancing valves. 

Existing outdoor reset-based central boiler controls would not be altered. Based on an average of 

the temperature readings in each zone, the controller would vary the openings of the valves to let 

the optimum amount of hot water flow through the risers so that the desired space temperature in 

each zone would be achieved. Efficiency and comfort metrics (space dry bulb temperature, 

consistency of adherence to set point) will be compared for this building with and without the 

local controls.

Results for the local zone control research would include an estimate of the potential energy 

savings from adding automated local zone controls to a central outdoor reset boiler control 

strategy.
38 

Figure 31. Diagram of local zone control scheme for Building 55
39 

9 References 

Center for Energy and the Environment. (2006). Outdoor Reset and Cutout Control. Center for 

Energy and Environment: www.mncee.org/pdf/multifamily/outdrreset_coutoutcon.pdf. 

CNT Energy. (2010). Boiler Controls and Sensors. Retrieved April 2011, from Energy Savers « 

CNT Energy: www.cntenergy.org/media/Boiler-Controls-Sensors.pdf. 

Gifford, H. (2004). Choosing Thermostatic Radiator Valves. American Boiler Manufacturer’s 

Association.

Hewett, M.J.; Peterson, G.A. (1984). “Measured Energy Savings From Outdoor Resets in 

Modern, Hydronically Heated Apartment Buildings.” Proceedings of the American Council for 

an Energy Efficient Economy 1984 Summer Study C:135–152. Washington. D.C.: ACEEE. 

Liao, Z.; Dexter, A. (2004). “The Potential for Energy Saving in Heating Systems Through 

Improving Boiler Controls.” Energy and Buildings 261–271. 

Liao, Z.; Dexter, A. (2005). “An Experimental Study on an Inferential Control Scheme for 

Optimising the Control of Boilers in Multi-Zone Heating System.” Energy and Buildings 55–63. 

Peterson, G. (1986). “Multifamily Pilot Project Single Pipe Steam Balancing Hot Water Outdoor 

Reset.” Proceedings of the American Council for an Energy Efficient Economy 1986 Summer 

Study 1:140–154. Washington, D.C.: ACEEE. 

U.S. Census Bureau. (2006). American Housing Survey for the United States: 2005. Washington 

D.C.: U.S. Government Printing Office. 

Massachusetts Department of Public Health. (2012, 06 10). MINIMUM STANDARDS OF 

FITNESS FOR HUMAN HABITATION. Retrieved 11 12, 2012, from The Official Website of 

the Commonwealth of Massachusetts: 

www.mass.gov/eohhs/docs/dph/regs/105cmr410.pdf#page=7. 

Regression for M&V: Reference Guide. (2011). Version 1.0. Prepared for Bonneville Power 

Administration: www.bpa.gov/energy/n/pdf/BPARegression_forMV_ReferenceGuide.pdf. 
40 

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