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Pacific Gas and Electric Company

Emerging Technologies Program

Application Assessment Report #0712

Small Commercial EMS for HVAC and Lighting

Issued: August 4, 2010 Project Manager: Wayne Krill Pacific Gas and Electric Company Prepared By: Asim Tahir, Eric Shadd, Deborah Stanescu Architectural Energy Corporation

LEGAL NOTICE

This report was prepared by Pacific Gas and Electric Company for exclusive use by its employees and agents. Neither Pacific Gas and Electric Company nor any of its employees and agents: 1. makes any written or oral warranty, expressed or implied, including, but not limited to those

concerning merchantability or fitness for a particular purpose; 2. assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of

any information, apparatus, product, process, method, or policy contained herein; or 3. represents that its use would not infringe any privately owned rights, including, but not limited

to, patents, trademarks, or copyrights.

Copyright, 2010, Pacific Gas and Electric Company. All rights reserved.

Table of Contents List of Tables………………………………………………………………………….….………ii

List of Figures……………………………………………………………………………………iii

Preface…………………………………………………………………………………….......…iv

Acknowledgements ………………………………………………………………….…………iv

1.0 Executive Summary................................................................................................ 1

2.0 Project Background ................................................................................................ 3

3.0 Project Objectives................................................................................................... 5

4.0 Methodology ........................................................................................................... 6

4.1 Test Site Description ........................................................................................... 64.2 Project Timeline................................................................................................... 9

5.0 Project Results ..................................................................................................... 11

5.1 Summary of Results .......................................................................................... 115.2 Results for Office Sites ...................................................................................... 125.3 Results for Retail Sites ...................................................................................... 215.4 Customer Satisfaction Findings......................................................................... 345.5 Issues Identified by the Equipment Installation Contractor ................................ 34

6.0 Conclusions and Recommendations .................................................................... 35

7.0 Appendices……………………………………………………………………………….36

7.1 Control System Installation Process and Photos............................................... 367.2 Screenshots from e-Stat Web Interface............................................................. 387.3 Supporting Documentation for Office Sites........................................................ 407.4 Supporting Documentation for Retail Sites........................................................ 42

Small Commercial EMS for HVAC and Lighting i

List of Tables Table 1-1: Test Site Summary......................................................................................... 1Table 1-2: Summary of Predicted Results by Site........................................................... 1Table 1-3: Summary of Sources of Energy Savings........................................................ 2Table 4-1: Test Site Summary......................................................................................... 6Table 4-2: Tracy Office Monitoring Plan .......................................................................... 7Table 4-3: Oroville Office Monitoring Plan....................................................................... 7Table 4-4: Fremont Retail Monitoring Plan...................................................................... 8Table 4-5: Fresno Retail Monitoring Plan ........................................................................ 9Table 4-6: Project Timeline ........................................................................................... 10Table 5-1: Summary of Predicted Energy Savings and System Payback by Site ......... 11Table 5-2: Summary of Predicted Annualized Energy Savings ..................................... 11Table 5-3: Summary of Predictions of, Actual, and Sources of Energy Savings…….…12Table 5-4: Inputs for Office Site Energy Savings Model ................................................ 15Table 5-5: Monthly Calibrations for the Model............................................................... 16Table 5-6: Summary of Predicted Energy Savings with Construction Type .................. 17Table 5-7: Summary of Energy Savings by Building Orientation................................... 18Table 5-8: Summary of Orientation Results w/ Windows Moved................................... 19Table 5-9: Baseline Model Correlations ........................................................................ 27Table 5-10: Inputs for Retail Sites Energy Savings Model ............................................ 28Table 5-11: Fresno Energy Savings by Climate Zone and Orientation.......................... 29Table 5-12: Fremont Energy Savings by Climate Zone and Orientation ....................... 30Table 6-1: Summary of Sources of Energy Savings...................................................... 35Table 7-1: Input Assumptions for Oroville Office Model ................................................ 40Table 7-2: Envelope Assumptions................................................................................. 43Table 7-3: Internal Load Assumptions........................................................................... 43Table 7-4: System Assumptions.................................................................................... 44

Small Commercial EMS for HVAC and Lighting ii

List of Figures Figure 2-1: e-Stat Two-Way Ethernet Thermostats………..………………………………...4 Figure 5-1: Oroville Office Average Daily Electric Load Profiles………………………….13Figure 5-2: Oroville Office AC-2 Fan Operation .......................................................... ..13Figure 5-3: Oroville AHU 1 and 2 Daily Temperature Profiles ....................................... 14Figure 5-4: Model Calibration Trends............................................................................ 16Figure 5-5: Oroville Utility Data Comparing Baseline and Post Retrofit Period ............. 20Figure 5-6: Oroville Monthly Consumption vs. Monthly Average Temperature ............. 21Figure 5-7: Fresno Air-Handling Unit Power vs. Outside Air Temperature .................... 22Figure 5-8: Fremont Air-Handling Unit Power vs. Outside Air Temperature.................. 22Figure 5-9: Fresno Whole-Building Power..................................................................... 23Figure 5-10: Fremont Whole-Building Power ................................................................ 24Figure 5-11: Fresno AHU 1 and 2 Daily Temperature Profiles ...................................... 25Figure 5-12: Fremont AHU 4 Daily Temperature Profiles.............................................. 25Figure 5-13: Fresno Baseline Model Calibration ........................................................... 26Figure 5-14: Fremont Baseline Model Calibration ......................................................... 27Figure 5-15: Fresno Utility Data Comparing Baseline and Post-Retrofit Periods .......... 31Figure 5-16: Fresno Monthly Consumption vs. Monthly Average Temperature………...31Figure 5-17: Fresno Monthly Electric Demand .............................................................. 32Figure 5-18: Fremont Utility Data Comparing Baseline and Post-Retrofit Periods ........ 33Figure 5-19: Fremont Monthly Consumption vs. Monthly Average Temperature .......... 33Figure 5-20: Fremont Monthly Electric Demand............................................................ 34Figure 7-1: Fresno Geometry and Zoning ..................................................................... 42Figure 7-2: Fremont Geometry and Zoning ................................................................... 42Figure 7-3: Fresno & Fremont Occupancy Schedules .................................................. 43

Small Commercial EMS for HVAC and Lighting iii

Small Commercial EMS for HVAC and Lighting iv

Preface Architectural Energy Corporation (AEC), an energy and environmental research, development, and design consulting firm located in Boulder, Colorado, prepared this document for PG&E. The report was contributed to by Asim Tahir, Eric Shadd and Deborah Stanescu.

Acknowledgements This project benefited from the assistance of:

Steve Blanc, Jim Anderson, David Haena, Roger Curtiss, Lee Cooper, Joanne Medvitz, Roger Ballesteros, and Wayne Krill of PG&E.

Larry Waldkirch of Waldkirch Electric Inc.

Suzie Chan of Electric Works Inc.

Ron Eigenbrod and Steve Metcalf of Lightstat Inc.

1.0 Executive Summary The hypothesis guiding this project was that a deemed savings model can be created based on the analysis and simplification of variables involving the operation of a small energy management system (EMS)—including market segment, load type, climate zone, building schedule, and building construction type. This was to be achieved by testing the reliability of thermostat-based EMS systems and collecting operational information to calibrate computer models and determine the feasibility of offering a deemed rebate program for this technology. Although the EMS product tested in this project was also applicable to controlling lighting systems, this functionality was not evaluated in this assessment.

Four test sites were selected to fulfill the project objectives. The intention was to capture a single zone and multiple zone facility for each market segment. This was achieved for the two office sites, but proved difficult for the retail segment. A brief summary of the test sites is in the Table 1-1 below.

Table 1-1: Test Site Summary Location Market

Segment HVAC Zones

Climate Zone

EMS Type Utility Rate

Area (sf)

Construction Type

Tracy, CA Office 1 12 HVAC Only A1 2.660 Metal building Oroville, CA Office 3 11 HVAC Only A1P 7,536 Wood Fremont, CA Retail 4 3 HVAC Only A1P 7,392 Strip Mall Fresno, CA Retail 4 13 HVAC Only A10S 7,181 Strip Mall

The Tracy Office is part of a workshop/ storage shed and storage yard. There is a significant amount of process loads on the same meter as the office. This was discovered during the data analysis period after the post retrofit data had been collected at site. Since the impact of the EMS retrofit was not discernible in the main electric meter, this site was excluded from further analysis.

The results for the three remaining sites summarized in Table 1-2 do show a reduction in annual energy consumption, indicating that implementation of thermostat-based EMS systems is a reliable method of achieving energy savings. Demand savings are fairly modest and there is even a potential for demand penalty, depending on how the controls implementation adjusts the setpoints from the baseline condition.

Table 1-2: Summary of Predicted Results by Site Test site Predicted

Energy Reduction

(kWh)

Predicted Peak Demand

Reduction (kW)

Cost Reduction ($)

Implementation Cost ($)

Simple Payback (yrs)

Tracy Office n/a n/a n/a $760 n/a Oroville Office 10,812 0.4 $1,850 $1,980 1.07 yrs Fremont Retail 15,161 0.9 $2,462 $3,040 1.23 yrs Fresno Retail 3,938 2 $606 $3,040 5.01 yrs

Results also show that savings are heavily dependent on specific operational anomalies that existed prior to control implementation. For the three test sites that were analyzed in detail, Table 1-3 compares the savings predicted by the simulation model for each location with those calculated from changes in monthly utility bills.

Small Commercial EMS for HVAC and Lighting 1

Table 1-3: Summary of Sources of Energy SavingsSite Predicted Energy

Savings kWh (%) Primary Source of savings in Simulation

Model Actual Energy Savings

kWh (%) Oroville office 10,812 kWh

(27%) Fans on two out of three AHUs were

operating in continuous mode, which were reset to cycle with loads post retrofit.

10,727 kWh (26.4%)

Fremont Retail 15,161 kWh (17.9%)

Faulty thermostat reset schedule on one AHU was causing excessive nighttime

heating during winter.

4,693 kWh (5.6%)

Fresno Retail 3,938 kWh (3.0%)

Faulty thermostat setting on two AHUs was causing some nighttime heating during winter. Most of the savings come from

changes in thermostat settings.

28,839 kWh (22.1%)

Only the results for the Oroville office site show an agreement between the predicted and measured energy savings. The operational anomalies that were causing excessive energy use at the Oroville site were consistent and easy to identify and quantify. The primary source of energy savings was correcting the operational mode of two AHU fans from continuous to intermittent.

Both retail sites had operational anomalies that were not occurring consistently and were difficult to quantify. Monitored data captured only a snapshot of these anomalies, and assumptions were made to extrapolate energy savings for the whole year. The actual savings vary significantly from the predicted values for both the retail sites. This indicates that developing a deemed savings model based on a limited set of variables may be difficult.


2.0 Project Background There are many EMS products for HVAC and lighting control available in the market targeting the small commercial sector. The full capabilities of these systems vary somewhat, but at a minimum they all provide the ability to control HVAC and lighting systems with remote access via the Internet.

PG&E provides incentives for the installation of these systems through the NRR/DR programs. However, the NRR/DR application process requires energy savings calculations and verification data for each site. This can involve significant resources on the part of the applicant and on the part of PG&E to prepare calculations, collect data, and shepherd applications through the review and verification process. The resources required can potentially be greater than the value of the energy savings achieved through the implementation of these controls. This situation is a potential barrier to these technologies gaining more market acceptance.

This barrier can potentially be crossed by creating a deemed savings model for these types of controls in the small commercial sector. Deemed savings are defined as pre-determined, validated estimates of energy and peak demand savings attributable to an energy efficiency measure in a particular type of application. Deemed savings can be used by a utility instead of energy and peak demand savings determined through measurement and verification.

The hypothesis guiding this work is that a deemed savings model can be created based on the analysis and simplification of variables involving the operation of a small EMS, including: market segment, load type, climate zone, building schedule, and building construction.

The installation of an EMS by itself does not save energy. The savings potential depends on how the control capabilities are used to improve operation compared to the baseline conditions. Potential savings vary greatly based on how well the existing controls are used, and what improvement, if any, a small commercial EMS would be able to provide.

The existing controls in the market depend greatly on the vintage of the facility. For HVAC in older facilities, the basic control would be a manual mercury bulb thermostat without any scheduling or setback capability. Some facilities will have a programmable thermostat with scheduling, setback and lock out controls. For lighting, the minimum control would be manual wall switches. Some facilities have a programmable astronomic clock controlling the interior and exterior lights.

The EMS evaluated in this project—the e-Stat Two-Way Ethernet Thermostat manufactured by LightStat and shown in Figure 2-1—claims the following attributes:


Figure 2-1: e-Stat Two-Way Ethernet Thermostats

It wires and operates like a traditional thermostat and can be installed by any HVAC vendor.

It receives time and temperature programs from any computer via the Internet.

It allows owner to lock out or limit the range for local temperature adjustments.

It reports system temperatures and equipment status, enabling simple, remote troubleshooting.

It has easy-to-use Internet-based control of up to 10,000 thermostats.

Requires no special training to operate.

The potential benefits of these systems are reduced energy costs due to enhanced controls and reduced labor costs due to remote troubleshooting capability.


3.0 Project Objectives The main objective of this project was to monitor a small commercial Energy Management System (EMS) for HVAC and lighting in three different California climate zones and in two different types of businesses. These were selected to test the reliability of thermostat-based EMS systems; collect operational information to calibrate computer models of energy savings performance; and determine the feasibility of offering a deemed rebate for this technology.

This project had the following goals:

Gather field data to quantify energy efficiency (EE) and demand response (DR) benefits. Understand the market issues of small commercial stakeholders (i.e. corporate/franchise

owners, local managers, and workers). Identify installation, system integration, maintenance and repair issues. Validate the overall technical functionality of the installed controls and associated supporting

communications and data systems (including control front-end software, databases and visualization systems) that constitute the central control capabilities.

The results of this project were designed to develop an average energy and demand savings model across small commercial market segments and various load types. The hypothesis guiding this work is that a deemed savings model can be created based on the analysis and simplification of the following variables involving the operation of a small EMS:

1. Market segment: a. Small office b. Retail (dry goods)

2. Load type: a. HVAC – Rooftop single-zone air conditioning units with built in compressor,

evaporator, condenser and supply air fan b. Lighting1 – ceiling luminaires circuited as 277V or 120V legs

3. Climate zones 3 and 11-13 - The California Energy Commission has divided the State into 16 climate zones (CZ) that represent a geographic area that has similar climatic characteristics. CZ 3 represents the coastal region around the Bay Area. CZs 11 to 13 represent central valley ranging from Red Bluff to Bakersfield.

4. Building schedule (i.e. typical hours of operation): a. Offices: 8am -5pm Mon-Fri b. Dry Goods Retail: 10am-9pm Mon – Fri and 10am – 5pm Sat Sun

5. Building type: Various constructions for “strip mall” or stand-alone buildings with flat roofs, with windows in front for retail.

6. Electric and gas rates: PG&E small commercial rates. A-1, A-6, G-1 1 None of the test sites implemented the lighting portion of the controls.


4.0 Methodology

The analysis for this project followed the general methodology outlined below. Specific details are contained in Sections 4.1 and 5.

Baseline and post retrofit data were collected for four test sites per the monitoring plan described for each site in Section 4.1.

The monitored data was analyzed to determine operational patterns that could be used as inputs in simulation models.

Simulation models were developed, and various input parameters were adjusted to calibrate them to actual utility bills.

Input parameter adjustments that were derived from monitored data were updated in the model to approximate post retrofit conditions, and predicted savings were calculated.

Various simulations were made to determine savings for changes in construction, orientation and climate zone.

Results were summarized and compared with savings calculated from utility bills.

Conclusions and recommendations were developed based on the analysis.

4.1 Test Site Description Four test sites were selected to fulfill the project objectives described in Section 3. The intention was to capture a single zone heating and cooling system and a multiple zone facility for each market segment. This was achieved for the two office sites but proved difficult for the retail segment. A summary of the test sites is in the table below.

Table 4-1: Test Site Summary Location Market

Segment HVAC Zones

Climate Zone

EMS Type Utility Rate

Building Floor Area

(sf)

Construction Type

Tracy, CA Office 1 12 HVAC Only A1 2.660 Metal building Oroville, CA Office 3 11 HVAC Only A1P 7,536 Wood Fremont, CA Retail 4 3 HVAC Only A1P 7,392 Strip Mall Fresno, CA Retail 4 13 HVAC Only A10S 7,181 Strip Mall

4.1.1 Tracy Office Test Site This site serves as a service center for PG&E electricians and service personnel. It has regular daytime office hours, but can be accessed 24/7 by electricians. The office is part of a workshop/ storage shed and storage yard. Therefore, there is a significant amount of process load on the same meter as the office. This was discovered during the data analysis period after the post retrofit data had been collected at site. Since the impact of the EMS retrofit was not discernible in the main electric meter data, this site was excluded from further analysis. The facility had manual lighting control and a programmable thermostat for its single rooftop air handling unit (AHU).


Table 4-2: Tracy Office Monitoring Plan Monitored Point Logged

interval Equipment used

Ambient Temperature 15 min Hobo ambient temperature/ RH logger Whole Building Power 15 min Dent 3 phase power logger AHU SAT 15 min Hobo indoor temperature logger Zone Temperature 15 min Hobo indoor temperature logger AHU Power 15 min Dent 3 phase power logger

4.1.2 Oroville Office Test Site This site serves as a customer service center and office for PG&E meter readers and other service personnel. It has regular daytime office hours, but can be accessed after-hours by meter readers working on weekend or swing shifts. The site has three split-system AC units. One serves the telecom room, another serves a conference room and executive office, and the third and largest unit serves the kitchen, restrooms, open office areas and customer lobby. The telecom room AC unit had a vintage mercury bulb thermostat, and the remaining two had advanced programmable thermostats with a touch-screen interface.

The thermostats had been programmed and locked out by the HVAC service personnel, but the occupants had defeated the lockout by following instructions in the user’s manual.

Table 4-3: Oroville Office Monitoring Plan Monitored Point Logged


Ambient Temperature 15 min Hobo ambient temperature/ RH logger Whole Building Power 15 min Dent 3 phase power logger AHU1 SAT 15 min Hobo indoor temperature logger Zone1 Temperature 15 min Hobo indoor temperature logger AHU1 Compressor current 15 min MicroDataLogger AHU1 supply fan current 15 min MicroDataLogger AHU2 SAT 15 min Hobo indoor temperature logger Zone2 Temperature 15 min Hobo indoor temperature logger AHU1 Compressor current 15 min MicroDataLogger AHU1 supply fan current 15 min MicroDataLogger AHU3 SAT 15 min Hobo indoor temperature logger Zone3 Temperature 15 min Hobo indoor temperature logger AHU1 Compressor current 15 min MicroDataLogger AHU1 supply fan current 15 min MicroDataLogger

4.1.3 Fremont Retail Test Site This test site is a dry goods retail store located in a strip mall in Fremont, CA. It operates seven days a week. Store hours are 10am-8pm Monday through Saturday and 11am-7pm Sunday. Employees arrive an hour before the store opens and stay an hour after it closes. Occasional after-hours occupancy occurs when large shipments need to be received. The store hours may


be extended for the holiday shopping season. The store has single pane floor-to-ceiling glazing and glass entry doors facing the street at the front and the pedestrian courtyard at the back. The back entrance ways are permanently locked. The space is conditioned by four rooftop package units with direct expansion cooling and electric resistance heating. AHUs 1, 2 & 4 are 5 tons each and AHU 3 is 10 tons.

Table 4-4: Fremont Retail Monitoring Plan Monitored Point Logged


Ambient Temperature 15 min Hobo ambient temperature/ RH logger Whole Building Power 15 min Dent 3 phase power logger Total Lighting Power 15 min Dent 3 phase power logger AHU1 SAT 15 min Hobo indoor temperature logger Zone1 Temperature 15 min Hobo indoor temperature logger AHU1 total current 15 min MicroDataLogger AHU2 SAT 15 min Hobo indoor temperature logger Zone2 Temperature 15 min Hobo indoor temperature logger AHU2 total current 15 min MicroDataLogger AHU3 SAT 15 min Hobo indoor temperature logger Zone3 Temperature 15 min Hobo indoor temperature logger AHU3 total current 15 min MicroDataLogger AHU4 SAT 15 min Hobo indoor temperature logger Zone4 Temperature 15 min Hobo indoor temperature logger AHU4 total current 15 min MicroDataLogger

4.1.4 Fresno Retail Test Site This test site is a dry goods retail store and is part of the same chain as the Fremont test site. It is located in a strip mall in Fresno, CA. It operates seven days a week. Store hours are 10am-8pm Monday through Saturday and 11am-7pm Sunday. Employees arrive an hour before the store opens and stay an hour after it closes. Occasional after-hours occupancy occurs when large shipments need to be received. The store hours may be extended for the holiday shopping season. The store has single pane floor-to-ceiling glazing and glass entry doors facing the street at the front. The space is conditioned by four rooftop package units with direct expansion cooling and electric resistance heating. AHUs 1, 2 & 4 are 5 tons each and AHU 3 is 10 tons.


Table 4-5: Fresno Retail Monitoring Plan Monitored Point Logged


Ambient Temperature 15 min Hobo ambient temperature/ RH logger Whole Building Power 15 min Dent 3 phase power logger AHU1 SAT 15 min Hobo indoor temperature logger Zone1 Temperature 15 min Hobo indoor temperature logger AHU1 total current 15 min ACR Amp Logger AHU2 SAT 15 min Hobo indoor temperature logger Zone2 Temperature 15 min Hobo indoor temperature logger AHU2 total current 15 min ACR Amp Logger AHU3 SAT 15 min Hobo indoor temperature logger Zone3 Temperature 15 min Hobo indoor temperature logger AHU3 total current 15 min ACR Amp Logger AHU4 SAT 15 min Hobo indoor temperature logger Zone4 Temperature 15 min Hobo indoor temperature logger AHU4 total current 15 min ACR Amp Logger

4.2 Project Timeline The project started with an aggressive timeline, anticipating that a final report could be generated by November 2007. However, there were significant delays in recruiting test sites, resolving network access and server issues, and determining technical details of the EMS installation.

Two dry goods sites originally recruited for the project were unable to commit to implementation within the original project schedule. Therefore, the retail portion of the project was rescheduled for 2008. The two office sites were tested in late 2007. Preliminary results from this study were presented to PG&E in February 2008 with updates provided in April 2008.

The dry goods sites remained unable to meet the revised schedule in 2008 and were reluctantly dropped. This necessitated additional recruiting activities which were successful in late summer 2008. Significant delays were experienced while working out the participation details with the customers, and the baseline monitoring started in December 2008. Controls implementation also proved to be a challenge due to scheduling issues and was not achieved until March 2009. Preliminary results from this study were presented to PG&E in May 2009 with updates provided in July 2009.


Table 4-6 summarizes the milestones for this project.

Table 4-6: Project Timeline

Oroville Office Tracy Office Fremont Retail Fresno Retail Site Selection 09/2007 09/2007 09/2008 09/2008 Baseline Monitoring Begins 10/15/2007 10/01/2007 12/5/2008 12/15/2008 Controls Implementation 11/09/2007 11/21/2007 3/23/2009 3/27/2009 Post-retrofit Monitoring Ends 12/10/2007 12/282007 4/15/2009 4/15/2009 Preliminary Results Reported 02/06/2008 02/06/2008 5/12/2009 5/12/2009 Draft Project Report 02/08/2010


5.0 Project Results

5.1 Summary of Results Two office and two retail sites were included in this study. One of the office sites, in Tracy, was dropped from the study after analysis of monitored data showed inconclusive results. For the three remaining test sites, the predicted annual energy savings ranged from 3% to 27%. The predicted peak demand savings for the same models were modest, ranging from 2.3% to 3.6%. Table 5-1 summarizes the predicted energy savings for the three sites along with implementation costs and simple paybacks. Table 5-1: Summary of Predicted Energy Savings and System Payback by Site

Test site Predicted Energy

Reduction (kWh/y)

Predicted Peak Demand

Reduction (kW)

Cost Reduction ($/y)

Implementation Cost ($)

Simple Payback (yrs)

Tracy Office n/a n/a n/a $760 n/a Oroville Office 10,812 0.4 $1,850 $1,980 1.07 yrs Fremont Retail 15,161 0.9 $2,462 $3,040 1.23 yrs Fresno Retail 3,938 2 $606 $3,040 5.01 yrs

Table 5-2 summarizes the maximum and minimum predicted energy savings for all the simulated alternatives for the office and retail segments. The retail segment shows a wider range of potential savings because it is based on two sites with different operating characteristics. Table 5-2: Summary of Predicted Annualized Energy Savings

Office Retail Maximum predicted energy reduction kWh/y

CZ Façade Orientation

11,036 13

North

15,909 13

West Minimum predicted energy reduction kWh/y

CZ Façade Orientation

8,688 3

South

2,873 3

South

Table 5-3 compares the predicted energy savings with the actual energy savings from monthly utility billing data. The predicted and actual values are fairly close for the office test site, but diverge significantly for both of the retail sites. This is indicative of the range of uncertainty of savings associated with thermostat-based EMS systems.


Table 5-3: Summary of Predictions of, Actual, and Sources of Energy SavingsSite Predicted

Energy Savings kWh/y (%)

Primary Source of Savings in Simulation Model

Actual Energy Savings kWh/y

(%)

Simple Payback Based on Actual

Savings (yrs) Oroville office 10,812 kWh/y

(27%) Fans on two out of three AHUs were operating in continuous mode, which was set to cycle

with loads post retrofit.

10,727 kWh/y (26.4%)

1.08 yrs

Fremont Retail

15,161 kWh/y (17.9%)

Faulty Thermostat reset schedule on one AHU was

causing excessive nighttime heating during winter.

4,693 kWh/y (5.6%)

3.99 yrs

Fresno Retail 3,938 kWh/y (3.0%)

Faulty Thermostat setting on two AHUs was causing some

nighttime heating during winter. Most savings come from

change in thermostat settings.

28,839 kWh/y (22.1%)

0.69 yrs

5.2 Results for Office Sites An energy simulation model was developed for the Oroville office test site. The Tracy site was excluded from this phase because of inconclusive monitoring results. The development of the simulation model was also a challenge as detailed drawings were not available for the Oroville office. Therefore, specific construction types and parameters were assumed to be consistent with minimum Title 24 requirements. All variables were then adjusted to match the model output with the measured energy use.

5.2.1 Monitored Data Figures 5-1 through 5-3 show building electric consumption, daily AC use profiles, and delivered temperatures for the Oroville office site. The analyses of this data are described in the following sections.


Oroville Office Average Daily Electric Profile Use for Weekdays and Weekends

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Figure 5-1: Oroville Office Average Daily Electric Load Profiles

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Figure 5-2: Oroville Office AC-2 Fan Operation


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Figure 5-3: Oroville AHU 1 and 2 Daily Temperature Profiles

5.2.2 Model Calibration A building model based on VisualDOE was developed for the Oroville office site. In calibrating the model, AHU2 was assumed to be running the fan constantly, even though it was found at the time to be running intermittently. Anecdotal information from the technicians and site personnel indicated that the fan did run continuously. This assumption made a more accurate calibration possible, bringing the modeled savings to within the range of the observed savings. The weather file used in the model is that for California climate zone 11. Table 5-4 shows the inputs to the building model.

Figure 5-4 and Table 5-5 show the calibrations used to refine the model and the resulting predicted energy savings. The primary AC savings of about 4% result from changes to the temperature schedule at the time of implementation. The remaining savings come from the cycling of fans rather than having them run continuously. However, the fans will still cycle as needed during the daytime to maintain comfort and do not lead to demand reduction. The demand savings predicted for the calibrated model are modest at 0.4 kW due to a thermostat setpoint change.


Table 5-4: Inputs for Office Site Energy Savings Model Variable Baseline (Calibrated) Post-Retrofit Notes

Cooling Setpoint

AC-1 Monday to Friday 6 AM – 8 AM: 79 ˚F 8 AM – 5 PM: 78 ˚F 5 PM – 6 AM: 82 ˚F Saturday, Sunday all day: 82 ˚F AC-2 Monday to Friday 6 AM – 5 PM: 75 ˚F 5 PM – 6 PM: 82 ˚F Saturday, Sunday all day: 82 ˚F AC-3 All week all hours 77 ˚F

All units Occupied: 75 ˚F Unoccupied: 85 ˚F

Occupied cooling setpoint remains the same for AC-2 but is lowered for AC-1 and AC-3. This will cause higher cooling energy consumption for the two units. The demand reduction might be minimal as the two units are small. Unoccupied cooling setpoint is increased for all units. This will lead to lower energy consumption to maintain setback temperatures.

Heating Setpoint

AC-1 Monday to Friday 6 AM – 8 AM: 70 ˚F 8 AM – 5 PM: 70 ˚F 5 PM – 6 AM: 65 ˚F Saturday, Sunday all day: 62 ˚F AC-2 Monday to Friday 6 AM – 5 PM: 72 ˚F 5 PM – 6 AM: 62 ˚F Saturday, Sunday all day: 65 ˚F AC-3 All week all hours 77 ˚F

Occupied: 70 ˚F Unoccupied: 60 ˚F

Occupied heating setpoint remains the same for AC-1 but is lowered for AC-2 and AC-3. This will lead to lower energy consumption for the latter two units. Unoccupied heating setpoint is decreased for all units. This will lead to lower energy consumption to maintain setback temperatures.

Fan Control AC-1: cycles on demand AC-2: on 24/7 AC-3: on 24/7

AC-1 cycles on demand AC-2 cycles on demand AC-3 cycles on demand

Significant reduction in fan run time for AC-2 and AC-3 will lead to energy savings.


Table 5-5: Monthly Calibrations for the Model Baseline

kWh Utility kWh Calibration Post kWh kWh Saved

Jan 2,927 2503 -14.50% 1,938 34% Feb 2,666 2343 -12.10% 1,760 34% Mar 3,192 2598 -18.60% 2,149 33% Apr 2,995 2741 -8.50% 2,121 29% May 3,659 3392 -7.30% 2,787 24% Jun 4,044 4031 -0.30% 3,238 20% Jul 4,147 4786 15.40% 3,444 17% Aug 4,451 4299 -3.40% 3,623 19% Sep 3,623 3,009 -16.90% 2,792 23% Oct 3,276 2,574 -21.40% 2,291 30% Nov 2,833 2586 -8.70% 1,858 34% Dec 2,858 2621 -8.30% 1,858 35%

Annual 40,671 37483 -7.80% 29,859 27%

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Figure 5-4: Model Calibration Trends


5.2.3 Construction Type Analysis After the building model was calibrated for energy savings, it was run to determine the impact of different construction types on potential energy savings. As the results in Table 5-6 show, construction has minimal impact on energy savings in this model.

Table 5-6: Summary of Predicted Energy Savings with Construction Type CZ Energy Category Wood Steel Concrete

Annual kWh Peak kW Annual kWh Peak kW Annual kWh Peak kW

Baseline 35,374 17.1 34,976 16.4 34,842 16.6 w/ EMS 25,359 16.3 24,902 15.4 25,365 16.2 Reduction 10,015 0.8 10,074 1 9,477 0.4

3

% savings 28% 4.7% 29% 6.1% 27% 2.4% Baseline 40,671 17.2 40,735 16.9 40,735 17.5 w/ EMS 29,859 16.8 29,132 16.5 30,965 17.4 Reduction 10,812 0.4 11,603 0.4 9,770 0.1

11


12


13

% savings 26% 1.1% 26% 1.7% 22% 0.5%

Grey cells present data for the baseline location.

Orange cells present the maximum kWh of savings for all locations and orientations.

Blue cells present the minimum kWh of savings for all locations and orientations.


5.2.4 Building Orientation Analysis The effect of building orientation on energy performance was also analyzed. In this analysis, 16 alternatives were tested. All alternatives use the same construction detailed as “wood construction” in Section 5.2.3. Energy savings due to the EMS varied up to 4% within a climate zone based on orientation. These results can be seen in Table 5-7. The total window area in the model is 214ft2, and the window to wall ratio is 9.0%. The table also gives the percentage of the total window area found in each building wall.

Table 5-7: Summary of Energy Savings by Building Orientation

CZ Energy Category

2

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All 3

East 0% 22% 32% 46%

Annual

kWh Peak kW Annual

kWh Peak kW Annual

kWh Peak kW Annual

kWh Peak kW Baseline 35,211 16.9 34,417 16.6 34,530 16.7 35,344 16.9 w/ EMS 25,359 16.3 24,005 16.4 25,842 16.4 26,513 16.7 Reduction 9,852 0.6 10,412 0.2 8,688 0.3 8,831 0.2

3

% savings 28% 3.6% 30% 1.2% 25% 1.8% 25% 1.2%

Baseline 40,671 17.2 40,130 17 39,897 16.5 40,957 16.8 w/ EMS 29,859 16.8 29,319 16.6 30,539 15.9 31,297 16.3 Reduction 10,812 0.4 10,811 0.4 9,358 0.6 9,660 0.5

114

% savings 27% 2.3% 27% 2.4% 23% 3.6% 24% 3.0%

Baseline 39,822 17.5 39,075 17.4 38,922 17 39,932 17.2 w/ EMS 28,968 17.1 28,458 17 29,719 16.3 30,342 16.7 Reduction 10,854 0.4 10,617 0.4 9,203 0.7 9,590 0.5

12

% savings 27% 2.3% 27% 2.3% 24% 4.1% 24% 2.9%

Baseline 42,908 17.9 42,102 17.9 41,901 17.4 43,224 17.6 w/ EMS 31,872 17.7 31,507 17.5 32,634 16.8 33,509 17.2 Reduction 11,036 0.2 10,595 0.4 9,267 0.6 9,715 0.4

13

% savings 26% 1.1% 25% 2.2% 22% 3.4% 22% 2.3%

Grey cells present data for the Baseline location.

Orange cells present the maximum percentage of savings for all locations and orientations.

Blue cells present the minimum percentage of savings for all locations and orientations.

2 For thumbnails, north is toward top of page 3 Percentages are of the total building window area, 214 ft2, given for each wall orientation. 4 Grey cells are calibrated model


5.2.5 Concentrated Window Area The original monitoring plan assumed an office location with glazing on a single orientation. To get a better look at which window configuration provides the greatest energy savings by installation of the EMS product, we tested the model with all window area concentrated on one wall. This wall is oriented to the north in the actual building. Again, the change in energy savings based on orientation can be up to 4%. The results of this analysis are given in Table 5-8. The total window area in this model is 216 ft2, and the window to wall ratio is 9.1%. From this analysis, the EMS product would be more effective in a building with more windows concentrated on the south and west facades.

Table 5-8: Summary of Orientation Results w/ Windows Moved

CZ Energy Category

5

All Window Orientation6

North East South West

Annual kWh

Peak kW

Annual kWh

Peak kW

Annual kWh

Peak kW

Annual kWh

Peak kW

Baseline 33,062 17.3 35,924 16.4 36,716 17.2 36,233 17 w/ EMS 22,781 16.7 23,572 15.8 23,797 16.6 23,428 16.5 Reduction 10,281 0.6 12,352 0.6 12,919 0.6 12,805 0.5

3

% savings 31% 3.5% 34% 3.7% 35% 3.5% 35% 2.9% Baseline 38,319 16.3 41,796 16.8 42,353 17 42,606 17.4 w/ EMS 27,880 16 29,741 16.5 29,729 16.4 29,848 17.2 Reduction 10,439 0.3 12,055 0.3 12,624 0.6 12,758 0.2

11

% savings 27% 1.8% 29% 1.8% 30% 3.5% 30% 1.1% Baseline 37,275 16.7 40,737 17.3 41,376 17.3 41,516 17.6 w/ EMS 26,827 16.3 28,719 16.8 28,538 16.9 28,603 17.4 Reduction 10,448 0.4 12,018 0.5 12,838 0.4 12,913 0.2

12

% savings 28% 2.4% 30% 2.9% 31% 2.3% 31% 1.1% Baseline 40,224 17.3 44,144 17.7 44,612 17.8 45,037 18 w/ EMS 29,897 16.9 32,067 17.3 32,050 17.5 32,341 17.8 Reduction 10,327 0.4 12,077 0.4 12,562 0.3 12,696 0.2

13

% savings 26% 2.3% 27% 2.3% 28% 1.7% 28% 1.1%


Orange cells present the maximum percent of energy savings for all locations and orientations.

Blue cells present the minimum percent of energy savings for all locations and orientations.

5 For thumbnails, north is toward top of page 6 All windows are on one side of the building model, which corresponds to the north side of the actual building. Window area is 216 ft2.


5.2.6 Savings Persistence One year of post retrofit utility data for the Oroville site showed persistent savings from the installation of the EMS. Figure 5-5 shows nearly five years of monthly utility data. The controls were implemented in November 2007, after which a reduced consumption pattern can be seen. An adjusted baseline was created using a normalization of monthly electric consumption with monthly average ambient temperature. This correlation is shown as developed in Figure 5-6. Demand data was not tracked for this site by PG&E, so persistence of peak demand reduction could not be verified from utility bills.

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Figure 5-5: Oroville Utility Data Comparing Baseline and Post Retrofit Period


Oroville Office Monthly Electric Consumption vs Monthly Average Ambient Temperature

y = 63.526x - 837.12R2 = 0.852

y = 36.452x - 53.653R2 = 0.6837

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Figure 5-6: Oroville Monthly Consumption vs. Monthly Average Temperature

5.3 Results for Retail Sites Retail stores located in strip malls in Fresno and Fremont were monitored to determine the energy savings associated with implementing the e-Stat EMS to control HVAC setpoints. Whole-building and HVAC electricity use were monitored as well as ambient and space temperatures.

5.3.1 Monitored Data Analysis Data was recorded before the implementation of the e-Stat EMS controls for approximately 3½ and 4 months for Fresno and Fremont respectively, and after the implementation for 2 and 3 weeks for Fresno and Fremont respectively. Due to lack of building data, specific properties for wall construction, window type, and equipment performance were assumed. Historical electricity data was gathered from utility bills.

Air handling unit (AHU) performance as a function of outside air temperature (OAT) was examined and is presented in Figure 5-7 and 5-8. Lighting loads can be estimated by taking the difference between the whole-building overnight load and the whole-building electrical load while the HVAC system was not in operation.


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The data showed no clear indication of savings from the implementation of the e-Stat controller. For the Fresno location—except for outside air conditions which required little or no heating or cooling—outside air temperature conditions were different between the baseline and the post-retrofit cases. The baseline was in the heating season while the post-retrofit was in the cooling season. Therefore, no clear indication of savings could be determined. For the Fremont location, outside air temperatures were similar for the baseline and the post-retrofit cases.


However, these temperatures were moderate and required little or no heating or cooling from the AHUs. Therefore, no clear indication of savings could be determined.

Whole-building power in Figure 5-9 and 5-10 alluded to the fact that air-handling units may have been operating overnight in January at the Fresno location and in December, January, February, and early March at the Fremont location. In the post-retrofit case, the air-handling unit operation is more regular.

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Figure 5-10: Fremont Whole-Building Power

The supply air temperatures (SAT) and zone temperatures were plotted against the time of day to uncover any heating setback that may have caused the overnight operation. Figure 5-12 shows that for the Fremont location, Zone 4 had an overnight heating setback. Figure 5-11 shows similar behavior for Fresno Zone 1 and Zone 2. The average zone temperature between 11pm and 7am while the AHU’s SATs were higher than 80o F was used to determine the heating setbacks. The setbacks were 69o F for both Fresno and Fremont.


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Figure 5-12: Fremont AHU 4 Daily Temperature Profiles

5.3.2 Model Calibration An eQUEST building energy model was created using the observed data and the data-derived analyses above. For the building and system data mentioned above that could not be directly determined, modeling was performed using inputs consistent with the vintage of the building and equipment. These model inputs are provided in Appendices 7.3 and 7.4.


The model’s predicted electric energy use is compared to the historical utility data in Figure 5-13 and 5-14. Table 5-9 presents the correlation parameters based on comparing the actual historical energy use and the eQUEST-predicted energy use. Table 5-10 shows the resulting parameters that were used to model energy savings.

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Figure 5-13: Fresno Baseline Model Calibration


Fremont Retail Calibration

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Figure 5-14: Fremont Baseline Model Calibration

Table 5-9: Baseline Model Correlations

Electrical Energy Compared to Historical Average Annual Energy Difference Monthly Correlation3 Fresno1 4.3% 60.6% Fremont2 2.3% 87.8% 1. Average of 2006 through 2009 2. Average of 2008 and 2009

3. This is the squared Pearson product moment correlation coefficient. It is a measure of how well the model curve fits the average utility data curve on a monthly basis.

The energy model showed reasonable correlation for both locations. The Fresno model predicts a noticeably lower heating load than historical data. This lowers any possible savings that may be possible from a heating setback change. Site observations noted that at each location that the doors into the space were left open for long periods of time. The infiltration from open doors adds a level of unpredictability to the calibration and may have affected the historical heating load during this period.


For the years 2006 and 2007, the Fremont location showed irregular energy consumption. Data from 2008 and 2009 at this location were in much better agreement with the correclation. It was theorized that operations and/or equipment may have been changed at some point during this period. Site staff verified that changes had been implemented, but they were unable to provide timing or exact nature of the changes. For this reason, only the years 2008 and 2009 were used in the model calibration.

The model suggests that savings are mostly realized when high heating setbacks can be avoided. These high heating setbacks were theorized to be caused by staff that changed the heating setbacks during the cold months. With the controls only accessible to authorized personnel, it is expected that these high setbacks could be avoided.

Table 5-10: Inputs for Retail Sites Energy Savings Model Variable Baseline (Calibrated) Post-Retrofit Notes

Cooling Setpoint

Fresno Occupied: 72 ˚F Unoccupied: 78 ˚F AHU 1, 2, and 3: Jan, Nov, Dec: 77 ˚F Fremont Occupied: 70 ˚F Unoccupied: 75 ˚F AHU 4: Jan, Feb, Dec: 75 ˚F

Both Sites Occupied: 74 ˚F Unoccupied: 85 ˚F

Occupied and unoccupied setpoint increased for both sites leading to energy & demand savings. Occupied & unoccupied setpoint increased for Fresno leading to energy and demand savings

Heating Setpoint

Fresno Occupied: 68 ˚F Unoccupied: 62 ˚F AHU 1, 2, and 3: Jan, Nov, Dec: 69 ˚F Fremont Occupied: 67 ˚F Unoccupied: 62 ˚F, AHU 4: Jan, Feb, Dec: 69 ˚F

Both Sites Occupied: 69 ˚F Unoccupied: 60 ˚F

Occupied setpoint increased for both sites leading to energy & demand penalty Un-occupied setpoint decreased for both sites leading to energy & Demand savings.

Fan Control Cycle on demand Cycle on demand Cycle on demand

5.3.3 Building Orientation Analysis For the retail sites, only the effects of climate zone and building orientation on energy performance were analyzed. The effects of construction type and concentration of window area were not evaluated. There were 20 alternatives tested for each location (four climate zones and five orientations). These results can be seen in Table 5-11 for Fresno and Table 5-12 for Fremont.

The demand savings predicted for both locations are fairly low. For certain orientations and climate zones, the model even predicts demand penalties. These penalties, however, are within 2% and can potentially be explained by the following factors:

The model is calibrated to about 5%. Differences smaller than this can be attributed to the uncertainty of the model.

The capacities for the AHUs were not auto-sized for each run. As the climate zone and orientation changes, the model sets up a different load profile, causing the HVAC system to operate at a different operating point on its efficiency curve.


Further analysis showed that the peak cooling is happening at different times for baseline vs. post-retrofit cases. This change in cooling times results in different entering wet bulb and outside air temperatures. This yields a different AHU capacity, which, in turn, means a different part-load performance. This could account for the slightly-higher peak load between baseline and post-retrofit cases.

The actual utility data for both test sites (Figure 5-17 and 5-20) also show demand penalties occurring during the post-retrofit period as compared to the baseline period.

Table 5-11: Fresno Energy Savings by Climate Zone and Orientation

CZ Energy Category

SW (Base) North East South West

Annual

kWh Peak kW Annual

kWh Peak kWAnnual

kWh Peak kWAnnual

kWh Peak kW Annual

kWh Peak kW

Baseline 111,809 43.7 103,447 42.7 108,018 43.8 110,349 41.2 110,567 45.1

w/ EMS 108,926 42 100,164 40.7 104,863 41.8 107,476 39.6 107,575 43.2

Reduction 2,883 1.7 3,283 2 3,155 2 2,873 1.6 2,992 1.9 3

% savings 2.6% 3.9% 3.2% 4.7% 2.9% 4.6% 2.6% 3.9% 2.7% 4.2%

Baseline 120,405 50.7 112,355 49.8 118,629 51.3 118,104 49.7 120,560 53.7

w/ EMS 117,014 48.5 108,138 49.2 114,645 50.8 114,812 47.5 116,777 51.8

Reduction 3,391 2.2 4,217 0.6 3,984 0.5 3,292 2.2 3,783 1.9 11

% savings 2.8% 4.3% 3.8% 1.2% 3.4% 1.0% 2.8% 4.4% 3.1% 3.5%

Baseline 120,462 51.8 112,557 53 119,385 54.5 118,099 51 120,853 55.4

w/ EMS 116,974 49.2 108,392 52 115,457 53.9 114,713 48.2 117,065 53.2

Reduction 3,488 2.6 4,165 1 3,928 0.6 3,386 2.8 3,788 2.2 12

% savings 2.9% 5.0% 3.7% 1.9% 3.3% 1.1% 2.9% 5.5% 3.1% 4.0%

Baseline 130,738 58.5 122,441 56.1 129,853 57.6 127,702 56.4 131,653 60.6

w/ EMS 126,800 56.5 118,259 56.6 125,647 58.5 123,839 53.9 127,589 60

Reduction 3,938 2 4,182 -0.5 4,206 -0.9 3,863 2.5 4,064 0.6 13

% savings 3.0% 3.4% 3.4% -0.9% 3.2% -1.6% 3.0% 4.4% 3.1% 1.0%





Table 5-12: Fremont Energy Savings by Climate Zone and Orientation

CZ Energy Category

SW (Base) North East South West

Annual kWh

Peak kW

Annual kWh

Peak kW

Annual kWh

Peak kW

Annual kWh

Peak kW

Annual kWh

Peak kW

Baseline 84,516 30.6 82,624 30.9 85,085 31.6 83,546 31 85,137 31.6

w/ EMS 69,355 29.7 67,888 29.6 69,975 30.5 67,900 29.6 69,978 30.6

Reduction 15,161 0.9 14,736 1.3 15,110 1.1 15,646 1.4 15,159 1 3

% savings 17.9% 2.9% 17.8% 4.2% 17.8% 3.5% 18.7% 4.5% 17.8% 3.2%

Baseline 102,24

5 33.7 100,55

3 33.7 103,63

4 33.6 100,48

6 33.7 103,29

1 33.7

w/ EMS 89,985 34.1 88,092 34 90,834 34 88,082 34 90,871 34.1

Reduction 12,260 -0.4 12,461 -0.3 12,800 -0.4 12,404 -0.3 12,420 -0.4 11

% savings 12.0% -1.2% 12.4% -0.9% 12.4% -1.2% 12.3% -0.9% 12.0% -1.2%

Baseline 98,931 35.3 97,064 35.3 100,01

1 35.7 97,106 35.3 100,01

3 35.7

w/ EMS 86,366 35.6 84,184 35.6 87,362 35.9 84,189 35.6 87,396 35.9

Reduction 12,565 -0.3 12,880 -0.3 12,649 -0.2 12,917 -0.3 12,617 -0.2 12

% savings 12.7% -0.8% 13.3% -0.8% 12.6% -0.6% 13.3% -0.8% 12.6% -0.6%

Baseline 106,69

7 36.5 104,29

3 36.7 107,92

0 37 104,63

9 36.7 107,80

1 37

w/ EMS 90,985 36.9 89,077 37 91,859 37.4 89,088 37 91,892 37.4

Reduction 15,712 -0.4 15,216 -0.3 16,061 -0.4 15,551 -0.3 15,909 -0.4 13

% savings 14.7% -1.1% 14.6% -0.8% 14.9% -1.1% 14.9% -0.8% 14.8% -1.1%

Grey cells present data for the actual location.



5.3.4 Savings Persistence Nine months of post-retrofit utility data were collected for each of the retail sites to document persistence of energy savings. Figure 5-15 shows four years of monthly utility data for Fresno. The controls were implemented in March 2009, after which a reduced consumption pattern can be seen. However an additional source of the savings apparent in the utility bills is the reduction of operational hours for the store in the post-retrofit period. The energy simulation model


adjusted the operational hours for the baseline to match the hours for the post-retrofit period. An adjusted baseline was created using a simple average of monthly electric consumption from the baseline period. A linear regression with monthly average ambient temperature was not appropriate for this site since the data shows both heating and cooling patterns. This is documented in Figure 5-16.

A comparison for the nine months of post-retrofit utility data with adjusted baseline shows 21,629 kWh of savings. If annualized, this increases to 28,839 kWh. The simulation model predicted annual savings of only 3,937 kWh.

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Figure 5-15: Fresno Utility Data Comparing Baseline and Post-Retrofit Periods

Fresno Retail Monthly Electric Consumption vs Monthly Average Ambient Temperature

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Figure 5-17: Fresno Monthly Electric Demand

Figure 5-18 shows four years of monthly utility data for Fremont. The controls were implemented in March 2009, though the consumption pattern is almost similar to the baseline period. An adjusted baseline was created using a simple average of monthly electric consumption from the baseline period. A linear regression with monthly average ambient temperature was not appropriate for this site since the data shows both heating and cooling patterns. This is documented in Figure 5-19.

A comparison for the nine months of post-retrofit utility data with adjusted baseline shows 3,520 kWh of savings. If annualized, this increases to 4,693 kWh. The simulation model predicted annual savings of 15,161 kWh.


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Figure 5-18: Fremont Utility Data Comparing Baseline and Post-Retrofit Periods

Fresno Retail Monthly Electric Consumption vs Monthly Average Ambient Temperature

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il

May

June July

Augu

st

Sept

em

Oct

ober

Nov

emb

Dec

emb

Elec

tric

Dem

and

(kW

)

2009 2008 2007 2006

Figure 5-20: Fremont Monthly Electric Demand

5.4 Customer Satisfaction Findings A formal customer satisfaction survey was not part of the project. However, feedback was collected on site about general operations before and after the EMS installations. Anecdotes from these encounters are summarized here.

The e-Stats were installed closer to the communication hubs, which were in restricted access areas in all four test sites. This greatly reduced the ability of the occupants to take frequent advantage of the timed override capabilities of the system. This is potentially a positive outcome for consistent system operation, but the lack of control by occupants was perceived negatively.

The HVAC technicians servicing all four test sites had generally positive feedback. The ability to remotely troubleshoot any problems and the potential to avoid traveling to the site were cited as the main benefits.

5.5 Issues Identified by the Equipment Installation Contractor

The AEC investigating engineer observed the installation at the Oroville office site. The thermostats were installed by different personnel for each AC unit, and all had generally positive comments about the installation.

For the office sites, PG&E chose to host the main server within its network firewall, which led to certain software, hardware, and communication setup issues. Through the course of this setup it was observed that communication between the field technicians installing the controls and the IT department was not seamless. The thermostats need a unique IP address which needs to be assigned by the IT department.


6.0 Conclusions and Recommendations

Results show that savings from the thermostat-based EMS system were often dependent on specific operational anomalies that existed prior to control implementation. For the three test sites that were analyzed in detail in this project, Table 6-1 summarizes the apparent sources of savings (as assumed in the simulation models) for each location.

Table 6-1: Summary of Sources of Energy SavingsSite Predicted Energy

Savings kWh/y (%) Primary Source of Savings in

Simulation Model Actual Energy Savings

kWh (%) Oroville office 10,812 kWh/y

(27%) Fans on two out of three AHUs were

operating in continuous mode, which was set to cycle with loads post retrofit.

10,727 kWh/y (26.4%)

Fremont Retail 15,161 kWh/y (17.9%)

Faulty thermostat reset schedule on one AHU was causing excessive nighttime

heating during winter.

4,693 kWh/y (5.6%)

Fresno Retail 3,938 kWh/y (3.0%)

Faulty thermostat setting on two AHUs was causing some nighttime heating

during winter. Most savings come from change in thermostat settings.

28,839 kWh/y (22.1%)

Only the results for the Oroville office site show close agreement between the predicted and measured energy savings. The operational anomalies that were causing excessive energy use at the Oroville site were consistent and were easy to identify and quantify. The primary source of energy savings was correcting the operational mode of two AHU fans from continuous to intermittent.

The retail sites however, had operational anomalies that were not occurring consistently and were difficult to quantify. The primary anomaly was excessive heating usage during unoccupied periods for some of the units at both sites. Monitored data captured only a snapshot of this anomaly, and assumptions were made to extrapolate this data for the whole year. Anecdotal information was not available to corroborate the assumptions used in the analysis.

The hypothesis guiding this project was that a deemed savings model can be created based on the analysis and simplification of variables involving the operation of a small EMS—including market segment, load type, climate zone, building use schedule, building construction type. This was to be achieved by testing the reliability of thermostat-based EMS systems and collecting operational information to calibrate computer models and determine the feasibility of offering a deemed rebate program for this technology.

The results show that three of the four test sites did see a reduction in annual energy consumption, indicating that implementation of thermostat-based EMS systems is a reliable method of achieving energy savings. However for both the retail sites, the actual savings vary significantly from the predicted values. This indicates that developing a deemed savings model based on a limited set of variables is difficult, and substantially more site data and analysis than performed in this project would be required.


7.0 Appendices

7.1 Control System Installation Process and Photos The following installation information was provided by LightStat personnel.


7.2 Screenshots from e-Stat Web Interface The graph shows a snapshot every 10 minutes. The orange line is the supply temperature; the blue line is the cooling setpoint; the red line is the heating setpoint; and the green line is the space temperature. The light pink and dark pink bands show the stages of heating. These bands will be blue when the unit is calling for cooling. The lighter colored band is stage one and the darker colored band is stage two.

Oroville Office site


Tracy Office Site

Screenshots from the retail sites were requested but not provided by the customer.


7.3 Supporting Documentation for Office Sites

Table 7-1: Input Assumptions for Oroville Office Model

Building Criteria Baseline Calibrated Model Alternate Models Climate Zone California CZ11 California CZ11 CZ3,

CZ12 CZ13

Roof Construction Wood Frame R11, 50% abs Wood Frame R11, 50% abs Metal Frame R11 30%abs 4” Concrete 70%abs w/ 1”

ins. Wall Construction Wood Frame 16”o.c. R11 Wood Frame 16”o.c. R11 90.1 Steel Frame wall

R13+R10c.i., 4” Concrete, RADVA, 70%

abs Fenestration

(%WWR) 5.7% 5.7% Same as Baseline

Glazing Single Bronze 6mm Single Bronze 6mm Same as Baseline

Lighting Power Density

1.00 1.00 Same as Baseline

Equipment Power Density

1.00 1.00 Same as Baseline

System Type Three Packaged Single Zone Units

Three Packaged Single Zone Units

Same as Baseline

Cooling Setpoints [oF]

AC-1 Monday to Friday 6 AM – 8 AM: 79 ˚F 8 AM – 5 PM: 78 ˚F 5 PM – 6 AM: 82 ˚F Saturday, Sunday All day: 82 ˚F AC-2 Monday to Friday 6 AM – 5 PM: 75 ˚F 5 PM – 6 PM: 82 ˚F Saturday, Sunday All day: 82 ˚F AC-3 All week all hours 77 ˚F



Heating Setpoints [oF]

AC-1 Monday to Friday 6 AM – 8 AM: 70 ˚F 8 AM – 5 PM: 70 ˚F 5 PM – 6 AM: 65 ˚F Saturday, Sunday All day: 62 ˚F AC-2 Monday to Friday 6 AM – 5 PM: 72 ˚F 5 PM – 6 AM: 62 ˚F Saturday, Sunday




All day: 65 ˚F AC-3 All week all hours 77 ˚F

Fan control AC-1: cycles on demand AC-2: on 24/7 AC-3: on 24/7

AC-1: cycles on demand AC-2: cycles on demand AC-3: cycles on demand

AC-1: cycles on demand AC-2: cycles on demand AC-3: cycles on demand


7.4 Supporting Documentation for Retail Sites

Figure 7-1: Fresno Geometry and Zoning

Figure 7-2: Fremont Geometry and Zoning



Table 7-2: Envelope Assumptions

Feature Fresno Baseline Fremont Baseline Notes/Source

Wall Construction NE and SW: 1" stucco, 1/2" fiber sheathing, R-19 batt in 24" OC steel frame, 1/2" gyp board. SE & NW: Adiabatic

NE and SW: 1" stucco, 1/2" fiber sheathing, R-19 batt in 24" OC steel frame, 1/2" gyp board. SE & NW: Adiabatic

Interior Walls Air walls Air walls Inter-zone walls were modeled as air walls so that individual AHUs could be modeled while still modeling the heat transfer between zones that do not have partitions.

Roof Construction 3/8" Built-up roof, 1" polyurethane, 5/8" plywood, R-19 batt in metal frame.

3/8" Built-up roof, 1" polyurethane, 5/8" plywood, R-19 batt in metal frame.

Glazing properties SC = 1.0 U-factor = 0.49

SC = 0.71 U-factor = 1.02

Window-to-Wall Ratio NE = 0.0 SW = 45% SE = 0.0 NW = 0.0

NE = 45% SW = 45% SE = 0.0 NW = 0.0

Overhangs, shading devices, light shelves

7' overhang on SW 7' overhang on NE & SW

Table 7-3: Internal Load Assumptions Fresno Baseline Fremont Baseline

Occupancy Type

People [ft2/person]

Lights [W/ft2] & Controls

Equipment [W/ft2]

Outside Air

[cfm/ft2]People

[ft2/person]

Lights [W/ft2] & Controls

Equipment [W/ft2]

Outside Air

[cfm/ft2]

Retail 20 2.3 0.25 0.25 20 1.38 0.25 0.25Office 100 2.3 0.75 0.15 100 1.38 0.75 0.15Storage 333 2.3 0.00 0.15 333 1.38 0.00 0.15Restroom 100 2.3 0.10 0.15 100 1.38 0.10 0.15

Weekday

00.10.20.30.40.50.60.70.80.9

1

0 - 1

3 - 4

6 - 7

9 - 10

12 - 1

3

15 - 1

6

18 - 1

9

21 - 2

2

Hour

Occ

upan

cy F

ract

ion

Saturday

00.10.20.30.40.50.60.70.80.9

1

0 - 1

3 - 4

6 - 7

9 - 10

12 - 1

3

15 - 1

6

18 - 1

9

21 - 2

2

Hour

Occ

upan

cy F

ract

ion

Sunday

00.10.20.30.40.50.60.70.80.9

1

0 - 1

3 - 4

6 - 7

9 - 10

12 - 1

3

15 - 1

6

18 - 1

9

21 - 2

2

Hour

Occ

upan

cy F

ract

ion

Holiday

00.10.20.30.40.50.60.70.80.9

1

0 - 1

3 - 4

6 - 7

9 - 10

12 - 1

3

15 - 1

6

18 - 1

9

21 - 2

2

Hour

Occ

upan

cy F

ract

ion

Figure 7-3: Fresno & Fremont Occupancy Schedules

Table 7-4: System Assumptions

Fresno Baseline Fremont Baseline Post-Retrofit

AHU 1, 2, 3, and 4

Designation

Type Packaged Single-Zone Packaged Single-Zone

Cooling

Specification Packaged DX, 10 ton, 8.0 EER

Packaged DX, AHU 1, 2, and 4: 5 ton, 10.0 SEER AHU 3: 10 ton, 10.3 EER

Supply temperature [oF] 55 ˚F constant minimum 55 ˚F constant minimum

Heating

Specification Electric resistance Electric resistance

Supply temperature [oF] 95, constant maximum 95, constant maximum

Fan

Specification Supply Draw through 1" WG @ 53% total efficiency

Supply Draw through 1" WG @ 53% total efficiency

Control Constant volume, intermittent; cycles on during unoccupied hours for any zone out of setpoint

Constant volume, intermittent; cycles on during unoccupied hours for any zone out of setpoint

Outside Air

Ventilation Fixed at minimum ventilation rate

Fixed at minimum ventilation rate

Economizer Setpoint [oF] None None

Zones

Areas served Single zone systems serve each respective zone

Single zone systems serve each respective zone

Cooling Setpoints [oF] Occupied: 72 ˚F Unoccupied: 78 ˚F AHU 1, 2, and 3:

Jan, Nov, Dec: 77 ˚F

Occupied: 70 ˚F Unoccupied: 75 ˚F AHU 4:

Jan, Feb, Dec: 75 ˚F

Occupied: 74 ˚F (increase) Unoccupied: 85 ˚F (increase)

Heating Setpoints [oF] Occupied: 68 ˚F Unoccupied: 62 ˚F AHU 1, 2, and 3: Jan, Nov, Dec: 69 ˚F

Occupied: 67 Unoccupied: 62, AHU 4: Jan, Feb, Dec: 69 ˚F

Occupied: 69 ˚F (increase) Unoccupied: 60 ˚F (decrease)

Fan Control Cycle on demand Cycle on demand Cycle on demand

Terminal Box None None


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