Connecticut Light & Power Company

Prepared for:

Connecticut Light & Power Company

Western Massachusetts Electric Company

United Illuminating Company

Prepared by:

179 Main Street

Middletown, CT 06457

(860) 346-5001

Table of Contents

1Introduction

Project Objectives

Summary of Approach

2Methodology

Task 1: Project Initiation

Task 2: Sample Design Development

Data Review of Study Population

Sample Design & Selection

Task 3: Site Work Preparation

Recruiting and Scheduling

Task 4: Data Collection

Verbal Data Collection

Logger Placement

Task 5: Analysis

Aggregation of Audited Data

Integration of Monitored Data

Statistical Expansion

3Results

Baseline Hours by School Type

Baseline Hours by Room Type

Baseline Lighting Profiles by Room Type

Baseline Lighting Profiles by Other Analysis Sectors

Baseline Peak Coincidence

Occupancy Sensor Savings Potential by Room Type

Occupancy Sensor Peak Coincidence and Savings

Tables and Figures

Table 1: Number of Schools by Category of Interest

Table 2: Multi-Dimensional Sample Design, by Enrollment and Sector

Table 3: Expected Precision by Primary Analysis Sector

Table 4: Expected Precision by Secondary Analysis Sector

Table 5: Final Sample Recruitment

Table 6: Room-Level Inventory (RLWID 24)

Table 7: Fixture Codes (RLWID 24)

Table 8: Reported Hours per Day Type (RLWID 24)

Table 9: Reported Results by Room Type (RLWID 24)

Table 10: Reported and Monitored Results by Room Type (RLWID 24)

Table 11: Occupancy/Lighting Status by Room Type (RLWID 24)

Table 12: Final Case Weights

Table 13: Baseline Lighting Hours by School Type

Table 14: Baseline Lighting Hours by Room Type

Table 15: Baseline Annual Lighting Hours and Peak Coincidence

Table 16: Occupancy/Lighting Status by Room Type

Table 17: Occupancy Sensor Savings Potential by Room Type

Table 18: Occupancy Sensor Annual Lighting Hours and Peak Coincidence

Table 19: Occupancy Sensor Annual Hours Saved

Figure 1: Study Analysis Flow

Figure 2: Optimal Sector Design for Schools

Figure 3: Scatter Plot of Lighting kW vs. kWh

Figure 4: Baseline Lighting Hours by School Type

Figure 5: Baseline Lighting Hours by Room Type

Figure 6: Baseline Lighting Profile – Auditorium

Figure 7: Baseline Lighting Profile – Cafeteria

Figure 8: Baseline Lighting Profile – Classroom

Figure 9: Baseline Lighting Profile – Gymnasium

Figure 10: Baseline Lighting Profile – Hallway

Figure 11: Baseline Lighting Profile – Kitchen

Figure 12: Baseline Lighting Profile – Library

Figure 13: Baseline Lighting Profile – Locker Room

Figure 14: Baseline Lighting Profile – Mechanical Room

Figure 15: Baseline Lighting Profile – Office

Figure 16: Baseline Lighting Profile – 'Other'

Figure 17: Baseline Lighting Profile – Restroom

Figure 18: Baseline Lighting Profile – Storage Closet

Figure 19: Baseline Lighting Profile – Teacher Lounge

Figure 20: Baseline Weekday Lighting Profile by School Level

Figure 21: Baseline Weekday Lighting Profile by School Funding

Figure 22: Baseline Weekday Lighting Profile by School Type

Figure 23: Baseline Weekday Lighting Profile by School Locale

Figure 24: Baseline Weekday Lighting Profile by Service Territory

Figure 25: Occupancy Sensor Status by Room Type

Connecticut & Massachusetts Utilities

2004-2005 Hours of Use for School Buildings Baseline Study______Page 1

2004-2005 Lighting Hours of Use for

School Buildings Baseline Study

Final Report

1Introduction

RLW Analytics, Inc. is pleased to submit this report for a Baseline Study of Lighting Hours of Use in School Buildings in Connecticut and Massachusetts. RLW has teamed with Practical Energy Solutions (PES), a Connecticut based company that manufactures and specializes in the installation and analysis of the Sensor Switch TOU loggers, an instrument that monitors both occupancy and lighting operation in the same compact logger.

Project Objectives

CL&P and WMECO offer occupancy sensors through their Municipal Program, New Construction Program, and Express Programs. UI installs occupancy sensors in their Energy Opportunities Program and Energy Blueprint Program.

The current program savings estimates are based upon hours of use that reflect the traditional uses of school buildings, which include educational, athletic, and dance functions. However, in recent years, more and more school buildings have been used for other purposes such as community events and college evening classes. This increased use is not currently captured in program savings assumptions and some suspect that the impact of occupancy sensor installations is being underestimated. In large part, the purpose of this study is to inform a better estimate of lighting use prior to sensor installation in the interest of more accurately estimating the impact of occupancy sensor controlled lighting.

Throughout this report, the term “baseline hours” is used to refer to the number of hours that a given unit of lighting operates across a typical year (i.e. “annual operating hours” or “annual hours”) prior to the installation of automatic lighting controls. For the purposes of this definition, these controls include, but are not limited to, occupancy sensors, daylight controls, time clocks, and a variety of direct digital controls (DDC).

The objectives of this study were to perform a credible estimation of baseline lighting operating hours in public and private school buildings by a variety of dimensions of interest, including school classification, demographics, room type, and room use before occupancy sensor installation. The study also provides patterns of lighting use including when the lights are on and the room is occupied and not occupied and when the lights are off in each occupancy situation. By providing this information, this study can be used to reassess the value of installing occupancy sensors in school buildings.

Summary of Approach

In pursuit of the evaluation objectives, RLW performed the following activities:

• A review of data sources preceded and informed the development of anefficient sampling plan for the selection of schools for on-site surveys, metering, and interviews.
• Data collection was performed at each of eighty (80) schoolsto assess the effects of operating schedules, behavioral factors, and demographics on lighting usage patterns.
• Direct measurement with 646 occupancy/lighting loggers occurred from May through October 2005, spanning both in-session and out-of-session timeframes. In total, RLW collected over one million records of lighting on/off and room occupied/unoccupied transitions.
• Analysis included the calculation of baseline annual hours of use by school type, room type, and other factors such as a more detailed room use, rural vs. urban and building age.
• This report of findings is comprehensive and includes all pertinent reporting dimensions, methodologies, and recommendations.

2Methodology

This section describes the approach employed toward completing this study, with each task presented in series below.

Task 1: Project Initiation

A project initiation meeting with key RLW personnel, sponsoring utility project managers, and non-utility party advisors (collectively referred to hereafter as ‘study team’) was held in October 2004. A key item for this meeting was discussion of the sample plan and design, as this element would be critical to ensuring that the study adequately meets sponsor objectives at the desired level of precision. The meeting included a full review of the analytical, data collection, and reporting methods to be applied to this study. The kickoff meeting served as a forum for the study team to discuss and finalize the study approach, schedule, and budget.

Task 2: Sample Design Development

Data Review of Study Population

The proposed analytical approach for this study relied upon a strong statistical characterization of both the study population and sample. RLW requested transfer of appropriate tracking and billing system information for all schools in the utilities’ service territories at the project initiation meeting. The availability and breadth of these data were critical to the development of an appropriate and rigorous sampling plan for this study. Program tracking systems from each sponsor would help ensure that the population from which the sample is pulled is free of ‘participants’ or schools with occupancy sensors already installed. Billing data were to be used to develop an estimate of annual consumption with which to stratify the study population of schools.

After much time and deliberation, RLW concluded that one could not definitively identify all of the schools in the utilities’ customer billing systems. Without a complete extract of schools, researchers would be unable to construct a valid population dataset of all eligible schools of interest. Annual energy consumption was undoubtedly the strongest candidate for the sample design’s key explanatory variable. RLW expanded its search to other potential data sources for individual Connecticut and Massachusetts schools.

In the end, the project team settled upon data from the National Council for Educational Statistics (NCES). While lacking energy usage indicators, NCES data proved to be accurate and comprehensive with regard to all other required information on schools throughout the United States.

Sample Design & Selection

This section details RLW’s approach to developing an appropriate research sample of baseline lighting participants. Model Based Statistical Sampling (MBSS) techniques were employed to develop a sample that is:

1. Efficient - yielding maximum results cost-effectively from a small sample size;
2. Accurate - targeted to achieve ±10% relative precision at the 90% confidence interval overall; and
3. Reliable - based upon program characteristics achieved in this or similar programs.

An hours-of-use study employs a different sampling strategy than an impact evaluation. It is well established in the statistical community that stratified statistical sampling is the preferred technique for developing statistically confident results at target precision levels while minimizing sample size requirements. In order to stratify, one seeks a numeric descriptor, or explanatory variable, with which to sort and divide the population. Larger schools generally have more lights in most space types, and thus should have a greater weight and influence on the average hours of use. With total energy usage by school unavailable, RLW used the best available characterization of school size for the entire study population – total student enrollment – to tailor the sampling fractions to be higher for larger schools.

Unlike energy and demand, operating hours is not an additive parameter; one cannot sum multiple estimates of operating hours to attain the aggregate estimate. Thus, one must in essence average the estimates, weighting them by an appropriate variable. Since this study strived to establish baseline lighting operating hours for use in refining estimates of energy savings, the most relevant weight was connected demand, as the energy usage of the lights is the product of the connected demand times the operating hours of the lights. Expressed differently, the annual operating hours for a space is the annual kWh consumption of the lights divided by its connected lighting load. This ratio – annual kWh over connected demand – was the central interest in the statistical ratio estimation analysis.

The choice of error ratio is of central importance to any sample design. The error ratio measures the 'variation' between numerator (y) and denominator (x) variables in the ratio of interest. Here y is annual kWh and x is connected kW, both of which represent the lighting load in the space type of interest, and y/x is the average annual operating hours for the space.

The error ratio parameter represents the expected variation in operating hours over the average operating hours. With no comparable analytical precedent of error ratio for an hours-of-use study of this nature, a conservative first approximation of the expected error would be:

Error Ratio = 1,000 hours variation / 2,000 annual hours = 0.5

where 1,000 hours is one standard deviation and 2,000 hours per year is the expected average value.

Table 1 tabulates the number of schools in the population across various categories of interest. Considerable pre-analysis, data cleaning and screening was performed to formulate this study population. RLW began with 3,193 public schools in CT and MA and 1,233 private schools, for a total of 4,426 schools. Of these schools, RLW mapped 1,720 into the CL&P, UI, and WMECo service territories.

Category / Number of Schools / Category / Number of Schools
Public 'Standard' / 1,088 / Type: 'Standard' / 1,399
Public Vo/Tech / 18 / Type: Vo/Tech / 19
Public Magnet / 23 / Type: Magnet / 23
Public Charter / 20 / Type: Charter / 20
Private ‘Standard’ / 312 / Level: Primary / 932
Funding: Public / 1,149 / Level: Middle / 250
Funding: Private / 312 / Level: High / 279
Locale: Urban / 401 / Utility: CL&P / 1,003
Locale: Suburban / 756 / Utility: UI / 266
Locale: Rural / 304 / Utility: WMECO / 192

Table 1: Number of Schools by Category of Interest

In order to examine results by educational level, RLW developed data processing routines to split schools into Primary, Middle, and High schools based upon the number of students in each grade taught at the facility. Finally, the project team decided to exclude several unique types of schools from the study (e.g. Montessori, Special Education, Preschool, etc.) in order to focus resources on predominant schools types. This process yielded a final population dataset containing a grand total of 1,461 qualifying schools in the CL&P, UI, and WMECo service territories.

The aforementioned categories were chosen collaboratively by the study team as the major dimensions of interest for this study. In other words, the study team wanted to yield results with reasonable precision in these specific sectors. The team decided that the first hybrid categorization of four public school types and one private school type (shaded region) was to be the primary target for this sample design.

Having defined the population and established a confident estimate of error ratio, RLW then proceeded with the sample design. The study team considered various alternatives with smaller sample sizes, but given the team’s interest in several different dimensions, they investigated and settled upon the multi-dimensional sample design presented below in Table 2.

Maximum / Total / Population / Sample / Weight
Stratum / Value / Enrollment / Size (N) / Size (n) / (N/n)
Public 'Standard' School
1 / 474 / 176,914 / 550 / 14 / 39.3
2 / 748 / 197,591 / 338 / 14 / 24.1
3 / 4,000 / 228,432 / 200 / 14 / 14.3
Sector Total / 602,937 / 1,088 / 42
Public Vo/Tech School
1 / 593 / 3,714 / 7 / 1 / 7.0
2 / 732 / 4,103 / 6 / 2 / 3.0
3 / 2,000 / 4,413 / 5 / 2 / 2.5
Sector Total / 12,230 / 18 / 5
Public Magnet School
1 / 357 / 2,768 / 11 / 2 / 5.5
2 / 536 / 2,680 / 6 / 2 / 3.0
3 / 1,000 / 3,731 / 6 / 2 / 3.0
Sector Total / 9,179 / 23 / 6
Public Charter School
1 / 180 / 1,285 / 11 / 2 / 5.5
2 / 298 / 1,576 / 6 / 2 / 3.0
3 / 2,000 / 2,014 / 3 / 2 / 1.5
Sector Total / 4,875 / 20 / 6
Private School
1 / 228 / 21,178 / 174 / 7 / 24.9
2 / 362 / 24,137 / 87 / 7 / 12.4
3 / 2,000 / 28,522 / 51 / 7 / 7.3
Sector Total / 73,837 / 312 / 21
GRAND TOTAL / 703,058 / 1,461 / 80

Table 2: Multi-Dimensional Sample Design, by Enrollment and Sector

RLW ran numerous iterations in order to optimize coverage and expected relative precision across analysis segments.

Table 3 presents the expected precision for a sample of 80 schools according to the sample design presented above. In total, RLW expected to achieve ±10.9% relative precision on the overall estimate of annual operating hours. By primary analysis sector – the categorization selected as the sampling framework in Table 2 – the expected precision ranges from ±12.5% for public ‘standard’ schools to ±30.9% for public magnet schools.

Population / Sample / Expected
School Type / Size (N) / % of Total / Size (n) / Precision
Public 'Standard' School / 1088 / 74% / 42 / 12.5%
Public Vo/Tech School / 18 / 1% / 5 / 24.2%
Public Magnet School / 23 / 2% / 6 / 30.9%
Public Charter School / 20 / 1% / 6 / 27.4%
Private School / 312 / 21% / 21 / 20.6%
Grand Total / 1461 / 80 / 10.9%

Table 3: Expected Precision by Primary Analysis Sector

Throughout the project development stage, it was clear that the study team were interested in obtaining results across a great many dimensions:

Classification: Elementary, Middle, and High

Funding: Public, Private

Special: Vocational, Technical, Charter, Magnet

Location: Rural, Suburban, Urban

Utility: CL&P, UI, WMECo

Room type: Auditorium, cafeteria, classroom, gymnasium, hallway, kitchen, library, locker room, mechanical room, office, restroom, storage closet, teacher's lounge, and ‘other’ spaces[1].

Classroom usage: Kindergarten, computer lab, music education, chemistry lab, lecture hall, etc.

Vintage: Less than 5 years old, 5 to 15 years old, over 15 years old

RLW moderated discussions with the study team utilities in order to consolidate and prioritize this list, as it would be unlikely to attain statistically significant results in all of these dimensions within available budget resources.

Next, RLW investigated the expected precision across a number of additional secondary analysis sectors. These dimensions were of the most interest to the study team, and RLW worked to prioritize them accordingly. The sample allocation and resultant precision were steered towards focusing precision upon more important sectors (like public vs. private schools) and relaxing precision in less important sectors such as utility company. In addition to ±10% overall, RLW strived to attain ±20% by public/private class and no worse than ±30% in any of the following sectors.

Population / Sample / Expected
School Type / Size (N) / % of Total / Size (n) / Precision
By Funding Source
Public / 1149 / 79% / 59 / 11.8%
Private / 312 / 21% / 21 / 20.6%
By School Locale
Urban / 401 / 27% / 30 / 20.5%
Suburban / 756 / 52% / 35 / 16.7%
Rural / 304 / 21% / 15 / 25.4%
By Type Classification
'Standard' School / 1399 / 96% / 63 / 11.3%
Vo/Tech / 19 / 1% / 5 / 31.8%
Magnet / 23 / 2% / 6 / 29.1%
Charter / 20 / 1% / 6 / 27.3%
By Educational Level
Primary / 932 / 64% / 38 / 16.6%
Middle / 250 / 17% / 17 / 23.7%
High / 279 / 19% / 25 / 19.3%
By Electric Utility
CL&P / 1003 / 69% / 48 / 14.1%
UI / 266 / 18% / 15 / 27.7%
WMECO / 192 / 13% / 17 / 27.3%

Table 4: Expected Precision by Secondary Analysis Sector

In Table 4, one sees that the sample design was able to achieve these objectives. The interactivity of the sectors – that improving coverage in one sector can degrade the results in another – made this sector validation quite challenging. The ‘worst’ precision in these dimensions would be expected in vocational/technical schools and the best in public schools.

Ultimately, the final estimates of relative precision would be dependent upon the success at recruiting and scheduling site visits according to this specific design.

Task 3: Site Work Preparation

After the work scope and sample design were established, two concurrent tasks were initiated in preparation for the on-site data collection. Senior engineering staff created structured data collection instruments to ensure that field personnel collected information of the necessary quality and comprehensiveness to support the study. Having selected the on-site research sample, dedicated and experienced analysts assumed responsibility for recruiting schools for this baseline study and scheduling appointments for the field engineers.