ENERGY STAR Score for Worship Facilities in Canada

ENERGY STAR Portfolio Manager

Technical Reference

Overview

The ENERGY STAR Score for Worship Facilities in Canada applies to buildings that primarily function as places of worship, such as churches, temples, mosques, synagogues, and meetinghouses. The objective of the ENERGY STAR score is to fairly assess how a property’s energy use measures up against similar properties considering the climate, weather, and business activities. A statistical analysis of the peer building population is performed to identify the aspects of property activity that are significant drivers of energy use and to normalize for those same factors. The result of this analysis is an equation that predicts the energy use of a property, based on its business activities. This prediction is compared to the property’s actual energy use to yield a 1 – 100 percentile ranking in relation to the national population of properties.

  • Property types. The ENERGY STAR score for Worship Facilities in Canada applies to buildings that are used as religious worship facilities. This includes churches, temples, mosques, synagogues, meetinghouses, or any other buildings that primarily function as religious worship facilities. The ENERGY STAR score applies to individual worship facility buildings only and is not available for campuses.
  • Reference data. The analysis for worship facilities in Canada is based on data from the Survey of Commercial and Institutional Energy Use (SCIEU), which was commissioned by Natural Resources Canada (NRCan) and carried out by Statistics Canada and represents the energy consumption year 2019.
  • Adjustments for weather and business activity. The analysis includes adjustments for:
    • Weekly Operating Hours
    • Worship Seats
    • Ceiling Height
    • Weather and Climate (using Heating and Cooling Degree Days, retrieved based on postal code)
    • Percent of the Building that is Heated and Cooled
  • Release date. August 2023 is the original release date of the ENERGY STAR score for Worship Facilities in Canada.

This document details the calculation of the 1 – 100 ENERGY STAR score for Worship Facilities. For more information on the methodology used to set up ENERGY STAR scores, go to the Technical Reference for the ENERGY STAR Score (PDF, 709 KB).

The following sections explain how the ENERGY STAR score for worship facilities is developed:

Reference data & filters

The reference data used to form the peer property population relies on the Survey on Commercial and Institutional Energy Use (SCIEU), which was commissioned by Natural Resources Canada and conducted by Statistics Canada. The energy data for the survey was from the calendar year 2019. The raw data file for this survey is not publicly available, but a report providing summary results will be available on Natural Resources Canada’s website at: National Energy Use Database (NEUD).

Four types of filters are applied to analyse the building energy and operating characteristics in the survey. They are set to define the peer group for comparison and to overcome any technical limitations. Those filters are Building Type Filters, Program Filters, Data Limitation Filters, and Analytical Filters.

A complete description of each category is given in the Technical Reference for the ENERGY STAR Score (PDF, 709 KB). Figure 1 summarizes each filter used to develop the ENERGY STAR score for Worship Facility model and the rationale that supports the filter. After all filters are applied, the remaining data set has 380 observations. Due to the confidentiality of the survey data, NRCan is not able to identify the number of cases after each filter.

Figure 1 – Summary of Filters for the ENERGY STAR Score for Worship Facilities
Condition for Including an Observation in the Analysis Rationale
Defined as Worship Facility in SCIEU2019 The SCIEU survey covered the commercial and institutional sector and included buildings of all types. For this model, only the observations identified as primarily Worship Facility are used.
Must be more than 50% Worship Facility and less than 50% of any other building type Building Type Filter – To be considered as a worship facility, the building must have a minimum amount of Worship Facility space.
Must have electricity consumption data Program Filter – worship facilities that do not use electricity are rare or non-existent and may indicate an omission in energy data. Electricity can be grid-purchased or produced on site.
Must be built in 2018 or earlier Data Limitation Filter – The survey reported the energy consumption data for calendar year 2019. Therefore, if the building was being built in 2019, a full year of energy data would not be available.
Must not include energy supplied to other buildings Data Limitation Filter – The survey asked whether the energy reported at the facility included energy supplied to other buildings such as a multi-building complex or portables. Usage data may not have been included; therefore, buildings were removed.
The area of the indoor or partially enclosed parking structures must be less than 50% of the gross floor area including indoor and partially enclosed parking structures Program Filter – If the combined area of parking structures is more than 50% the area of the Worship Facility building, the overall structure is classified as a parking structure, not as a worship facility.
The size of the vacant space must be less than 50% of the gross floor area Program Filter – Occupancy needs to be greater than 50% for Worship Facility to meet ENERGY STAR certification requirements.
More than 50% of the building must be heated Program Filter – More than 50% of a worship facility must be heated for it to be considered a worship facility.
Must be at least 92.9 m2 Program Filter – To be considered as a worship facility, buildings must be at least 92.9 m2 (1,000 sq. ft.) in Canada.
Must operate for at least one hour per week Program Filter – To be considered as a worship facility, buildings must operate for at least one hour per year
Must operate for at least 10 months per year Program Filter – To be considered as a worship facility, buildings must operate for at least 10 months per year
Must have at least one worker Program Filter – To be considered as a worship facility, buildings must have at least one worker
Ceiling height must be at least 2.0 m Program Filter – To be considered as a worship facility, buildings must have a ceiling height of at least 2.0m.
Must have Food Preparation Percentage less than or equal to 10 Analytical Filter – Values determined to be outliers based on analysis of the data. Outliers are typically clearly outside normal operating parameters for a building of this type.
Source EUI must be higher than 0 and lower or equal to 3.0G J/m2 Analytical Filter – Values determined to be outliers based on analysis of the data. Outliers are typically clearly outside normal operating parameters for a building of this type.

Of the filters applied to the reference data, some result in constraints on calculating a score in Portfolio Manager, and others do not. Building Type and Program Filters are used to limit the reference data to include only properties that are intended to receive a score in Portfolio Manager and are therefore related to eligibility requirements. In contrast, Data Limitation Filters account for limitations in the data available during the analysis, but do not apply in Portfolio Manager. Analytical Filters are used to eliminate outlier data points or different subsets of data and may or may not affect eligibility. A full description of the criteria you must meet to obtain a score in Portfolio Manager is available at Benchmarking - Frequently Asked Questions.

Related to the filters and eligibility criteria described above, another consideration is how Portfolio Manager treats properties situated on a campus. The main unit for benchmarking in Portfolio Manager is the property, which may be used to describe either a single building or campus of buildings. The applicability of the ENERGY STAR score depends on the type of property. For worship facilities, the score is based on individual buildings, because the primary function of worship facilities is contained within a single building and because the properties included in the reference data are single buildings. In cases where multiple worship facilities are situated together (e.g., a worship campus), each individual building can receive its own ENERGY STAR score, but the campus cannot earn a score.

Variables analysed

To normalize for differences in business activity, NRCan performed a statistical analysis to understand what aspects of building activity are significant with respect to energy use. The filtered reference data set, described in the previous section, was analysed using a weighted ordinary least squares regression, which evaluated energy use relative to business activity (e.g. number of workers, operating hours per week, floor area, and climate).This linear regression gave an equation used to compute energy use (also called the dependent variable) based on a series of characteristics that describe the business activities (also called independent variables). This section details the variables used in the statistical analysis for Worship Facilities in Canada.

Dependent Variables

The dependent variable is what NRCan tries to predict with the regression equation. For Worship Facility analysis, the dependent variable is energy use, expressed in source energy use intensity (source EUI). This is equal to the total source energy use of the property divided by the gross floor area. The regression analyzes the key drivers of source EUI—those factors that explain the variation in source energy use per square metre in Worship Facilities. The units for source EUI in the Canadian model are annual gigajoules per square metre (GJ/m2).

Independent Variables

The reference survey contains numerous property operation questions that NRCan identified as likely to be important for Worship Facilities. Based on a review of the variables found in the reference data, following the criteria for inclusion in Portfolio Manager,Footnote 1 NRCan initially analysed the variables below in the regression analysis:

  • Gross floor area (m2)
  • Gross floor area for food preparation (m2)
  • Cooling degree days (CDD)
  • Heating degree days (HDD)
  • Percentage of floor space that is cooled
  • Percentage of floor space that is heated
  • Weekly hours of operation
  • Number of workers during the main shift
  • Number of worship seats
  • Number of computers
  • Months in operation in 2019
  • Number of domestic appliances
  • Number of commercial appliances
  • Number of electronic displays
  • Ceiling height (m)
  • Year of construction
  • Indoor gymnasiums and racquet sport courts

NRCan, with the advice of the Environmental Protection Agency (EPA) and its contractor, performed an extensive review on all of these operational characteristics individually and in combination with each other (e.g., Heating Degree Days times Percent Heated). As part of the analysis, some variables were reformatted to reflect the physical relationships of building components. For example, the number of worship religious seats can be evaluated in a density format: seats per 100 m2. Overall, the analysis consists of multiple regression formulations, structured to find the combination of statistically significant operating characteristics that explained the greatest amount of variance in the dependent variable: source EUI.

The final regression equation includes the following variables:

  • Weekly Operating Hours (Hours)
  • Number of Worship Seats per 100 m2 (Seating Density)
  • Ceiling Height
  • Percent Cooled x Cooling Degree Days (Percent Cooled x CDD)
  • Percent Heated x Heating Degree Days (Percent Heated x HDD)

These variables are used together to compute the predicted source EUI for Worship Facilities. The predicted source EUI is the mean EUI for a hypothetical population of buildings that share the same values for each of these characteristics. It is the mean energy for buildings that operate like your building.

Testing

NRCan further analysed the regression equation using actual data entered in Portfolio Manager. In addition to the SCIEU data, this analysis provided another set of buildings to examine the ENERGY STAR scores and distributions to assess the impacts and adjustments. It also confirmed that there are minimal biases when it comes to fundamental operational characteristics, such as percent cooled or percent heated, and that there was no regional bias or bias for the type of energy used for heating.

It is important to reiterate that the final regression equation relies on the nationally representative reference data from SCIEU 2019, and not on data previously stored in Portfolio Manager.

Regression equation results

The final regression is a weighted ordinary least squares regression across the filtered data set of 380 observations. The dependent variable is source EUI. Each independent variable is centred relative to the weighted mean value, presented in Figure 2. The final equation is presented in Figure 3. All variables in the regression equation are considered significant at a 90% confidence level or better, as shown by their respective significance levels.

The regression equation has a coefficient of determination (R2) value of 0.2867 indicating that this equation explains 28.67% of the variance in source EUI for Worship Facilities. Because the final equation is structured with energy per unit area as the dependent variable, the explanatory power of the area is not included in the R2 value, and thus this value appears artificially low. Recomputing the R2 value in units of source energyFootnote 2 demonstrates that the equation explains 66.82% of the variation in total source energy of Worship Facilities. It is a reasonable result for a statistically based energy model.

For detailed information on the ordinary least squares regression approach, see the Technical Reference for the ENERGY STAR Score (PDF, 709 KB).

Figure 2 – Descriptive Statistics for Variables in Final Regression Equation
Variable Minimum Median Maximum Mean
Source energy per square metre (GJ/m2) 0.0111 0.6324 2.964 0.7843
Weekly Operating Hours 1.000 18.00 168.0 22.06
Seating Density 0.4019 29.90 85.87 29.81
Ceiling Height 2.438 7.000 35.00 8.230
Percent Cooled x CDD 0.000 6.500 408.0 79.47
Percent Heated x HDD 1767 4560 8352 4560

Figure 3 – Final Regression Results

Summary
Dependent variable Source energy use intensity (GJ/m2)
Number of observations in analysis 380
R2 value 0.2867
Adjusted R2 value 0.2771
F statistic 30.06
Significance (p-level) <0.0001
Regression Results
Dependent variable Unstandardized Coefficients Standard Error T Value Significance
Constant 0.7843 2.235E-02 35.1 <.0001
Weekly Operating Hours 5.580E-03 1.17E-03 4.78 <.0001
Seating Density 1.651E-02 1.67E-03 9.9 <.0001
Ceiling Height 2.046E-04 5.09E-03 4.02 <.0001
Percent Cooled x CDD 5.196E-04 2.176E-04 2.39 0.0174
Percent Heated x HDD 6.670E-05 2.687E-04 2.48 0.0135
Notes:
  • The regression is a weighted ordinary least squares regression, weighted by the SCIEU variable “FSWGT.”
  • All model variables are centred. The centred variable is equal to the difference between the actual value and the observed mean. The observed mean values are presented in Figure 2.
  • HDDs and CDDs are sourced from Canadian weather stations included in the U.S. National Climatic Data Center system.

Energy star score lookup table

The final regression equation (presented in Figure 3) gives a prediction of source EUI based on a building’s operating characteristics. Some buildings in the SCIEU data sample use more energy than predicted by the regression equation, while others use less. The actual source EUI of each reference data observation is divided by its predicted source EUI to calculate an energy efficiency ratio:

Energy Efficiency Ratio = Actual Source Energy Intensity Predicted Source Energy Intensity

An efficiency ratio lower than one indicates that a building uses less energy than predicted, and consequently is more efficient. A higher efficiency ratio indicates the opposite.

The efficiency ratios are sorted from smallest to largest, and the cumulative percent of the population at each ratio is computed using the individual observation weights from the reference data set. Figure 4 presents a plot of this cumulative distribution. A smooth curve (shown in orange) is fitted to the data using a two-parameter gamma distribution. The fit is performed to minimize the sum of squared differences between each building’s actual percent rank in the group and each building’s percent rank with the gamma solution. The final fit for the gamma curve gave a shape parameter (alpha) of 3.753 and a scale parameter (beta) of 0.2633. The sum of the squared error for this fit is 0.8713.

Figure 4 – Distribution for Worship Facilities

Distribution for Worship Facilities

The final gamma shape and scale parameters are used to calculate the efficiency ratio at each percentile (1 to 100) along the curve. For example, the ratio on the gamma curve at 1% corresponds to a score of 99; only 1% of the population has a ratio this small or smaller. The ratio on the gamma curve at the value of 25% corresponds to the ratio for a score of 75; only 25% of the population has a ratio this small or smaller. Figure 5 shows the complete score lookup table.

Figure 5 – ENERGY STAR Score Lookup Table for Worship Facilities

ENERGY STAR Score Lookup Table for 100 to 51
ENERG STAR Score Cumulative Percentage Energy Efficiency Ratio
> = <
100 0% 0.0000 0.1896
99 1% 0.1896 0.2365
98 2% 0.2365 0.2705
97 3% 0.2705 0.2983
96 4% 0.2983 0.3225
95 5% 0.3225 0.3441
94 6% 0.3441 0.3640
93 7% 0.3640 0.3824
92 8% 0.3824 0.3998
91 9% 0.3998 0.4163
90 10% 0.4163 0.4320
89 11% 0.4320 0.4472
88 12% 0.4472 0.4618
87 13% 0.4618 0.4759
86 14% 0.4759 0.4897
85 15% 0.4897 0.5032
84 16% 0.5032 0.5163
83 17% 0.5163 0.5292
82 18% 0.5292 0.5418
81 19% 0.5418 0.5543
80 20% 0.5543 0.5666
79 21% 0.5666 0.5787
78 22% 0.5787 0.5906
77 23% 0.5906 0.6025
76 24% 0.6025 0.6142
75 25% 0.6142 0.6259
74 26% 0.6259 0.6375
73 27% 0.6375 0.6490
72 28% 0.6490 0.6604
71 29% 0.6604 0.6718
70 30% 0.6718 0.6832
69 31% 0.6832 0.6945
68 32% 0.6945 0.7058
67 33% 0.7058 0.7171
66 34% 0.7171 0.7284
65 35% 0.7284 0.7397
64 36% 0.7397 0.7510
63 37% 0.7510 0.7623
62 38% 0.7623 0.7736
61 39% 0.7736 0.7850
60 40% 0.7850 0.7964
59 41% 0.7964 0.8078
58 42% 0.8078 0.8193
57 43% 0.8193 0.8309
56 44% 0.8309 0.8425
55 45% 0.8425 0.8542
54 46% 0.8542 0.8660
53 47% 0.8660 0.8779
52 48% 0.8779 0.8898
51 49% 0.8898 0.9019
ENERGY STAR Score Lookup Table for 50 to 1
ENERGY STAR Score Cumulative Percentage Energy Efficiency Ratio
>= <
50 50% 0.9019 0.9141
49 51% 0.9141 0.9263
48 52% 0.9263 0.9388
47 53% 0.9388 0.9513
46 54% 0.9513 0.9640
45 55% 0.9640 0.9768
44 56% 0.9768 0.9899
43 57% 0.9899 1.0030
42 58% 1.0030 1.0164
41 59% 1.0164 1.0300
40 60% 1.0300 1.0438
39 61% 1.0438 1.0578
38 62% 1.0578 1.0720
37 63% 1.0720 1.0865
36 64% 1.0865 1.1013
35 65% 1.1013 1.1163
34 66% 1.1163 1.1317
33 67% 1.1317 1.1474
32 68% 1.1474 1.1634
31 69% 1.1634 1.1798
30 70% 1.1798 1.1967
29 71% 1.1967 1.2139
28 72% 1.2139 1.2316
27 73% 1.2316 1.2499
26 74% 1.2499 1.2686
25 75% 1.2686 1.2880
24 76% 1.2880 1.3080
23 77% 1.3080 1.3288
22 78% 1.3288 1.3502
21 79% 1.3502 1.3726
20 80% 1.3726 1.3958
19 81% 1.3958 1.4201
18 82% 1.4201 1.4456
17 83% 1.4456 1.4723
16 84% 1.4723 1.5005
15 85% 1.5005 1.5303
14 86% 1.5303 1.5620
13 87% 1.5620 1.5960
12 88% 1.5960 1.6325
11 89% 1.6325 1.6720
10 90% 1.6720 1.7152
9 91% 1.7152 1.7630
8 92% 1.7630 1.8164
7 93% 1.8164 1.8773
6 94% 1.8773 1.9484
5 95% 1.9484 2.0340
4 96% 2.0340 2.1424
3 97% 2.1424 2.2919
2 98% 2.2919 2.5406
1 99% 2.5406 >2.5406

Example calculation

According to the Technical Reference for the ENERGY STAR Score (PDF, 709 KB), there are five steps to compute a score for Worship Facilities. Below is a specific example:

1 User enters building data into Portfolio Manager

  • 12 months of energy use information for all energy types (annual values, entered in monthly meter entries)
  • Physical building information (size, location, etc.) and use details describing building activity (hours, etc.)
Summary of Energy Data
Energy Data Value
Electricity 31,300 kWh
Natural gas 9,200 m3
Summary of Property Use Details
Property Use Details Value
Gross floor area (m2) 1,115
Weekly Operating Hours 30
Seating Capacity

5 300
Ceiling Height (m) 9
Percent Cooled 100%
Percent Heated 100%
CDD (provided by Portfolio Manager, based on postal code) 13
HDD (provided by Portfolio Manager, based on postal code) 5,832

2 Portfolio Manager computes the actual source EUI

  • Total energy consumption for each fuel is converted from billing units into site energy and source energy.
  • Source energy values are added across all fuel types.
  • Source energy is divided by gross floor area to determine actual source EUI.
Computing Actual Source EUI
Fuel Billing Units Site GJ Multiplier Site GJ Source Multiplier Source GJ
Electricity 31,300 kWh 3.600E-03 112.7 1.830 206.2
Natural gas 9,200 m3 3.843E-02 353.6 1.060 374.8
Total Source Energy (GJ) 581.0
Source EUI (GJ/m2) 0.5210

3 Portfolio Manager computes the predicted source EUI

  • Using the property use details from Step 1, Portfolio Manager computes each building variable value in the regression equation (determining the density as necessary).
  • The centring values are subtracted to compute the centred variable for each operating parameter (e.g., actual building value minus reference centring value).
  • The centred variables are multiplied by the coefficients from the Worship Facility regression equation to obtain a predicted source EUI.
Computing Predicted Source EUI
Variable Actual Building Value Reference Centring Value Building Centred Variable Coefficient Coefficient x Centred Variable
Constant - - - 0.7843 0.7843
Weekly Operating Hours 30 22.06 7.940 5.580E-03 0.04431
Seating Density 26.9 29.81 -2.094 1.651E-02 -0.04795
Ceiling Height (m) 9 8.230 0.7000 2.046E-04 0.01575
Percent Cooled x CDD 5832 79.49 -76.22 5.196E-04 -0.03960
Percent Heated x HDD 3 4,560 1272 6.670E-05 0.08484
Predicted Source EUI (GJ/m2) 0.8417

4 Portfolio Manager computes the energy efficiency ratio

  • The ratio equals the actual source EUI (Step 2) divided by the predicted source EUI (Step 3).
  • Ratio = 0.5210 / 0.8479= 0.6179

5 Portfolio Manager uses the efficiency ratio to assign a score via a lookup table

  • The ratio from Step 4 is used to identify the score from the lookup table.
  • A ratio of 0.6179 is greater than 0.6142 and less than 0.6259.
  • The ENERGY STAR score is 75.
 
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