ENERGY STAR Score for Medical Offices in Canada

ENERGY STAR Portfolio Manager

Technical Reference

Overview

The ENERGY STAR score for Medical Offices applies to all medical offices in Canada. 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 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 to 100 percentile ranking in relation to the national population of properties.

  • Property types. The ENERGY STAR score for medical offices in Canada applies to medical offices used to provide diagnosis and treatment for medical, dental, or psychiatric outpatient care. The score applies to individual medical offices and is not available for a campus of buildings.
  • Reference data. The analysis for medical offices in Canada relies on data from the Survey on Commercial and Institutional Energy Use (SCIEU), which was commissioned by Natural Resources Canada (NRCan) and carried out by Statistics Canada. The SCIEU represents the energy use for the year 2014.
  • Adjustments for weather and business activity. The analysis includes adjustments for:
    • Computer density (the number of computers per 100 m2)
    • Percentage of the building that is cooled
    • Percentage of the building that is heated
    • Weather and climate (using cooling degree and heating degree days, retrieved based on postal code)
    • Weekly operating hours
  • Release date. This is the second release of the ENERGY STAR score for medical offices in Canada. The ENERGY STAR score for medical offices is updated periodically as more recent data becomes available:
    • Most Recent Update: February 2020
    • Original Release: August 2015

This document details the calculation of the 1 – 100 ENERGY STAR score for medical office properties. 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 medical offices 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 in late 2015 and early 2016. The energy data for the survey was from the calendar year 2014. The raw collected data file for this survey is not publicly available, but a report providing summary results is available on Natural Resources Canada’s website at: Survey of Commercial and Institutional Energy Use (SCIEU) - Buildings 2014 – Data Tables.

Four types of filters are applied to analyze 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 set the ENERGY STAR score for medical office model and the rationale that supports the filter. After all filters are applied, the remaining data set has 136 observations. Given the confidentiality of the data in the survey, NRCan has not been able to identify the number of cases after each filter.

Figure 1 – Summary of Filters for the ENERGY STAR Score for Medical Offices
Condition for Including an Observation in the Analysis Rationale
Defined as category 2 in SCIEU – Medical Office The SCIEU survey covered the commercial and institutional sector and included buildings of all types. For this model, only the observations identified as primarily Medical Office are used.
Cannot be a senior care establishment or hospital Building Type Filter – To be considered as a medical office, the building cannot be a senior care or hospital
Building must be more than 50% medical office and less than 50% of any other building type Building Type Filter – To be considered as a medical office, the building must have a minimum medical office space.
Must have electricity consumption data Program Filter – Medical offices 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 not use any “other” fuels for which the consumption is not reported Data Limitation Filter – The survey asked whether fuels other than purchased electricity, on-site generated electricity from renewable sources, natural gas, light fuel oil, diesel, kerosene, propane, district steam, district hot water or district chilled water were consumed in the facility. Either the type of energy was not defined or in the case of wood, the energy was not easily convertible; therefore, the energy provided by these fuels could not be directly compared. In such cases, these observations were removed from the analysis.
Must be built in 2013 or earlier Data Limitation Filter – The survey reported the energy for calendar year 2014. Therefore, if the building was being built in 2014, a full year of energy data would not be available.
Building must operate for a minimum of 30 hours per week Program Filter – Medical offices must operate for at least 30 hours per week to be considered a full-time operating medical office.
The percent of the building that is heated must be greater than 50% Program Filter – Medical offices must be greater than 50% heated to be considered a medical office in Canada.
The percent of the building that is cooled must be greater than 50% Program Filter – Medical offices must be greater than 50% cooled to be considered a medical office in Canada.
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 size 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 square foot of parking structures exceeds the size of the medical office building, then the overall structure is classified as parking, not medical office. This is a standard policy in Portfolio Manager.
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 medical office to meet ENERGY STAR certification requirements.
Must operate at least 10 months per year Analytical Filter – Basic requirement to be considered as full-time operation.
Must have at least one computer Program Filter – Medical offices that do not have any computers are rare or non-existent and may indicate an omission in data.
Must have at least one worker Analytical Filter – Medical office that does not have any workers are rare or non-existent and may indicate an omission in data.
Must have sterilization density less than 6 per 100 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.
Must have computer density less than 10 per 100 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.
Source EUI must be less than 4.0 GJ/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.
Must be at least 92.9 m2 Analytical Filter – The analysis could not model trends for buildings smaller than 92.9 m2 (1,000 ft2).
Must be less than or equal to 35,000 m2 Analytical Filter – Values determined to be outliers based on analysis of the data. In Canada, most single medical offices do not exceed 35,000 m2.

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 medical offices, the score is based on a single building.

Variables Analyzed

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 analyzed 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 medical offices in Canada.

Dependent Variables

The dependent variable is what NRCan tries to predict with the regression equation. For the medical offices 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 medical offices. 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 medical offices. Based on a review of the variables found in the reference data, following the criteria for inclusion in Portfolio Manager,Footnote 1 NRCan initially analyzed the variables below in the regression analysis:

  • Gross floor area (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 computers
  • Months in operation in 2014
  • Number of commercial appliances
  • Number of sterilization units
  • Number of MRI units
  • Number of beds
  • Number of elevators
  • Number of televisions/electronic displays/LCDs
  • Year of construction
  • Presence of onsite laundry

NRCan, with the advice of the Environmental Protection Agency (EPA), 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 workers on the main shift can be evaluated in a density format: workers per 100 m2. The worker density (as opposed to the gross number of workers) is more closely related to the energy use intensity. In addition, using analytical results and residual plots, variables were assessed using different transformations (such as the natural logarithm, abbreviated as Ln). 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:

  • Number of computers per 100 m2 (computer density)
  • The percentage of the building that is cooled times the number of Cooling Degree Days
    (Percent Cooled x CDD)
  • The percentage of the building that is heated times the number of Heating Degree Days
    (Percent Heated x HDD)
  • Natural log of weekly operating hours Ln (Weekly Operating Hours)

These variables are used together to compute the predicted source EUI for medical offices. 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.

Computer Density Analysis

Computer density (computers per 100 square meters) and worker density (workers per 100 square meters) both showed a positive correlation with energy usage. Both variables represent measures of occupant activity in medical offices. The high correlation between computer density and worker density allowed only one of the terms to be included in the model. Computer density showed stronger statistical significance and was therefore included in the model.

Medical Diagnosis or Treatment Machines

The SCIEU 2014 survey collected data on the presence of various types of medical equipment, including x-ray and MRI machines. As MRI machines are potentially high energy consumers, it was important to investigate their impact on energy and energy use intensity. However, the results of the analysis indicated that the number of medical diagnosis and treatment machines (including MRIs) was not a statistically significant predictor of energy use intensity in medical offices.

Testing

NRCan further analyzed 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 2014, 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 136 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 0.3468, indicating that this equation explains 34.68% of the variance in source EUI for medical offices. 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 93.57% of the variation in total source energy of medical offices. It is an excellent 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) 0.3140 1.594 3.909 1.666
Computer Density* 3.300E-02 1.676 9.936 2.382
Percent Cooled x CDD 0 143.4 409.5 163.1
Percent Heated x HDD 1,408 4,966 6,923 4,782
Ln (Weekly Operating Hours) 3.689 3.850 5.124 3.950

Figure 3 – Final Regression Results

Summary
Variable Dependent Source energy use intensity (GJ/m2)
Number of observations in analysis 136
R2 value 0.3468
Adjusted R2 value 0.3269
F statistic 17.39
Significance (p-level) < 0.0001
Regression Results
Variable Dependent Unstandardized Coefficients Standard Error T Value Significance
(p-level)
Constant 1.670 4.744E-02 35.11 <.0001
Computer Density 0.1804 2.443E-02 7.38 <.0001
Percent Cooled x CDD 2.903E-03 5.764E-04 5.04 <.0001
Percent Heated x HDD 1.336E-04 6.09E-05 2.19 0.03
Ln (Weekly Operating Hours) 0.2846 0.1695 1.68 0.0955
Notes:
  • The regression is a weighted ordinary least squares regression, weighted by the SCIEU variable “SWEIGHT.”
  • All model variables are centered. The centered variable is equal to the difference between the actual value and the observed mean. The observed mean values are presented in Figure 2.
  • Heating and cooling degree days 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 in order 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 13.34 and a scale parameter (beta) of 0.07433. The sum of the squared error for this fit is 0.2167.

Figure 4 – Distribution for Medical Office

A graph of a curve Description automatically generated with medium confidence

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 Medical Office

ENERGY STAR Score Lookup Table for 100 to 51
ENERGY STAR Score Cumulative Percent Energy Efficiency Ratio
> = <
100 0% 0.0000 0.4704
99 1% 0.4704 0.5163
98 2% 0.5163 0.5470
97 3% 0.5470 0.5710
96 4% 0.5710 0.5910
95 5% 0.5910 0.6084
94 6% 0.6084 0.6239
93 7% 0.6239 0.6381
92 8% 0.6381 0.6511
91 9% 0.6511 0.6633
90 10% 0.6633 0.6748
89 11% 0.6748 0.6856
88 12% 0.6856 0.6960
87 13% 0.6960 0.7059
86 14% 0.7059 0.7154
85 15% 0.7154 0.7246
84 16% 0.7246 0.7335
83 17% 0.7335 0.7422
82 18% 0.7422 0.7506
81 19% 0.7506 0.7588
80 20% 0.7588 0.7668
79 21% 0.7668 0.7747
78 22% 0.7747 0.7824
77 23% 0.7824 0.7900
76 24% 0.7900 0.7974
75 25% 0.7974 0.8048
74 26% 0.8048 0.8120
73 27% 0.8120 0.8192
72 28% 0.8192 0.8262
71 29% 0.8262 0.8332
70 30% 0.8332 0.8402
69 31% 0.8402 0.8470
68 32% 0.8470 0.8538
67 33% 0.8538 0.8606
66 34% 0.8606 0.8674
65 35% 0.8674 0.8741
64 36% 0.8741 0.8807
63 37% 0.8807 0.8874
62 38% 0.8874 0.8940
61 39% 0.8940 0.9006
60 40% 0.9006 0.9072
59 41% 0.9072 0.9138
58 42% 0.9138 0.9204
57 43% 0.9204 0.9270
56 44% 0.9270 0.9336
55 45% 0.9336 0.9402
54 46% 0.9402 0.9468
53 47% 0.9468 0.9534
52 48% 0.9534 0.9601
51 49% 0.9601 0.9668
ENERGY STAR Score Lookup Table for 50 to 1
ENERGY STAR Score Cumulative Percent Energy Efficiency Ratio
>= <
50 50% 0.9668 0.9735
49 51% 0.9735 0.9803
48 52% 0.9803 0.9871
47 53% 0.9871 0.9939
46 54% 0.9939 1.0008
45 55% 1.0008 1.0077
44 56% 1.0077 1.0147
43 57% 1.0147 1.0218
42 58% 1.0218 1.0289
41 59% 1.0289 1.0362
40 60% 1.0362 1.0434
39 61% 1.0434 1.0508
38 62% 1.0508 1.0583
37 63% 1.0583 1.0659
36 64% 1.0659 1.0736
35 65% 1.0736 1.0814
34 66% 1.0814 1.0893
33 67% 1.0893 1.0974
32 68% 1.0974 1.1056
31 69% 1.1056 1.1139
30 70% 1.1139 1.1225
29 71% 1.1225 1.1312
28 72% 1.1312 1.1401
27 73% 1.1401 1.1493
26 74% 1.1493 1.1586
25 75% 1.1586 1.1683
24 76% 1.1683 1.1781
23 77% 1.1781 1.1883
22 78% 1.1883 1.1989
21 79% 1.1989 1.2098
20 80% 1.2098 1.2211
19 81% 1.2211 1.2328
18 82% 1.2328 1.2450
17 83% 1.2450 1.2578
16 84% 1.2578 1.2712
15 85% 1.2712 1.2853
14 86% 1.2853 1.3003
13 87% 1.3003 1.3161
12 88% 1.3161 1.3331
11 89% 1.3331 1.3514
10 90% 1.3514 1.3712
9 91% 1.3712 1.3930
8 92% 1.3930 1.4172
7 93% 1.4172 1.4445
6 94% 1.4445 1.4761
5 95% 1.4761 1.5138
4 96% 1.5138 1.5609
3 97% 1.5609 1.6251
2 98% 1.6251 1.7296
1 99% 1.7296 >1.7296

Example Calculation

According to the Technical Reference for the ENERGY STAR Score (PDF, 709 KB), there are five steps to compute a score for medical offices. 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 1,200,000 kWh
Natural gas 120,000 m3
Summary of Property Use Details
Property Use Details Value
Gross floor area (m2) 10,000
Weekly Operating Hours 70
Number of Computers 311
Percent That Can Be Heated 100%
Percent That Can Be Cooled 100%
HDD (provided by Portfolio Manager, based on postal code) 2,035
CDD (provided by Portfolio Manager, based on postal code) 165

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 1,200,000 kWh 3.600E-03 4,320 1.83 7,906
Natural gas 120,000 m3 3.843E-02 4,612 1.06 4,888
Total Source Energy (GJ) 12,794
Source EUI (GJ/m2) 1.279

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 centering values are subtracted to compute the centered variable for each operating parameter.
  • The centered variables are multiplied by the coefficients from the Medical Offices regression equation to obtain a predicted source EUI.
Computing Predicted Source EUI
Variable Actual Building Value Reference Centering Value Building Centered Variable Coefficient Coefficient x Centered Variable
Constant - - - 1.666 1.666
Computer Density* 3.110 2.382 0.728 0.1804 0.13133
Percent Cooled x CDD 165.0 163.1 1.900 2.900E-03 5.520E-03
Percent Heated x HDD 2035 4782 -2747 1.336E-04 -0.3670
Ln (Weekly Operating Hours) 4.2485 3.950 0.2985 0.2846 8.495E-02
Predicted Source EUI (GJ/m2) 1.521
*Computers per 100 m2

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 = 1.279 / 1.521 = 0.8409

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 0.8409 is greater than 0.8402 and less than 0.8470.
  • The ENERGY STAR score is 69.
 
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