ENERGY STAR Score for Hotels in Canada

ENERGY STAR Portfolio Manager

Technical Reference

Overview

The ENERGY STAR score for Hotels applies to hotels, as well as hostels, lodges, motels and resorts.

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 hotels in Canada applies to hotels and other nightly lodgings. The score applies to an entire hotel, whether it is a single building or a campus of buildings.
  • Reference data. The analysis for hotels in Canada is based 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, and represents the energy use for the year 2014
  • Adjustments for weather and business activity. The analysis includes adjustments for:
    • Number of rooms
    • Number of workers on the main shift
    • Gross floor area used for food preparation
    • Percent of the building that is heated
    • Percent of the building that is cooled
    • Weather and Climate (using heating degree days, retrieved based on postal code)
  • Release Date. This is the original release of the ENERGY STAR score for hotels in Canada.

This document details the development of the 1 – 100 ENERGY STAR score for hotel properties. For more information on the methodology used to develop 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 hotels 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 population 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 hotels model and the rationale that supports the filter. After all filters are applied, the remaining data set has 119 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 Hotels
Condition for Including an Observation in the Analysis Rationale
Defined as category 6 in SCIEU – Hotel, Motel and/or Lodge The SCIEU survey covered the commercial and institutional sector and included buildings of all types. For this model, only the observations identified as primarily hotels are used.
Buildings must be more than 50% Hotel and less than 50% of another building type Building Type Filter – To be considered part of the hotel peer population, the building must have a minimum hotel space.
Must have electricity consumption data Program Filter – Hotels 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 – Hotels must operate for at least 30 hours per week to be considered a full-time operating hotel.
The percent of the building that is heated must be greater than 50% Program Filter – Hotels must be at least 50% heated to be considered a hotel 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 these 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 hotel building, then the overall structure is classified as parking, not hotel. 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 hotels to meet ENERGY STAR certification requirements.
Must operate at least 10 months per year Program Filter – Basic requirement to be considered as full-time operation.
Must have a Point-of-sale device (computer or cash register) unless it is located on a campus Program Filter – Requirement to be considered a hotel.
The sum of the area for commercial food preparation, conference, gym, walk-in refrigeration, and pool area must be less than 50% of the total area of the hotel Program Filter – Requirement to be considered a hotel.
The hotel must have at least one worker Program Filter – Requirement to be considered a hotel.
The hotel must be at least 464.5 m2 (5,000 ft2) Analytical Filter – The analysis could not model behaviours for buildings smaller than 464.5 m2 (5,000 ft2).
The hotel must have at least 7 rooms Analytical Filter – Values determined to be statistical outliers.
The hotel must have a room density of at least 0.1 rooms/100m2 Analytical Filter – Values determined to be statistical outliers.
The hotel must have a worker to room ratio of at least 0.08 workers per room Analytical Filter – Values determined to be statistical outliers.
The hotel must have a worker density of less than or equal to 1.6 workers per 100m2 Analytical Filter – Values determined to be statistical outliers.
The hotel must have a source EUI of 5 GJ/m2 or less. Analytical Filter – Values determined to be statistical outliers.

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. In some cases, a subset of the data has a different behaviour from the rest of the properties (e.g. hotels smaller than 464.5 m2 do not behave the same way as larger buildings), in which case an Analytical Filter is used to determine eligibility in Portfolio Manager. In other cases, Analytical Filters exclude a small number of outliers with extreme values that skew the analysis, but do not affect eligibility requirements. 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 hotels, the score applies to the entire hotel, whether it is a single building or a campus of buildings. Hotels may have multiple buildings that are all integral to the primary activity. One building may be the reception and administration, another, the actual accommodations. In these cases, the campus can get an ENERGY STAR score as long as the energy for all the buildings is metred and reported. For cases where all the activities are contained within one building, that hotel can get an ENERGY STAR score.

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 gives 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 hotels in Canada.

Dependent Variables

The dependent variable is what NRCan tries to predict with the regression equation. For the hotel 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 hotels. 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 hotels. Based on a review of the variables found in the reference data, and following the criteria for inclusion in Portfolio Manager,Footnote 1 NRCan initially analyzed the following variables 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
  • Length of all open/closed refrigeration/freezer units
  • Area of walk-in refrigeration
  • Number of vending machines
  • Months in operation in 2014
  • Number of commercial appliances
  • Number of domestic appliances
  • Area of commercial food preparation
  • Number of computers
  • Number of cash registers
  • Number of televisions/electronic displays/LCDs
  • Year of construction
  • Area of conference space
  • Number of rooms
  • Presence of onsite laundry
  • Percentage of floor space that is pool
  • Percentage of floor space that is gym

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 rooms can be evaluated in a density format: rooms per 100m2. The room density (as opposed to the gross number of rooms) 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:

  • Percent of floor space that is used for food preparation (Food Preparation Percent)
  • 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)
  • Number of rooms per 100m2, with a floor at 2 (Room Density)
  • Number of workers divided by the number of rooms (Worker to Room Ratio)

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

Room Density

Room density is an important variable used to account for the various room and space configurations used in different hotel types. The positive relationship between energy use and room density was primarily observed in hotels with a room density greater than 2 rooms per 100 m2. Therefore, a floor was applied to room density at 2. This means that a hotel with a room density less than 2 will be scored as if it had a room density of 2.

Worker to Room Ratio

The ratio of workers to rooms is a strong driver of hotel energy use and may account for hotel service level. Hotels with a higher number of workers per room showed increased energy use. A comparison between Worker to Room Ratio and Worker Density was conducted, and Worker to Room Ratio resulted in a stronger model with more equitable scoring across different hotel types. Therefore, the number of workers divided by the number of rooms was included in the model.

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 Room Density 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 is based on the nationally representative reference data from SCIEU 2014, not on data previously entered into Portfolio Manager.

Regression equation results

The final regression is a weighted ordinary least squares regression across the filtered data set of 119 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 0.4681, indicating that this equation explains 46.81% of the variance in source EUI for office buildings. 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 actually explains 84.94% of the variation in total source energy of hotels. It is an excellent result for a statistically based energy model.

For detailed information on the ordinary least squares regression approach, go to 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) 0.375 1.694 4.98 1.973
Room Density* 2.000 2.000 5.517 2.504
Worker to Room Ratio 8.100E-02 0.2500 0.9600 0.316
Percent Cooled x CDD 0 84.67 337.4 82.29
Percent Heated x HDD 1423 4685 6897 4544
Food Preparation Percent 0 0 6.500E-02 1.1E-02
*Room Density is floored at 2

Figure 3 – Final Regression Results

Summary
Dependent variable Source energy use intensity (GJ/m2)
Number of observations in analysis 119
R2 value 0.4681
Adjusted R2 value 0.4446
F statistic 19.89
Significance (p-level) < 0.0001
Regression Results
Dependent variable Unstandardized Coefficients Standard Error T Value Significance
(p-level)
Constant 1.97 6.55E-02 30.12 <.0001
Room Density* 6.423E-01 8.4E-02 7.65 <.0001
Worker to Room Ratio 9.799E-01 3.711E-01 2.64 0.0095
Percent Cooled x CDD 3.666E-03 1.142E-03 3.21 0.0017
Percent Heated x HDD 1.025E-04 5.881E-05 1.74 0.0841
Food Preparation Percent 13.74 4.567 3.01 0.0032
Notes:
  • *Room Density is floored at 2
  • 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 population and each building’s percent rank with the gamma solution. The final fit for the gamma curve gives a shape parameter (alpha) of 8.466 and a scale parameter (beta) of 0.1169. The sum of the squared error for this fit is 0.1720.

Figure 4 – Distribution for Hotels

Figure 4 - A graph of a curve described above.

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 registers such a small or even smaller ratio. 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 registers such a small or even smaller ratio. Figure 5 shows the complete score lookup table.

Figure 5 – ENERGY STAR Score Lookup Table for Hotel

ENERGY STAR Score 100 to 51 for Hotel
ENERGY STAR Score Cumulative Percent Energy Efficiency Ratio
> = <
100 0% 0.0000 0.3718
99 1% 0.3718 0.4211
98 2% 0.4211 0.4547
97 3% 0.4547 0.4812
96 4% 0.4812 0.5036
95 5% 0.5036 0.5232
94 6% 0.5232 0.5408
93 7% 0.5408 0.5569
92 8% 0.5569 0.5718
91 9% 0.5718 0.5859
90 10% 0.5859 0.5991
89 11% 0.5991 0.6117
88 12% 0.6117 0.6237
87 13% 0.6237 0.6353
86 14% 0.6353 0.6464
85 15% 0.6464 0.6572
84 16% 0.6572 0.6676
83 17% 0.6676 0.6778
82 18% 0.6778 0.6878
81 19% 0.6878 0.6975
80 20% 0.6975 0.7070
79 21% 0.7070 0.7164
78 22% 0.7164 0.7255
77 23% 0.7255 0.7346
76 24% 0.7346 0.7435
75 25% 0.7435 0.7523
74 26% 0.7523 0.7610
73 27% 0.7610 0.7696
72 28% 0.7696 0.7781
71 29% 0.7781 0.7865
70 30% 0.7865 0.7949
69 31% 0.7949 0.8032
68 32% 0.8032 0.8115
67 33% 0.8115 0.8197
66 34% 0.8197 0.8279
65 35% 0.8279 0.8360
64 36% 0.8360 0.8442
63 37% 0.8442 0.8523
62 38% 0.8523 0.8604
61 39% 0.8604 0.8685
60 40% 0.8685 0.8766
59 41% 0.8766 0.8847
58 42% 0.8847 0.8928
57 43% 0.8928 0.9009
56 44% 0.9009 0.9090
55 45% 0.9090 0.9172
54 46% 0.9172 0.9253
53 47% 0.9253 0.9336
52 48% 0.9336 0.9418
51 49% 0.9418 0.9501
ENERGY STAR Score 50 to 1 for Hotel
ENERGY STAR Score Cumulative Percent Energy Efficiency Ratio
>= <
50 50% 0.9501 0.9585
49 51% 0.9585 0.9669
48 52% 0.9669 0.9753
47 53% 0.9753 0.9838
46 54% 0.9838 0.9924
45 55% 0.9924 1.0011
44 56% 1.0011 1.0099
43 57% 1.0099 1.0187
42 58% 1.0187 1.0277
41 59% 1.0277 1.0367
40 60% 1.0367 1.0459
39 61% 1.0459 1.0552
38 62% 1.0552 1.0646
37 63% 1.0646 1.0741
36 64% 1.0741 1.0838
35 65% 1.0838 1.0937
34 66% 1.0937 1.1037
33 67% 1.1037 1.1140
32 68% 1.1140 1.1244
31 69% 1.1244 1.1350
30 70% 1.1350 1.1459
29 71% 1.1459 1.1570
28 72% 1.1570 1.1684
27 73% 1.1684 1.1800
26 74% 1.1800 1.1920
25 75% 1.1920 1.2043
24 76% 1.2043 1.2170
23 77% 1.2170 1.2301
22 78% 1.2301 1.2436
21 79% 1.2436 1.2576
20 80% 1.2576 1.2722
19 81% 1.2722 1.2873
18 82% 1.2873 1.3031
17 83% 1.3031 1.3197
16 84% 1.3197 1.3371
15 85% 1.3371 1.3554
14 86% 1.3554 1.3748
13 87% 1.3748 1.3955
12 88% 1.3955 1.4176
11 89% 1.4176 1.4415
10 90% 1.4415 1.4675
9 91% 1.4675 1.4961
8 92% 1.4961 1.5280
7 93% 1.5280 1.5640
6 94% 1.5640 1.6058
5 95% 1.6058 1.6559
4 96% 1.6559 1.7187
3 97% 1.7187 1.8046
2 98% 1.8046 1.9453
1 99% 1.9453 >1.9453

Example Calculation

According to the Technical Reference for the ENERGY STAR Score (PDF, 709 KB), there are five steps to compute a score for hotels. The following is an 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 800,000 kWh
Natural gas 310,000 m3
Summary of Property Use Details
Property Use Details Value
Gross floor area (m2) 10,000
Number of Rooms 200
Number of Workers on Main Shift 50
Gross Floor Area Used for Food Preparation (m2) 100
Percent That Can Be Heated 100%
Percent That Can Be Cooled 100%
HDD (provided by Portfolio Manager, based on postal code) 3,700
CDD (provided by Portfolio Manager, based on postal code) 300

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 800,000 kWh 3.600E-03 2,879 1.83 5,266
Natural gas 310,000 m3 3.843E-02 11,913 1.06 12,628
Total Source Energy (GJ) 17,894
Source EUI (GJ/m2) 1.789

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 Hotel 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 - - - 1.970 1.970
Room Density* 2.000 2.504 -0.504 0.6423 -0.3237
Worker to Room Ratio 0.2500 0.316 -6.6E-02 0.9799 -6.467E-02
Percent Cooled x CDD 300.0 82.29 217.71 3.666E-03 0.7981
Percent Heated x HDD 3700 4544 -844.0 1.025E-04 -8.651E-02
Food Preparation Percent 1.000E-02 1.100E-02 -1.000E-03 13.74 -1.374E-02
Predicted Source EUI (GJ/m2) 2.279
*Room density floored at 2

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 = 1.789 / 2.279 = 0.7850

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 0.7850 is greater than 0.7781 and less than 0.7865.
  • The ENERGY STAR score is 71.
 
Date modified: