Performance Based Rating and Energy Performance ...info.tuwien.ac.at/bausim/presentations/23.09.2010_HS_14/15_5_105... · •Energy Conservation Building Code (ECBC) •Leadership

Performance Based Rating and Energy Performance Benchmarking for Commercial Buildings in India BauSIM 2010, Vienna University of TechnologySeptember 22-24, 2010, Vienna, Austria.

Performance Based Rating and Energy Performance Benchmarking for Commercial Buildings in India

Satish Kumar1, Saket Sarraf2, Sanjay Seth3, Sameer Pandita3, Archana Walia4, Madhav Kamath1 and Aalok Deshmukh1

1International Resources Group, New Delhi, India2ps Collective, Ahmedabad, India

3Bureau of Energy Efficiency, New Delhi, India4United States Agency for International Development, New Delhi, India

Presented by:

Aalok A. Deshmukh

Manager: Energy Efficiency Solutions

USAID ECO-III Project

International Resources Group


Outline

»Background

»Motivation

»Macro Level Analysis – Office, Hospital, Hotels

»Regression Analysis

»Benchmarking

»Rating

»Limitations

»Future Steps


Why Buildings?

Agriculture18%

Industry46%

Others7%

Buildings29%

Breakdown of Electricity Consumption in India

Source: CEA 2009

Building sector accounts for 29% of total electricity consumption in India


Commercial Electricity Consumption

Residential, 21%

Commercial, 8%

Industry, 46%

Public Lighting, 1%

Railway, 2% Agriculture, 18% Public Water Works & Sewage

Pumping, 2%

Miscellaneous, 2%

SECTOR- WISE ELECTRICITY CONSUMPTION IN INDIA (2007- 2008)

Source: CEA 2009

Commercial sector accounts for 8% of total electricity consumption in India


Growth of Electricity Consumption in Commercial Sector in India

2820131381

3596540220

46685

11.314.6

11.816.1

0

10000

20000

30000

40000

50000

2003-04 2004-05 2005-06 2006-07 2007-08

GW

h

Growth in % over the previous year

SOURCE: Central Electricity Authority (2009).

Commercial building sector electricity consumption growing at 12-14% annually on average


Electricity Scenario in India

Source: Central Electricity Authority General Review 2006 & 2009 and Planning Commission’s Integrated Energy Policy Report 2006

Linear Projection


Commercial Floor Space Projection for India

65

9

69

2

72

8

76

5

80

5

84

7

89

2

94

0

99

0

1,0

44

1,1

02

1,1

63

1,2

28

1,2

98

1,3

72

1,4

50

1,5

34

1,6

24

1,7

20

1,8

22

1,9

32

33 35 37 40 42 4548

5154

5761

6569

7479

8490

96102

109

0

500

1000

1500

2000

2500

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

Mill

ion

sq

. m.

Total Commercial Floor Space (Estimated) (M. sq. m.) Floor Space Added Annually (Estimated) (M. sq. m.)

Source: USAID ECO- III Project * Assuming 5-6% Annual Growth


30%

70%

Growth in the Indian Building Sector

Year: 2010

Yet to be built

659 million

m2

Year: 2030* Assuming 5-6% Annual Growth

Current34%

Yet to be Built66%

1,900

million m2

Commercial Buildings Growth Forecast» Currently, ~ 659 million m2 (USAID ECO-III Internal Estimate Using MOSPI, CEA and

Benchmarked Energy Use data)

» In 2030,~ 1,900 million m2 (estimated) *

• 66% building stock is yet to be constructed

Source: USAID ECO- III Project


Green Building / EE Instruments in India

»Green building / EE instruments in India:

• Energy Conservation Building Code (ECBC)

• Leadership in Energy and Environmental Design (LEED India)

• Green Rating for Integrated Habitat Assessment (GRIHA)

»All based on design intent rather than actual, verified performance

»Not designed to rate energy performance through a systematic evaluation and award scheme

»Do not provide defendable energy consumption targets for new buildings, based on contextual data


Challenges

»Little or no reliable data available in public domain – on actual energy consumption in different types of commercial buildings

»No systematic data collection and analysis of building energy data

»Limited scope to compare individual building performance against peers

»Limited understanding about what are the significant parameters influencing the energy consumption in a building

»Limited understanding on the role of energy efficiency professionals, statisticians, public and private sector organizations (data providers)


What is Energy Benchmarking ?

»Energy Benchmarking is a relatively simple, low-cost method for evaluating a buildings energy performance

• Initial cost/effort of designing the program and collecting and analyzing data may seem high but it can pay big time in the long run

»Benchmarks are commonly used in other industries

• Autos – km/liter

• Industrial Output – kWh/unit produced

»Building Level Benchmarks: kWh/m2/Year, kWh/person/year, etc.

»System Level Benchmarks: Watts/m2, etc.


Benefits of Benchmarking

»Designers, Owners and Users• Designers/ESCOs – Building performance targets for new and existing

buildings

• Owners/Users – Measure the performance of their buildings

• Building portfolio managers can compare the performance of individual facilities to others

»Building Developers and operators• Helps to assess the potential savings

• Use of appropriate products and technologies

»Policy Makers• Reward/Incentivize exemplary building performance and penalize

/discourage poor building performance

• Starting Point for Energy Audits and Building Retrofits


Benefits of Benchmarking – Indian Context

»Introduce Sanity in Building Energy Audit Process

• No need to start with “Investment Grade Audits”

• Help in developing a graduated response to building performance issues

»Provide a Framework for Meeting ECBC Related Stipulations in the EC Act

• Help in quantifying energy savings from ECBC

»Can be Used to Provide Performance Briefs to Design Teams

• System-Level Benchmarking Needed

»Can be Used as the Basis for Existing Building Rating Programs


Use of Benchmark Numbers

» Building Level Benchmarks – First Step, Less Costly

• Energy consumption per employee in an office

• Energy consumption per bed in a hospital

• Energy consumption per room in a hotel

» System Level Benchmarks – Requires Metering Infrastructure, More Costly and Data-Intensive

• Lighting System: 5 Watts/m2

• Equipment Power: 10 Watts/m2

• HVAC System: 50 m2/Ton of Refrigeration; 25 Watts/m2

• Chilled and Condenser water pumps: 10 Watts/GPM

• Air Handling Unit: 0.75 Watts/CFM


Milestones – Benchmarking Exercise

Formation of Technical Steering Committee

Design of the Standardized Form for Data Collection

First Round of Data Collection from ECO-III Project

Launch of Star Rating for Office Buildings

Data Collection for BPO, Hotel, Hospitals by ICMQ

Launch of BPO Building Star Rating Scheme

November 2008

December 2008

Jan - March 2009

March 2009

Apr- Sept 2009

Jan 2010

Development of Multivariate Regression Model and Recommendations for Improving Star Rating Program

Feb -June 2010

System Level Data Collection & Benchmarking Ongoing


Standardized Form for Data Collection


Benchmarking: Macro Analysis – Building Population

N=760

N=861


Benchmarking – Preliminary Analysis

Averages for different commercial buildings (Source: Building Energy Benchmarking study undertaken by the USAID ECO-III Project)

Number of

BuildingsBuilding Type

Floor Area

(m2)

Annual Energy

Consumption

(kWh)

Benchmarking Indices

OFFICE BUILDINGS kWh/m2/year kWh/m2/hour

145 One shift Buildings 16,716 20,92,364 149 0.068

55 Three shifts Buildings 31,226 88,82,824 349 0.042

88 Public Sector Buildings 15,799 18,38,331 115 0.045

224 Private Sector Buildings 28,335 44,98,942 258 0.064

10 Green Buildings 8,382 15,89,508 141 -

HOSPITALS kWh/m2/year kWh/bed/year

128 Multi-specialty Hospitals 8,721 24,53,060 378 13,890

22 Government Hospitals 19,859 13,65,066 88 2,009

HOTELS kWh/m2/year kWh/room/year

89Luxury Hotels (4 and 5

Star)19,136 48,65,711 279 24,110

SHOPPING MALLS kWh/m2/year kWh/m2/hour

101 Shopping Malls 10,516 23,40,939 252 0.05642


Types of Benchmarking

»Point Based Rating»Raw Data Visualization Method»Regression Based Statistical Method»Simulation & Model Based Approaches»Hierarchical End Use Metrics»Other methods include energy audits, domain expert

knowledge, etc.


Why Regression Based Rating and Why Not Simple EPI Based Rating?

»Simple EPI based rating does not take into account

• Physical characteristics

• Location characteristics

• Operating characteristics

Regression based methodology:

Energy consumption of a benchmarked building

= function (Building use, physical, operational and location characteristics).


Performance Based Rating

1. Estimate energy consumed by benchmarked buildinglog(kwh) = c0. + c1.climate + c2.log(%ac) + c3.log(bua) + c4.log(hrs) + c5.log(emp)

2. Calculate performance relative to the benchmarked building (BPI)BPI=Actual Energy Consumed / Energy Consumed by the benchmarked building

3. Compare candidate buildings’ BPI to other buildings and assign score to the performance differential

0 1 2 3 4

0.0

0.2

0.4

0.6

0.8

1.0

Building Performance Index (BPI)

BPI (Actual Annual KWH consumed/ Predicted KWH)

Cu

mu

lative

Pe

rce

nt

Data

Fitted Curve

10 12 14 16 18

10

12

14

16

Actual Electricity Consumed(Log)

Estim

ate

d E

lectr

icity C

on

su

me

d(L

og

)

lkwh

-3 -2 -1 0

0.17 0.87

8.0 8.5 9.0

0.093

10

12

14

16

18

0.83

-3-2

-10

lpac2

0.082 0.29 0.081

lbua

0.11

46

810

0.80

8.0

8.5

9.0

lhrs

0.19

10 12 14 16 18 4 6 8 10 12 3 5 7 9

35

79

lemp

Offices


Benchmarking: Regression Concept

»Benchmarked building: A building that is similar to the candidate building in terms of its use, location, operation characteristics based on our current knowledge about the national building stock

»Estimated energy use by the benchmarked building =

function (building type, physical, operational and location characteristics).

»Issues: Functional form, Variable transformation, Estimation method, …


Benchmarking: Regression Analysis

Note: natural log of all

continuous variables is used in

the graph

lkwh

-3 -2 -1 0

0.17 0.87

8.0 8.5 9.0

0.093

10

12

14

16

18

0.83

-3-2

-10

lpac2

0.082 0.29 0.081

lbua

0.11

46

810

0.80

8.0

8.5

9.0

lhrs

0.19

10 12 14 16 18 4 6 8 10 12 3 5 7 93

57

9

lemp

Offices

Kw

H

%ac

.BuA

Hrs.

. Em

p

KwH Energy Use

%ac % Conditioned Space

BuA Built up Area

Hrs Hours of Operation

Emp # Workers


log(kwh) = c0. + c1.climate + c2.log(%ac) + c3.log(bua) + c4.log(hrs) + c5.log(emp)

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 3.25353 0.93575 3.477 0.000629 ***

Climate:Hot & Dry -0.33754 0.20305 -1.662 0.098100 .

Climate:Temperate 0.04821 0.14611 0.330 0.741815

Climate:Warm & Humid 0.13827 0.11501 1.202 0.230785

log(%ac) 0.44334 0.07272 6.096 5.98e-09 ***

log(bua) 0.77891 0.06108 12.753 < 2e-16 ***

log(hrs) 0.25989 0.10383 2.503 0.013160 *

log(emp) 0.29468 0.07165 4.113 5.83e-05 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.6869 on 189 degrees of freedom

Multiple R-squared: 0.8516, Adjusted R-squared: 0.8461

F-statistic: 155 on 7 and 189 DF, p-value: < 2.2e-16

Benchmarking: Regression Estimation

Base Climate: Composite


Benchmarking

User Input Calculated values

Select Building Type Office EPI (kWh/m2) 70

Built up area (m2) 2,000

Annual Electricity Purchased (kWh) 100,000

Annual Electricity Generated (kWh) 40,000

Select Climate zone Hot and Dry

Hours per day 12

Total Annual Electricity Consumed 140,000Days per Week 5

Percent conditioned space 0.75 Average Energy Consumed by a Similar Benchmarked Building

214,718

Total number of employees 150


10 12 14 16 18

10

12

14

16


Estim

ate

d E

lectr

icity C

on

su

me

d(L

og

)


Building Performance Index (BPI) =

Energy consumed by the

benchmarked building

Actual Energy Consumed

BPI < 1

Efficient Bldgs.

BPI > 1

Inefficient Bldgs.



0 1 2 3 4 5 6

0.0

0.2

0.4

0.6

0.8

1.0

Cumulative Distribution Function

x

Fn

(x)

Estimated EER

Normal

Weibull

Gamma

Log normal

Cauchy

0 1 2 3 4

0.0

0.2

0.4

0.6

0.8

1.0



Cu

mu

lative

Pe

rce

nt

Data

Fitted CurvePerf

orm

ance P

erc

entile


BPI Percentile BPI Percentile BPI Percentile BPI Percentile BPI Percentile

0.14 1 0.6 21 0.92 41 1.29 61 1.83 81

0.19 2 0.62 22 0.94 42 1.31 62 1.86 82

0.23 3 0.63 23 0.95 43 1.33 63 1.9 83

0.26 4 0.65 24 0.97 44 1.35 64 1.95 84

0.29 5 0.67 25 0.99 45 1.37 65 1.99 85

0.31 6 0.68 26 1.01 46 1.4 66 2.04 86

0.34 7 0.7 27 1.02 47 1.42 67 2.09 87

0.36 8 0.71 28 1.04 48 1.44 68 2.14 88

0.38 9 0.73 29 1.06 49 1.47 69 2.2 89

0.4 10 0.74 30 1.08 50 1.49 70 2.26 90

0.42 11 0.76 31 1.09 51 1.52 71 2.33 91

0.44 12 0.78 32 1.11 52 1.54 72 2.41 92

0.46 13 0.79 33 1.13 53 1.57 73 2.49 93

0.48 14 0.81 34 1.15 54 1.6 74 2.59 94

0.5 15 0.82 35 1.17 55 1.63 75 2.71 95

0.52 16 0.84 36 1.19 56 1.66 76 2.85 96

0.53 17 0.86 37 1.21 57 1.69 77 3.02 97

0.55 18 0.87 38 1.23 58 1.72 78 3.27 98

0.57 19 0.89 39 1.25 59 1.76 79 3.68 99

0.58 20 0.91 40 1.27 60 1.79 80 Inf 100




1. Estimate energy consumed by benchmarked buildinglog(kwh) = c0. + c1.climate + c2.log(%ac) + c3.log(bua) + c4.log(hrs) + c5.log(emp)

2. Calculate performance relative to the benchmarked building (BPI)BPI=Actual Energy Consumed / Energy Consumed by the benchmarked building

3. Compare candidate buildings’ BPI to other buildings and assign score to the performance differential

0 1 2 3 4

0.0

0.2

0.4

0.6

0.8

1.0



Cu

mu

lative

Pe

rce

nt

Data

Fitted Curve

10 12 14 16 18

10

12

14

16


Estim

ate

d E

lectr

icity C

on

su

me

d(L

og

)

lkwh

-3 -2 -1 0

0.17 0.87

8.0 8.5 9.0

0.093

10

12

14

16

18

0.83

-3-2

-10

lpac2

0.082 0.29 0.081

lbua

0.11

46

810

0.80

8.0

8.5

9.0

lhrs

0.19

10 12 14 16 18 4 6 8 10 12 3 5 7 9

35

79

lemp

Offices


Factors influencing the energy consumption

Building type Independent variables

Built up area

Hours of operation

Percent conditioned space

Number of employees

Climate

Built up area

Number of beds

Small / Large category

Climate

Built up area

Number of rooms

Climate



EPI Percentile

~ 82 25%

132 50%

200 ~ 75%

Building Type Office


Annual Elec. Purchased (kWh) 100,000

Annual Elec. Generated (kWh) 40,000


Hours per day 12

Days per Week 5

Percent conditioned space 0.75




EPI Percentile

~ 72 25%

116 50%

175 ~ 75%Dynamic!

Building Type Office


Annual Elec. Purchased (kWh) 100,000

Annual Elec. Generated (kWh) 40,000


Hours per day 12

Days per Week 5

Percent conditioned space 0.75



Current Limitations

»Data related issues

• Additional questions in the survey

• Information that is difficult to collect

• Data credibility

»Estimation related issues

• Multi collinearity (e.g. built up area and number of users)

»Impact of climate

»Geographical scope

»Program filters


Next Steps

»Collecting and analyzing system level energy consumption data

»Development of system level benchmarks

»Using benchmarked data

• To design higher performing new buildings

• To introduce operational efficiency in existing buildings

»Institutionalize data collection and analysis process

»Refinement of rating programs for existing buildings

• Keep it simple but make it more rigorous

• Reduce the possibility of gaming the system


Next Steps

»From score to labels

»Web based administration

»Periodic survey• Balanced coverage: Climate, Geography, Building types,

Sample size, Program filters

• Additional data: Year of construction , Utility bills averaged over at least 2 years, Occupancy by shifts, Envelop characteristics, System / Equipment load, etc. while ensuring data reliability and credibility!


Future Direction

»Continuous monitoring and performance evaluation

»Move from site to source energy

»System level benchmarking


Concluding Remarks

»Transparent, Rigorous, Versatile, and Extensible System

»Initial stage of development

»Continuous technical and administrative support required for administration


»USAID India

»Bureau of Energy Efficiency (BEE)

»ICMQ and LBNL

»Analysis Team: Saket Sarraf, Madhav Kamath, AalokDeshmukh, Satish Kumar, Sanjay Seth, Sameer Pandita

Acknowledgements


Thank You

Contact Information

Aalok DeshmukhManager: Energy Efficiency SolutionsUSAID ECO-III ProjectPhone: +91 11 2685 3110E-mail: adeshmukh (at) irgssa (dot) comURL: www.eco3.org

Documents

Performance Based Rating and Energy Performance ...info.tuwien.ac.at/bausim/presentations/23.09.2010_HS_14/15_5_105... · •Energy Conservation Building Code (ECBC) •Leadership