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Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006...

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Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak Go Sustainable Energy, LLC Dayton, OH
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Page 1: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Applying State-of-the-Art Analysis to Utility Billing Data

Ohio Board of Regents

April 25, 2006 Bowling Green, OH

April 26, 2006 Columbus, OH

John Seryak

Go Sustainable Energy, LLC

Dayton, OH

Page 2: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

My Experience

• Bachelor and Master’s in Mechanical Engineering from UD

• Several years of experience working with Ohio/New England/New

York state and utility energy efficiency programs

– Energy assessments

– Demand reduction (permanent, load-shedding, peak generation)

– Technical assistance

– Program consulting (measure life, lean manufacturing & energy)

– Commissioning teams and retro-commissioning studies

– Industrial ecology

• Approx. 100 facilities evaluated

Page 3: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

• Core services

– Utility & energy data statistical regression analysis

– Energy assessments

– Technical assistance (focused projects)

– Instructional

• Also,

– Incentive packages - GHG emissions credits; federal tax incentives; state loans & grants; utility rebates

– Next generation - Industrial ecology, biomimicry, sustainable product design

– Long-term demand-side management

Page 4: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

HB 251 – For State Institutions of Higher Education

• Goal to reduce building energy consumption by 20% by 2014

– 2004 is benchmark year

• Creation of energy efficiency standards for new buildings

>$100,000 in cost

• Each board must have a 15-year plan for phasing in energy

efficiency projects

Page 5: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

HB 251 – It makes sense!

• Goal to reduce building energy consumption by 20% by 2014

– 2.7% reduction per year from now until then

– No big deal…

• Refrigerators 5.5% improvement per year for past 30 years – standards

• Economy 2.25% improvement with high prices - incentives

Page 6: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

HB 251 – For State Institutions of Higher Education

• Creation of energy efficiency standards for new buildings

>$100,000 in cost

• Each board must have a 15-year plan for phasing in energy

efficiency projects

– $250 million project….$0.00 spent on evaluating energy operating cost

Page 7: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Our Analysis: How Does It Help These Efforts?

• Benchmark energy use

– Measure energy savings, track improvements

– Normalize weather, occupancy, enrollment, other

• Identify energy savings

– Evaluate energy signatures

– Past-performance benchmarking

– Multi-facility benchmarking

Page 8: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Our Analysis

• State-of-the-art analysis

– Complementary to EnergyStar – 6 metrics+ instead of one

– Expert analysis – highest quality returns - good advice will save you

time and money in the long run

– Clean database – trustworthy results

• Our higher education clients

– California State University

• Campus level

• Facility level

Page 9: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

State-of-the-Art Analysis: What is it?

• Energy Informatics

– Extracting useful information from energy data sets

• Multi-variable change-point statistical regression (MVR) models

– We call them Energy Signatures for short

– Graphical and mathematical model

– Regression beyond Excel

• Inverse Modeling

– Interpreting the physical significance of the energy signature

parameters

Page 10: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Who Uses It?

• EModel: Texas LoanSTAR Program

• Advanced PRISM: Princeton Center for Energy and Environmental

Studies

• IMT: ASHRAE Guideline 14 on Measurement and Verification

• ETracker: U.S. EPA Energy Star Buildings Program

• Regression Method: International Performance Measurement and

Verification Protocols

Page 11: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

MVR, What is it Good for?

• Baselining energy use

• Normalizing energy use

• Forecasting energy use

• Identifying energy reduction opportunities

• Past-performance and multi-building benchmarking

• Continual monitoring

• Measurement of energy savings

Page 12: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Why use it?

• Powerful analysis tool

• Next generation temperature normalization

– HDD/CDD Actual temperature data and TMY2

• Its There! – Data is readily available

– Utility data (except for un-metered buildings!)

– Temperature data – free on web

• engr.udayton.edu/weather

• Weatherunderground.com

Page 13: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

MVR Analysis of Utility Billing Data: Overview

• 1. Characterize performance with ‘Energy Signature’ model

• 2. Remove noise with ‘Normalized Annual Consumption’ NAC

• 3. Track performance with ‘Past Performance Benchmarking’ - ‘Sliding NAC’ analysis

• 4. Compare performance with ‘Multi-Facility Benchmarking’ analysis

Page 14: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Data Requirements

Monthly electricity and fuel use: utility bills

Actual outdoor air temperature

Typical outdoor air temperature

Influential variables (optional)– Floor area, occupancy, sales, production, etc.

Page 15: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

1. Characterize Performance with ‘Energy Signature’ Model

• Develop 3PC or 3PH energy signature model

• Disaggregate energy use

• Identify energy saving opportunities– Expected shape– Coefficient analysis– Fit analysis

Page 16: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Load Gas and Temperature Data

3 Years of Gas Bills 3 Years of Temperature Data

Page 17: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Three-parameter Heating (3PH) Model

Page 18: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Three-parameter Heating (3PH) Model

Gas Use = Eind – HS (Tbal – Toa)+

Eind

Tbal

HS

Page 19: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Three-parameter Cooling (3PC) Model

Page 20: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Three-parameter Cooling (3PC) Model

Eind

Tbal

CS

Elec Use = Eind + CS (Toa – Tbal)+

Page 21: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Physical Meaning of Coefficients

• Eind = Temperature-independent energy use

• Tbal = Balance temperature= Outdoor temperature where heating/cooling

begins = Tsp – Qint/UA

• HS = Heating Slope

= Heating energy per degree below Tbal = UA/Eff_Heater

• CS = Cooling Slope

= Cooling energy use per degree above Tbal = UA/Eff_AC

Tsp

Qint

UA = building conductance x area

Page 22: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Add Additional Variables For Industrial, Commercial, Retail Facilities

• Add additional independent variables (IV) to make 3P-MVR models

• Gas Use = Eind + HS x (Tcp -Toa)+ + (IV1 x Occupancy) + (IV2 x Sales)

• Elec Use = Eind + CS x (Toa - Tcp)+ + (IV1 x Occupancy) + (IV2 x Sales)

Page 23: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Energy Saving Opportunities:‘Lean Energy’ Breakdowns

• Lean Manufacturing: Any ‘activity’ that does not directly add value to the product is waste

• Lean Energy: Any ‘energy’ that does not directly add value to the product is waste.

• Hence, ‘independent’ energy use is energy that does not vary with production or weather, and may be waste.

• Target independent energy use for savings opportunities.

Page 24: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Lean Energy Breakdown: High Independent Electricity Use

Equipment left on when not in use– Lights

– Computers

– Vending machines

• Poor load-following behavior– Un-staged air compressors

– Throttled pumps

– Part-loaded chillers

• No load-following behavior– Constant-volume pumping

– Constant-volume air flow

– Compressed air leaks

Indian Riffle Electricity Use Breakdown

Lights, Fans, Motors, etc.

78%

Air Conditioning11%

Occupant Dependent

(Lights, Computers,

etc.)11%

Page 25: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Lean Energy Breakdown: High Independent Fuel Use

• Equipment left on when not in use– Ovens, furnaces, boilers

• Poor load-following behavior – Boilers with load/unload

control

• No load-following behavior – Reheat/cooling interaction

Indian Riffle Gas Use Breakdown

Space Heating89%

DHW11%

Page 26: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Using Models to Identify Problems: Chillers Left On

R2 = 0.92 CV-RMSE = 22.4%

Page 27: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Using Models to Identify Problems: Malfunction Economizer

R2 = 0.70 CV-RMSE = 7.8%

Page 28: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Using Models to Identify Problems: High Scatter = Poor Control

R2 = 0.59 CV-RMSE = 68%

ObservationHeating energy varies by 3X at same temp!DiscoveryDidn’t close shipping doors!

Page 29: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Using Models to Identify Success:Low Scatter = Good Control

R2 = 0.99 CV-RMSE = 1.1%

Page 30: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Measuring Actual Savings:Insulation

Average savings = 24.2 ccf/month = 290 ccf/yr

Page 31: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Normalized Annual Consumption: NAC

• Utility bills tells us ‘Annual Consumption’, which how much energy facility consumed with weather, sales, production, etc. that actually occurred

• We want to know how much energy building would have consumed during ‘normal’ weather, sales, production, etc.

• This is called ‘Normalized Annual Consumption’ NAC

• Calculating NAC is a two step process.

Page 32: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Calculating NAC:Step 1 of 2:

Actual gas bills + Actual weather data = Energy signature model

Page 33: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Calculating NAC:Step 2 of 2:

Energy signature model + Typical weather = NAC

Page 34: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

NAC is “Noisefree” Energy Consumption

• NAC removes ‘noise’ from variable weather

• NAC reveals true energy use characteristic of facility

• NAC allows comparison of sites with different weather

Page 35: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Past-Performance Benchmarking

• Track ‘Noiseless’ Performance with ‘Sliding NAC’ Analysis

• Calculate NAC for every twelve month period in data set.

• Change in NAC indicates change in building energy use characteristic

• Understand change in NAC by examining change in energy signature coefficients

Page 36: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Sliding NAC:Calc NAC for Every 12-month Period

Page 37: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Sliding NAC and Heating Slope

Page 38: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Sliding NAC and Independent Fuel Use

Page 39: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Sliding NAC and Balance Temperature

Page 40: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Multi-Facility Benchmarking

• Quantify average energy performance and distribution of energy performance across all sites

• Benchmark best/worst NAC and change in NAC

• Benchmark best/worst coefficients and change in coefficients

Page 41: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

NAC and Change in NAC for 355 Sites

Page 42: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Slope and Change in Slope for 355 Sites

Page 43: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Benchmark Disaggregated Energy Performance

Heating Change-Point

50

52

54

56

58

60

62

64

66

68

70

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Best P

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iffle

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view

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Van B

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Beave

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Mea

dows

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hard

(F)

Page 44: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Let the Data Speak: Summary

• Characterize Performance with ‘Energy Signature’ Model– 3PC-MVR or 3PH-MVR baseline model– Disaggregate energy use (Lean Energy Analysis)– Identify energy saving opportunities– Measure ‘actual’ savings

• Remove Noise with ‘Normalized Annual Consumption’ NAC– Reveals true energy use characteristic of facility– Allows comparison of sites with different weather, sales, prod, etc.

• Track Performance with ‘Sliding NAC’ Analysis– Identify problems/improvements when they happen– Understand nature of change with coefficient analysis

• Benchmark Performance with ‘Multi-site Sliding NAC’ Analysis– Determine center and spread of NAC and coefficients– Benchmark best/worst NAC and change in NAC– Benchmark best/worst coefficients and change in coefficients

Page 45: Applying State-of-the-Art Analysis to Utility Billing Data Ohio Board of Regents April 25, 2006 Bowling Green, OH April 26, 2006 Columbus, OH John Seryak.

Thank you!

questions to

[email protected](937) 474-5196


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