Continuous Efficiency Improvement Loop

Transcription

1 Make Data Driven, Continuous Efficiency Improvements as Standard Practice: Technical Loop Lessons Learned & Databases Update Building(s) Benchmarking (Fleet Databases) Energy Use & Performance Deviation Assessments: Inverse & Forward Models Continuous Efficiency Improvement Loop Performance Monitoring ECM Implementation with Sub-Metering, Data Acquisition Plan Building (Type & Size Specific) Metering & Sub-metering: Sub-System End Use Energy Data Indoor Environment Data As-Operated Inverse Model Building Analytics & Modeling Existing, As-Operated Forward Whole Building & End Use Energy & Performance Models for ECM Selection Proposed ROI Prioritized ECMs: Equipment, Controls (Single Building or Fleet Application) 19

2 ake Data Driven, Continuous Efficiency Improvements as Standard Practice 2

3 Make Data Driven, Continuous Efficiency Improvements as Standard Practice Policy-Market-Behavior Loop Codes, Standards, Guidelines Outer Policy-Market Behavior (PMB) Loop (Enabling Whole Building Efficiency Standard Practice ) e.g. dedicated end-use sub-panels; level of sub-metering ROI Enablers Triple Net Value Propositions Asset Improvement Business Models Energy Savings Value Models Work Force Training ROI & Education Enablers Triple Net Value Propositions Asset Improvement Business Models Energy Savings Value Models Work Force Training & Education Codes, Standards, Guidelines (Enabling Whole Building Efficiency Standard Practice ) e.g. dedicated end-use sub-panels; level of sub-metering Disclosure Ordinance Environment Continuous Efficiency Improvement Loop (CEIL) Disclosure Ordinance Databases Regional, National, Fleet Benchmarking Databases Disclosure Ordinance Databases Regional, National, Fleet Benchmarking Databases Building Specific Energy & IEQ Data Transfer e.g. Energy Star Portfolio Building Specific Energy & IEQ Data Transfer e.g. Energy Star Portfolio Utility/Policy Incentive Qualified Protocol-Based Utility/Policy Energy Incentive Audit Qualified Protocol-Based Toolkit Energy Audit Toolkit Market Driven Environment Investment Tools Utility, Low Interest On-Bill Financing Data Verified ECM Rebates Standardized Asset Improvement Valuation Measured Continuously Improving Building Stock 11/11/2013 Slide: 20 1/15/2014 Slide: 3

4 Make Data Driven, Continuous Efficiency Improvements as Standard Practice Measure Consumptio n Measure Energy Driver Energy Modeling Monitoring Set Targets Report Take Action 4

5 Convenience Store Chain Seeks to Substantially Reduce Energy Use in Fleet EEB Hub Seeks to Establish CEI Loop Paradigm for All Such Energy Intensive Fleets FLEET Characterization A local Office Building 101,700 ft 2 2,175,880 kwh/year 0 therms/year 73 kbtu/(ft 2 -year) A typical Philadelphia Convenience Store 5,589 ft 2 648,080 kwh/year 1,351 therms/year 420 kbtu/(ft 2 -year) Full Service Convenience Stores = 3,500 Mid-Sized (35-40,000 ft 2 ) Office Buildings

6 Benchmarking within Wawa s Portfolio Stores w Gas Station (Fuel) Stores w/o Gas Station (Non-Fuel)

7 Identify Energy Outlier Stores Excessive Energy? Check outliers first: Incorrect metering Malfunctioning equipment 95% Confidence Interval: Fuel Stores: kbtu/ft 2 -yr Non-Fuel Stores: kbtu/ft 2 -yr A plot of a normal distribution where each band has a width of 1 standard deviation. Fuel Stores (Threshold, kbtu/ft 2 -yr) Non-Fuel Stores (Threshold, kbtu/ft 2 -yr) Outliers 5 (582) 1 (581) Top 2.2% (EUI>Mean+2*StDev) 11 (549) 6 (545)

8 Benchmarking within Convenience Store Fleet Portfolio EUI (kbtu/ft 2 - yr) Sample Size (N) All Stores Fuel Stores Non-Fuel Stores Max Q Median Mean Q Min No difference! 185 kbtu/ft 2 -yr difference!

9 2012/2011 Electricity Consumption Operation Changes in 2012? 2012/2011 Electricity Consumption Incorrect Metering? Why do these stores use 20% more? Cumulative Number of Stores

10 Fuel Stores have better energy efficiency than Non-Fuel Stores. Newer fuel stores (better efficient refrigeration sys. & bldg. efficiency)might offset the dispenser fuel pumps & outdoor lights. This assumption can be further investigated by comparing end-use data between Fuel and Non- Fuel Stores. Site EUI: kbtu/ft 2 Site EUI: kbtu/ft EUI vs. Store Age (Fuel-Type) Age EUI vs. Store Age (Non-Fuel Type) Age

11 Summary In general, Fuel Stores consume less energy per square foot per transaction than Non-Fuel Stores. 95% of Fuel Stores use 427±122 kbtu/ft 2 -yr, while 95% of Non-Fuel Stores use 425±120 kbtu/ft 2 -yr. Identified 17 Excessive Energy Use Stores. Identified stores that consume significantly more energy in 2012 than that of Conducted statistical tests, identified three most influential independent parameters customer transaction #, square footage, store age, based on currently available store info.

12 Data Requirements for CEIL Building Parameters Statistical Model Energy Use Drivers Database Building Information Wea ther Gas Energy Electricity Performance Cha ra cteristics Electricity Submetering

13 Kwhr (Sub Panels) 600 Store 592 Energy Utilization Data Kwhr (Main Feed) Lighting Refrig HVAC Main RPA_Daily_Usage RPB_Daily_Usage RPC_Daily_Usage RPE_Daily_Usage RPG_Daily_Usage Refrig_Daily_Usage HVAC_Daily_Usage LPA_Daily_Usage MainElectric /10/10 2/26/11 9/14/11 4/1/12 10/18/12 5/6/13 11/22/13 6/10/14

14 ake Data Driven, Continuous Efficiency Improvements as Standard Practice Weather/Non weather dependent energy use (MMT) (Kissock, 2010) 14

15 kwh/day kwh/day MMT Control Group (Electricity) T oa Store T oa Normalization Store Non Normalized Normalized /6 1/22 8/10 2/26 9/14 4/1 10/18 5/6

16 kwh/day kwh/day 1200 MMT Control Group (Electricity) Toa Store Toa 1200 Normalization Store Non Normalized Normalized /6/2009 1/22/2010 8/10/2010 2/26/2011 9/14/2011 4/1/ /18/2012 5/6/2013

17 kwh/day MMT Control Group (Electricity) kwh/day 1600 Toa Store Toa Normalization Store Non Normalized Normalized /6 1/22 8/10 2/26 9/14 4/1 10/18 5/6

18 Data Summary / IMT Results All Data IMT based calculations (All Data) Store Best Model Cooling Balance Temperature [ F] Electric consumption at balance termperature [kwh/day] Approximated Storefront orientation direction [ ] Store Area [ft2] R2 All data kwh/day ft2 at balance temp 43 4P / SW P / NW Control 128 3P / NE P / SE P / SE Special case 247 4P / SE

19 Data Summary / IMT Results Base Line IMT based calculations (Base Line) Store Best Model Cooling Balance Temperature [ F] Electric consumption at balance termperature [kwh/day] Approximated Storefront orientation direction [ ] Store Area [ft2] R2 Base line kwh/day ft2 at balance temp 43 4P / SW P / NW Control 128 4P / NE P / SE Special case 240 4P / SE P / SE

20 Weather Related Energy Consumption Comparison Electricity consumption [kwh/day] Electricity consumption [kwh/ft2 day] Weather Related Electricity Consumption Store Base Electricity Consumption [kwh] Weather Related Electricity Consumption [kwh] Weather Related Electricity Consumption (Normalized) Store Base Electricity Consumption [kwh] Weather Related Electricity Consumption [kwh]

21 Weather Related Energy Consumption Comparison (Store 710) Electricity consumption [kwh/day] 1600 Store 710 Weather Related Electricity Consumption Pre 710 Post Base Electricity Consumption [kwh] Weather Related Electricity Consumption [kwh]

22 kwh/day CVDD Store 43 (Electricity / Base Line) CVDD Store 43 (28 F Base Temperature / Base line / ) y = 4.425x R² = CDD/Day

23 CUSUM [kwh/day] CUSUM Store 43(Electricity) 200 CUSUM / Store Jan-10 May-10 Aug-10 Nov-10 Feb-11 Jun-11 Sep-11 Dec-11 Apr-12 Jul-12 Oct-12 Jan

24 CUSUM [kwh/ft 2 day] 0.5 Comparative CUSUM per Store Area Jan-10 Aug-10 Feb-11 Sep-11 Apr-12 Oct Store A Store B Store C Store D Store E Store J Target Level 1 Target Level 2 Target Level 3

25 Electric consumption [kwh/day] Retrofits and CEIL 1500 Normalization Store K / Electricity Dec-09 Jun-10 Jan-11 Jul-11 Feb-12 Aug-12 Mar-13 Oct-13 Normalized [kwh/day] Non Normalized [kwh/day]

26 Gas consumption [Therms/day] Retrofits and CEIL 6.00 Normalization Store K / Gas Dec-09 Jun-10 Jan-11 Jul-11 Feb-12 Aug-12 Mar-13 Oct-13 Normalized [Therms/day] Non Normalized [Therms/day]

27 CUSUM [kwh/day] CUSUM / Store K (Post retrofit savings) 0 Jul-12 Aug-12 Oct-12 Dec-12 Jan-13 Mar-13 May-13 Jun-13 Aug

28 CUSUM [Terms/day] 6.00 Cusum / Store K (post remodel savings) Jul-12 Aug-12 Oct-12 Dec-12 Jan-13 Mar-13 May-13 Jun-13 Aug-13

29 The CEIL Process for Energy Intensive Stores Data Recollection EUI Outdoor Air Temperature Correlation Base Line Monitoring (Inverse Energy Modeling-CUSUM) Store Type A Age Level 1 CUSUM Store Inspection (Repairs, modifications, retrofit) Age Level 2 CUSUM Store Type B Age Level 1 CUSUM Age Level 2 CUSUM

30 Make Data Driven, Continuous Efficiency Improvements as Standard Practice Technical Loop Lessons Learned & Databases Update Building(s) Benchmarking (Fleet Databases) Energy Use & Performance Deviation Assessments: Inverse & Forward Models Continuous Efficiency Improvement Loop Performance Monitoring ECM Implementation with Sub-Metering, Data Acquisition Plan Building (Type & Size Specific) Metering & Sub-metering: Sub-System End Use Energy Data Indoor Environment Data As-Operated Inverse Model Building Analytics & Modeling Existing, As-Operated Forward Whole Building & End Use Energy & Performance Models for ECM Selection Proposed ROI Prioritized ECMs: Equipment, Controls (Single Building or Fleet Application) 19

31 As-Operated Building EnergyPlus Models Importance of Plug Load Input

32 Building #1 (saves 11% of annual energy use) Savings Breakdown Building #2 (saves 14% of annual energy use) Note: Lighting- lighting related retrofits including day lighting harvest, occupancy sensor based control, etc.; DCV- Demand Control Ventilation; DSP- Duct Static Pressure Reset; SAT Reset- Supply Air Temperature Reset;

33 kwh/day Analysis of Warehouse Electric Energy Use Data: - Two electric accounts serve the warehouse space (387,500) - Total electric usage for base line year was: 1,783,000 kwh - One electric account showed relationship between outside air temperature and building energy use; other electric account did not show this type of relationship - Three methods were used to determine energy savings: - Monthly year-to-year comparison based on utility billing data - Weather normalized base line comparison - Average weekday and weekend hourly loadshape comparison Weather Normalized Data Analysis Warehouse - Large Consumption Meter Ave Outside Air Temperature (F) Base Line Ave daily Consumption (Actual) Post-Retrofit Ave Daily Consumption Weather Normalized Base Line Ave Daily Consumption (Regression)

34 Develop Long Term, Staged ECM Investment Plan Methodology Based on Analysis of Ten Year Data for ECM Performance Property A, B, C,.. ECM x ECM y ECM z Time Energy Use (Electricity, Gas)

35 Natural Gas Use Domestic Hot Water (Meter 2)