MEASUREMENT AND VERIFICATION TOOLS Spiros Metallinos Regulatory Authority for Energy, Greece

Transcription

1 EnPC Intrans International Conference, November 15, 2016 MEASUREMENT AND VERIFICATION OF SAVINGS MEASUREMENT AND VERIFICATION TOOLS Spiros Metallinos Regulatory Authority for Energy, Greece

2 EnPC Intrans International Conference, November 15, 2016 Introduction The Regulatory Authority for Energy ( RAE ) building is the first in Greece to have an EMS according to ISO standard installed, based on measurement and verification procedure for the energy savings, dated February The measurement and verification procedure followed is according to the International Performance Measurement and Verification Protocol (hereafter referred to as IPMVP) which is not a requirement of the ISO standard itself. RAE s ISO energy management system which includes this measurement and verification procedure according to IPMVP is certified by the independent certification body TUV Austria Hellas.

3 EnPC Intrans International Conference, November 15, 2016 List of sections 1. Building and climate characteristics 2. Linking ISO with the IPMVP and ASHRAE Guidelines 3. Presentation of the adopted IPMVP option 4. Challenges and solutions 5. Natural gas regression model 6. Savings and uncertainty calculations 7. Review and conclusions 8. Questions

4 SECTION 1: Building and climate characteristics Page 1/47 The RAE Building The Headquarters of the Regulatory Authority for Energy [RAE] is located in the center of Athens, at Piraeus Avenue 132. The surface area of the building is: m² (from the 1 st to the 7 th floor) m² (2 basement levels) 85 m² (ground floor) It comprises: 1. Heating (hydronic network feeding 251 FCUs and heated by two natural gas burners, 543 kw each) 2. AC-Air conditioning (hydronic network fed by two chillers, 451 kw each) 3. Ventilation (pre-climatised air with m 3 /h total fresh air volume, supplied by two AHUs) 4. Lighting 5. IT-Data Center 6. Devices

5 SECTION 1: Building and climate characteristics Page 2/47 Climate Characteristics The city of Athens has a hot-summer Mediterranean climate: The dominant feature of Athens' climate is alternation between prolonged hot and dry summers and mild winters with moderate rainfall. The RAE building has AC needs (heating and cooling), which are related to an area equal to m². For the implementation of the weather calculations of this study we used weather data from the NASA database that is provided freely by a Canadian Government software named RETSCREEN

6 SECTION 2: Linking ISO with the IPMVP and ASHRAE Guidelines Page 3/47 The Energy Baseline - ISO According to paragraph 3.6 for the Energy Baseline: it reflects a specified period of time. it gives us a quantitative reference providing a basis for comparison of energy performance. it can be normalized using variables which affect energy use and/or consumption, e.g. production level, degree days (outdoor temperature), etc. it is also used for calculation of energy savings, as a reference before and after implementation of energy performance improvement actions. ISO does not describe how to normalize the Energy Baseline

7 SECTION 2: Linking ISO with the IPMVP and ASHRAE Guidelines Page 4/47 The Energy Baseline - ISO According to paragraph 4.4.4: The organization shall establish an Energy Baseline(s) using the information in the initial energy review, considering a data period suitable to the organization's energy use and consumption. Changes in energy performance shall be measured against the Energy Baseline(s). Adjustments to the Energy Baseline(s) shall be made in the case of one or more of the following: EnPIs no longer reflect organizational energy use and consumption, or there have been major changes to the process, operational patterns, or energy systems, or according to a predetermined method. The Energy Baseline(s) shall be maintained and recorded.

8 SECTION 2: Linking ISO with the IPMVP and ASHRAE Guidelines Page 5/47 Use of the IPMVP Given that ISO does not describe how to normalize the baseline we used the IPMVP (International Performance Measurement and Verification Protocol) in order to implement a robust M&V plan ex ante. Is the IPMVP compatible with ISO 50001? The answer is in D-11 of IPMVP under the title : Energy Users Seeking ISO Certification. IPMVP: The management methods required under ISO focus on managing total facility utility costs. IPMVP s Options C and D (Whole Facility and Calibrated Simulation) describe methods useful for this purpose, even if no savings are being sought. IPMVP cautions on the challenges of detecting small changes in whole facility utility energy use data. IPMVP offers both general guidance and specific guidance on how large total facility energy use changes must be to be reportable with any statistical validity.

9 SECTION 2: Linking ISO with the IPMVP and ASHRAE Guidelines Page 6/47 IPMVP and ASHRAE Guideline 14 Is there any connection between IPMVP and ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers) Guideline 14 (Guideline Measurement of Energy and Demand Savings)? ASHRAE Guideline 14: 1. provides complementary details for IPMVP. 2. has many of the same original authors as IPMVP. 3. provides technical details following many of the same concepts of IPMVP (but it does not use the same Option names as IPMVP.). 4. is a unique and useful resource for M&V professionals around the world. IPMVP: refers to ASHRAE Guideline 14 for practical details of the M&V methodology.

10 SECTION 2: Linking ISO with the IPMVP and ASHRAE Guidelines Page 7/47 ISO 50001, IPMVP and ASHRAE Guideline 14 ISO Provides general guidance about energy saving IPMVP Provides general guidance about M&V ASHRAE Guideline 14 Provides practical details about M&V By using the above mentioned documents we started forming our M&V procedure

11 SECTION 3: Presentation of the adopted IPMVP option Page 8/47 The Baseline concept A. We have two periods: 1. The Baseline Period (before any retrofits) 2. The Reporting Period(s) (after any retrofits) B. With data from Baseline period, we develop a mathematical equation that correlates the energy consumption of the building with independent variable(s) that cause this consumption: C. The mathematical model will give different energy use or demand as each period has different independent variables (values) and the energy performance of the building after the ECM is expected to be improved. We refer to this mathematical equation of the Baseline Period as Baseline model

12 SECTION 3: Presentation of the adopted IPMVP option Page 9/47 The Baseline concept Savings = Adjusted Baseline Energy Reporting Period Measured Energy = (Baseline Energy Reporting Period Measured Energy ) ± Adjustments

13 SECTION 3: Presentation of the adopted IPMVP option Page 10/47 Which Baseline Period? The Baseline Period should be established to: Represent all operating modes of the facility. This period should span a full operating cycle from maximum energy use to minimum. Fairly represent all operating conditions of a normal operating cycle. Include only time periods for which all fixed and variable energy-governing facts are known about the facility. Coincide with the period immediately before commitment to undertake the retrofit.

14 SECTION 3: Presentation of the adopted IPMVP option Page 11/47 Savings calculation According to IPMVP there are two approaches to calculate savings: Savings (Energy and Cost savings) Avoided Energy Use or Avoided Cost Stated under conditions of the Reporting Period Normalized Savings Stated under fixed or normal conditions Savings = (Baseline Energy Reporting Period Energy) ± Routine Adjustments ± Non-Routine Adjustments

15 SECTION 3: Presentation of the adopted IPMVP option Page 12/47 The Adjusted Baseline Energy The Adjusted Baseline Energy is defined as the Baseline Energy plus any routine adjustments needed to adjust it to the conditions of the reporting period. The Adjusted Baseline Energy is normally found by first developing a mathematical model which correlates actual baseline energy data with appropriate independent variable(s) in the baseline period. Each reporting period s independent variable(s) are then inserted into this baseline mathematical model to produce the Adjusted Baseline Energy use. An independent variable is a parameter that is expected to change regularly and have a measurable impact on the energy use of a system or facility. For example, a common independent variable governing building energy use is outdoor temperature.

16 SECTION 3: Presentation of the adopted IPMVP option Page 13/47 Summary of Baseline terms Baseline Period = Reference period before any energy interventions take part in a building Energy Baseline / Baseline Energy = Quantitative reference providing a basis for comparison of energy performance Adjusted Baseline Energy = Baseline Energy plus any routine adjustments needed to adjust it to the conditions of the reporting period Baseline Energy Equation / Baseline Equation = Mathematical expression that correlates the energy consumption of the building with independent variables that cause this consumption Baseline Method = Method used to predict the energy consumption of a building for a certain period in the hypothetical case that energy interventions didn t occurred Baseline = In general, the term Baseline can be associated with the term Reference

17 SECTION 4: Challenges and solutions Page 14/47 Facts Baseline period In the RAE building occurred the following events: In 2010 and in the first semester of 2011 there have been lots of changes in the patterns of consumption such as alteration of setpoints, facility working hours etc as well as there was not an acceptable thermal comfort. In 2011 RAE s Board decided to implement an EMS in line with ISO which included a multifaceted energy management program affecting many systems in the building. In summer 2011 the building started to operate normally and the building management team set the building in a way that ensured thermal comfort to the occupants. During 2012 the EMS was designed and the first energy review was commissioned in October In 2013, the M&V procedure was updated using the same baseline data but in line with IPMVP and Ashrae Guideline 14 methodology.

18 SECTION 4: Challenges and solutions Page 15/47 1) Which option to follow? The IPMVP provides four options for determining savings (A, B, C and D): A. Retrofit Isolation: Key Parameter Measurement B. Retrofit Isolation: All Parameter Measurement C. Whole Facility D. Calibrated Simulation

19 SECTION 4: Challenges and solutions Page 16/47 1) Which option to follow? We decided to use the Whole Facility option (C). This option is based on utility bills that assumed to have zero uncertainty. Some of the reasons for this decision of option C are the following: 1. Savings calculation at the facility level. 2. No calibration program = no reliable data 3. Human resources and money for a sophisticated A, B or even more D option. 4. No plan for major changes to the premises and staff for years to come. 5. Α multifaceted energy management program planned 6. IPMVP reporting requirement : Savings to be expressed in energy and monetary units. 7. IPMVP s Retrofit Isolation Options A and/or B are appropriate management tools for specific energy efficiency projects but they cannot be correlated with Whole Facility utility bills.

20 SECTION 4: Challenges and solutions Page 17/47 2) Which are the independent variables that have impact to the NG consumption? IPMVP states that in order to judge the relevance of independent variables it is required both experience and intuition. The associated t-statistic is one way of confirming the relevance of particular independent variables included in a model. Experience in energy analysis for the type of facility involved in any M&V program is necessary to determine the relevance of independent variables.

21 SECTION 4: Challenges and solutions Page 18/47 2) Which are the independent variables that have impact to the NG consumption? To identify the significance of individual coefficients, t-statistics are used. Tstatistics are the ratio of the coefficient estimate divided by the standard error of the estimate. b t SEb For a coefficient to be statistically meaningful, the absolute value of its t-statistic must be at least 2.0. Under no circumstances should a variable be included in a regression if the standard error of its coefficient estimate is greater than half the magnitude of the coefficient (even when including a variable that increases the R 2 ).

22 SECTION 4: Challenges and solutions Page 19/47 2) Which are the independent variables that have impact to the NG consumption? In order to improve the t-statistic result we should: Select independent variable(s) with the strongest relationship to energy Select independent variable(s) whose values span the widest possible range (if X does not vary at all in the regression model, b cannot be estimated and the t- statistic will be poor) Gather and use more data points to develop the model Select a different functional form for the model, for example, one which separately determines coefficient(s) for each season in a building that is significantly affected by seasonal weather changes.

23 SECTION 4: Challenges and solutions Page 20/47 2) Which are the independent variables that have impact to the NG consumption? Given the instruction of the textbooks we did not included the occupancy which remained stable during the baseline period and proved to have a t-statistic < 2. The only significant variable detected was the DD (Degree Days) for both electricity and Natural gas models with the exception of the low price zone electricity model.

24 SECTION 4: Challenges and solutions Page 21/47 3) Where do we find reliable weather data? A simple definition for DD (Degree Days): DD are a measurement designed to measure the demand for energy needed to heat/cool a building. DD are derived from measurements of outside air temperature. The heating/cooling requirements for a given building at a specific location are considered to be directly proportional to the number of DD at that location.

25 SECTION 4: Challenges and solutions Page 22/47 3) Where do we find reliable weather data? 1. In order to calculate the DD we need reliable weather data as ASHRAE Guideline 14 states. 2. We had temperature sensors and a lot of data in our databases but they were not reliable since there was not a calibration plan. 3. IPMVP advises: Where monthly energy measurements are used, weather data should be recorded daily so it can be matched to the actual energy-metering reading dates. For monthly or daily analysis, government published weather data is usually the most accurate and verifiable. When analyzing the response of energy use to weather in mathematical modeling, daily mean temperature data or DD (Degree Days) may be used.

26 SECTION 4: Challenges and solutions Page 23/47 3) Where do we find reliable weather data? In our estimations we used weather data from the NASA database that is provided freely by a Canadian Government software named RETSCREEN, thus reducing the cost of M&V procedures

27 SECTION 4: Challenges and solutions Page 24/47 4) Which base-point to use in order to calculate the DD? ASHRAE Fundamentals describes the procedure of modeling utility bill data: 1. To obtain the equation coefficients through regression, the utility bills must be normalized by the length of the time interval between utility bills. This is equivalent to expressing all utility bills, degree-days, and other independent variables by their daily averages. 2. Appropriate modeling software is used in which values are assumed for heating and cooling balance points. From these, the corresponding heating and cooling degree-days for each utility bill period are determined. 3. Repeated regression is done till the regression equation represents the best fit to the meter data. 4. The model coefficients are then assumed to be tuned. Some programs allow direct determination of these optimal model parameters without the user s manual tuning of the parameters.

28 SECTION 4: Challenges and solutions Page 25/47 4) Which base-point to use in order to calculate the DD? IPMVP states that any reference temperature may be used for recording DD (Degree Days), though it is usually chosen to reflect the temperature at which a particular building no longer needs heating or cooling. Since the only significant variable is the weather, by implementing the above methodology we calculated the appropriate balance point or reference temperature for the DD. After this tuning we developed the regression model that best fits to the metered data.

29 SECTION 4: Challenges and solutions Page 26/47 5) IPMVP requires savings to be expressed in energy and monetary units The pricing policy of Natural Gas and Electricity utility bills makes difficult to associate the energy consumption with monetary units. Tariff schedule for Natural Gas: Fixed demand tariff normalized by the length of the billing period Commodity tariff (consumption) multiplied by the higher calorific value (HCV) for each month or period Taxes and levies on consumption VAT Tariff schedule for Electricity: Competitive charges Regulated charges High pricing zone Transmission system charges Low pricing zone Distribution system charges Demand within peak period Charges for the reduction of gas emissions Demand during the off-peak period Taxes and levies Cos(ф)

30 SECTION 4: Challenges and solutions Page 27/47 5) IPMVP requires savings to be expressed in energy and monetary units We thoroughly examined the tariff schedule of RAE Electricity and Natural Gas suppliers and we decided to establish the following models in order to be able to calculate the appropriate tariff drivers (parameters) in the reporting period: For the Electricity we established five mathematical models: High pricing zone Low pricing zone Demand within peak period Demand during the off-peak period For the Natural Gas we established one model since there is not up to date differed charge for peak and off-peak periods.

31 SECTION 5: Natural Gas regression model Page 28/47 Natural Gas consumption Heating Degree Days (HDD 18,77 ) and Natural Gas consumption (utility bills) Period Index Starting Date Ending Date Number of Days Total HDD (Kd) Total Consumption (kwh) P1 24/9/ /11/ , ,65 P2 24/11/ /1/ , ,06 P3 26/1/ /3/ , ,54 P4 24/3/ /5/ , ,92 P5 24/5/ /7/ ,00 0 P6 24/7/ /9/ ,00 0 SUM 1.082, ,17

32 SECTION 5: Natural Gas regression model Page 29/47 Natural Gas Baseline Equation After the preparatory procedure, where we examined the independent variables that have impact to the NG consumption, we concluded that the only independent variable that have measurable impact is the HDD. HDD was proved to be statistically significant variable as t-statistic was roughly 36. To establish the mathematical model for the Natural Gas Consumption of our building we had to correlate the consumption with the heating degree days (X) with the Natural Gas consumption. Υ = α 0 + α 1 Χ Where: Υ : Natural Gas consumption X : Heating Degree Days (HDD)

33 SECTION 5: Natural Gas regression model Page 30/47 Natural Gas Baseline Equation This equation simulates the Natural Gas consumption by using the method of least squares. The form of the regression models can be either purely statistical or loosely based on some basic engineering formulation of energy use in the building The identified model coefficients are such that no (or very little) physical meaning can be assigned to them. If we try to give to these constants physical meaning we could put it as follows: The constant α 0 (intercept) reflects the basic energy consumption (or base load) under normal operation without the influence of any kind of variables that trigger consumption. The constant α 1 (slope) is a constant that, being multiplied with the variable which is the heating degree days, results to the energy that is consumed when there are HDD. Υ = α 0 + α 1 Χ Where: Υ : Natural Gas consumption X : Heating Degree Days (HDD)

34 SECTION 5: Natural Gas regression model Page 31/47 Calculation of the Natural Gas Baseline Equation IPMPV gives examples of monthly model but measurements from our Natural Gas provider took place roughly every two months in the baseline period. The meter readings schedule was amended by our supplier after the Baseline period ( ) and it was announced (2012) that it would take place henceforth roughly every month. In order to eliminate Net Bias Error Due to Data Length Variation we used a more sophisticated regression technique on the basis that the ordinary least squares technique is a simplification of a more general case, when all data points are equally weighted. Since every data point covered less/ longer time period than others, it meant that it should carry more or less weight. Following Ashrae Guideline 14 procedures we regressed the average daily utility consumption data against billing period Degree Days using the appropriate weights (weighted regression).

35 SECTION 5: Natural Gas regression model Page 32/47 Calculation of the Natural Gas Baseline Equation Our two parameters α 0 and α 1 are derived from the following equations using the weighted regression: a 0 ( WY ( W WX 2 ) ( WX 2 ) ( WX WX) WXY) 2 a 1 ( W ( W WXY) ( WX 2 ) ( WY WX) WX) 2

36 SECTION 5: Natural Gas regression model Page 33/47 Baseline Analysis Results Given the above mentioned facts the Baseline Equation related to the Natural Gas: was assessed on mean daily values was detected throughout the reference period since no energy interventions occurred in the building is given by the equation: Υ = 194, ,6x Where: Y = average Natural Gas consumption (kwh/day) X = average Heating Degree Days (Kd/day)

37 SECTION 5: Natural Gas regression model Page 34/47 Baseline Analysis Results Heating Degree Days (HDD 18,77 ) and Natural Gas consumption (utility bills) Period Index Starting Date Ending Date Number of Days Total HDD (Kh) Average HDD (Kh/Day) Total Consumption (kwh) Average Consumption (kwh/day) P1 24/9/ /11/ ,83 2, , ,85 P2 24/11/ /1/ ,74 6, , ,79 P3 26/1/ /3/ ,23 7, , ,44 P4 24/3/ /5/ ,17 1, ,92 601,82 P5 24/5/ /7/ P6 24/7/ /9/ SUM 1.082, ,17 Following ASHRAE Guideline 14 procedures we regressed the average daily utility consumption data against billing period Degree Days using the appropriate weights (weighted regression) for a twelve month period without including the period from 24/5/2012 to 24/9/2012 since heating is by turned off default.

38 SECTION 5: Natural Gas regression model Page 35/47 Model Evaluation IPMVP provide us with three tests to evaluate our model: 1. Coefficient of Determination (R 2 ) R (ˆ y y) 2 2 i 2 ( yi y) 2. Standard Error of the Estimate (SE, CVRMSE & MBE) SE yˆ (ˆ yi yi) n p 1 2 CVRMSE ( y yˆ i i) /( n p) y MBE (ˆ Y Yi ) n 3. T-statistic t statistic b SE b Yˆ t SE Y ˆ Minimum Requirements for Compliance with Guideline 14 : For the three performance paths, the level of uncertainty shall not be greater than 50% of the annual reported savings (at the 68% confidence level).

39 SECTION 5: Natural Gas regression model Page 36/47 Model Evaluation Results of the calculated regression on Natural Gas Baseline REGRESSION RESULTS Natural Gas Validation Criteria R 2 0,998 > 0,75 t-statistic CDD or HDD 36,88 > 2 t-statistic intercept 2,82 > 2 STANDARD ERROR 3343,8 the lower the better CV(RMSE) 0,0465 <0,25 (ASHRAE) The methodology applied to verify the energy savings is more important than the absolute energy saving results

40 SECTION 6: Savings and uncertainty calculations Page 37/47 Savings The general equation for savings calculation is: Savings = (Baseline Energy Reporting Period Energy) ± Routine Adjustments ± Non-Routine Adjustments The regression model for the daily consumption of Natural gas is: Y = 354,6 X +194,2 We can now calculate the energy we would have consumed if we had not taken any ECMs in the reporting period by inserting the mean daily HDDs of the reporting period prior to non routing adjustments.

41 SECTION 6: Savings and uncertainty calculations Page 38/47 1 st Reporting period Begin End Billed Energy Daily Actual Energy Days Total HDD 18,77 Aver. HDD Modeled Monthly Energy 1st floor Energy Savings kwh /kwh Savings 25/9/ /11/ ,69 229, ,7 0, , ,05 0,08 650,79 23/11/ /1/ , , ,78 5, , ,21 0, ,55 24/1/ /2/ , , ,31 6, , ,46 0, ,41 23/2/ /3/ , , ,78 5, , ,66 0, ,18 27/3/ /4/ ,94 440, ,3 3, , ,18 0, ,41 23/4/ /5/ ,09 12, ,51 0, , ,04 0,08 547,66 Total , ,00

42 SECTION 6: Savings and uncertainty calculations Page 39/47 2 nd Reporting period Begin End Billed Energy Daily Actual Energy Days Total HDD 18,77 Aver. HDD Modeled Monthly Energy 1st floor Energy (estimation) Savings kwh /kwh Savings 26/9/ /10/ ,09 0, , ,53 0,07 520,70 25/10/ /11/ ,40 248, ,61 0, , , ,3 0,07 476,89 27/11/ /12/ , , ,88 5, , , ,66 0, ,86 24/12/ /1/ , , ,33 4, , , ,58 0, ,65 28/1/ /2/ , , ,95 5, , , ,84 0, ,50 25/2/ /3/ , , ,68 5, , , ,65 0, ,69 25/3/ /4/ ,67 549, ,96 3, , , ,58 0, ,46 26/4/ /5/ ,99 1, , ,71 0, ,73 Total , ,00

43 SECTION 6: Savings and uncertainty calculations Page 40/47 3 rd Reporting period Begin End Billed Energy Daily Actual Energy Days Total HDD 18,77 Aver. HDD Modeled Monthly Energy 1st floor Energy Savings kwh /kwh Savings 25/9/ /10/ , ,92 0,08 437,76 25/10/ /11/ ,36 602, ,81 1, , , ,64 0,08 646,25 25/11/ /12/ ,29 967, ,84 3, , ,98 0, ,83 24/12/ /1/ , , ,33 6, , , ,44 0, ,44 28/1/ /2/ , , ,33 7, , , ,23 0, ,91 25/2/ /3/ , , ,84 5, , , ,58 0, ,97 27/3/ /4/ ,77 674, ,14 3, , , ,56 0, ,26 29/4/ /5/ ,77 0, , ,91 0,07 587,40 Total , ,00

44 SECTION 6: Savings and uncertainty calculations Page 41/47 Uncertainty Every statistical model have some degree of uncertainty. What level of uncertainty is acceptable and how do we estimate it? IPMVP states that savings are deemed to be statistically valid if they are large relative to the statistical variations. If the variance of the baseline data is excessive, the unexplained random behavior in energy use of the facility or system is high, and any single savings determination is unreliable. The standard error of the baseline is calculated by the formula: SE ( Y i Yˆ) n , 92 n In order to be in line with the IPMVP requirement that savings need to be larger than twice the standard error of the baseline value we should exceed ,83 kwh savings in Natural Gas consumption. 2

45 SECTION 6: Savings and uncertainty calculations Page 42/47 Uncertainty SE ( Y i Yˆ) n 1 n , 92 Savings in all periods exceed the limit of ,83 kwh. Savings of the three Reporting periods in energy and monetary units Period Begin End Savings kwh Savings 1st Reporting period 25/9/ /9/ , ,00 2nd Reporting period 26/9/ /9/ , ,00 3rd Reporting period 25/9/ /9/ , ,00 Total , ,00

46 SECTION 6: Savings and uncertainty calculations Page 43/47 Energy Related Interventions in the building Title of intervention Date of commissioning To Reduce the Natural Gas consumption Ventilation Reduction: Redesign ventilation in 2 fresh air AHU 4/10/2012 Adjustments in automation to introduce free and night cooling in 2 fresh air AHU 4/10/2012 Improvement in the boilers and chillers automation sequence 12/11/2012 Geothermal use of phreatic well for fresh air preheating, reduction of water flow 11/07/2015 To Reduce the Electricity consumption Change in the schedule of the building s facilities: Lightning, Ventilation, Air handlers 4/11/2012 Intervention in the closed control unit plenary CCU-S18 in the plenary room 19/06/2012 Intervention in the electric boards of the stairwells 05/07/2012 Intervention in the lighting of the -5 basement garage, with control via motion detectors 29/07/2012 Ventilation Reduction: Redesign ventilation in 2 fresh Air handlers 10/10/2012 Intervention in building corridors lighting 12/02/2015

47 SECTION 7: Review and conclusions Page 44/47 Review The statistical criteria of the verification procedure used to prove the reported energy savings give excellent results since all requirements are highly satisfied. To achieve the presented savings, a number of low cost interventions has been implemented in the building, with a payback period of 1-5 years. The said savings are verified via the IPMVP tool and are as follows: The cumulative total real energy savings for the two-year period from 1/10/2013 to 30/9/2015 were ,30 kwh el+th or energy consumption savings up to 28,76%, compared to the normalized baseline data. The cumulative overall savings in monetary units for the same two-year period reached the amount of ,62 or 25,37% savings in monetary units, compared to the normalized baseline data. Concerning the uncertainty, the Natural Gas energy savings uncertainty of the 3 rd performance period, for instance, was 17,29% with confidence level 95% when the savings were calculated to be 48,58% compared to the normalized baseline.

48 SECTION 7: Review and conclusions Page 45/47 Conclusions The engineering techniques TAB, RD and MTB as well as the holistic Energy Management can give important energy savings. RAE s building case shows that the EPCs potential market in buildings can rely on low cost interventions and operate on existing commercial energy saving methodologies, as well as on verification procedures of the energy savings that allow for billing of these savings to be accurately measured by third parties for the said TAB techniques. For the future, until 2020, the Authority Management has set an ambitious target to reduce the building energy consumption down to 240 kwh/m 2 /y in EP terms, in line with the suggested value for the overall energy consumption in NZEB Mediterranean buildings. TAB = Testing Adjusting Balancing RD = Re-Design MTB = Measuring, Targeting and Benchmarking

49 SECTION 8: References Page 46/47 References IPMVP 2012, January 2012, International Performance Energy Measurement and Verification Protocol, USA EVO Efficiency Valuation Organization BPA - Bonneville Power Administration Regression for M&V - Measurement and Verification, September 2011, Reference Guide, Version 1.0 Greek Regulation for Energy Inspections (D6/Β/OIK 11038/FEK 1526/1999), 1999 NSW Government, December 2012, Measurement and Verification Operation Guide, Whole Building Applications, Office of Environment and Heritage ASHRAE, 2012, «Performance Measuring Protocol, a Best Practice Guide» ASHRAE, 2002, ASHRAE Guideline «Measurement of Energy and Demand Savings»

50 SECTION 8: References Page 47/47 References M. Karagiorgas, A. Adamopoulos, S. Metallinos, V. Georgiopoulos, G. Anastasopoulos, D. Zacharias, A&B Enreprise, Athens, Greece, Regulatory Authority for Energy, Athens, Greece, «Energy Saving Verification for an ISO in the RAE building», 3rd International Conference ENERGY in BUILDINGS 2014, EinB2014, pp , ASHRAE and ΤΕΕ, Athens Ledra Marriott, November 2014

51 SECTION 9: Questions MEASUREMENT AND VERIFICATION TOOLS Spiros Metallinos, Speaker Regulatory Authority for Energy, Greece ANY QUESTIONS?