ASHRAE LV-11-020-2011 Improvements to a Methodology for Estimating Potential Energy Savings from Existing Building-Commissioning Retrofit Measures.pdf

1、868 ASHRAE TransactionsABSTRACTThis paper presents a screening methodology for estimat-ing potential energy savings from existing building-commis-sioning/retrofit measures prior to conducting an audit orassessment. This methodology compares the measuredconsumption of the building with the consumptio

2、n predicted byan optimized simulation based on the modified bin method. Themethod is illustrated with application to a commissioned exist-ing building; results show that the potential for savings fromresetting minimum airflow in this building is significant (20%and 54% during occupied and unoccupied

3、 periods, respec-tively) while additional savings obtainable by resetting theroom temperatures, cold deck leaving air temperature, andoutside air intake are limited (7% and 12% during occupiedand unoccupied periods, respectively).INTRODUCTIONToday, as energy prices increase, saving money on energybi

4、lls through an existing building commissioning (EBCx) oran energy retrofit project is attractive to many commercialbuilding owners. At the beginning of such a project, someform of screening is often applied to determine whether thereis sufficient potential for savings to justify an EBCx assess-ment

5、or an energy audit. If screening results are positive, theassessment/audit is performed and the potential for energysavings in the building is evaluated before the owner/operatordecides that further work is likely to produce significantenergy savings meeting the owners economic criteria. A popular t

6、echnique that is used to screen for savingspotential in a building is to compare its energy use per squarefoot of gross area to a group of buildings of similar type in thesame climate. This technique is also known as energy bench-marking. Although this technique is very easy to use when asatisfactor

7、y database is available and gives some idea aboutthe relative efficiency of the building, buildings are not alwaysas similar as they appear. The buildings used for comparisonare not necessarily energy efficient in general, and it gives noindication of energy conservation measures (ECMs) that meritco

8、nsideration in the subsequent EBCx or retrofit process.Some of the improved energy benchmarking methods found inrecent studies (Mills et al., 2008; Mathew and Mills, 2008;Yalcintas, 2006 and Cipriano et al., 2009) show potential insuggesting ECMs, but the other limitations still apply. Variousenergy

9、 simulation tools are also available to energy engineers.They can be used to predict savings from implementation ofcertain ECMs by changing inputs and comparing results.However, they are not designed to project the potential ofsavings in a building without detailed information about thebuilding and

10、the built-in system; in addition, they are usuallycomplicated to use. Consequently, it would be desirable tohave a methodology that is capable of predicting the opportu-nities for savings from low-cost/no-cost measures indepen-dent from the energy performance of other buildings. And, yet,this method

11、ology should be easy enough to use in the earlyphase of EBCx assessments or energy audits to help decide ifa comprehensive assessment should be carried out to identifycommissioning measures or ECMs for further analysis. Baltazar-Cervantes (2006) proposed such a methodol-ogy for estimating the potent

12、ial energy savings in commer-cial buildings. At its core is a procedure for minimizing theenergy cost required to maintain indoor thermal comfort.This methodology was applied to several existing buildingsthat have been retrofitted and/or commissioned. Themeasured savings in one of the buildings was

13、about 85% ofImprovements to a Methodology for Estimating Potential Energy Savings from Existing Building-Commissioning/Retrofit MeasuresJingjing Liu Juan-Carlos Baltazar, PhD David E. Claridge, PhD, PEStudent Member ASHRAE Member ASHRAE Fellow ASHRAEJingjing Liu is a graduate student in the Departme

14、nt of Mechanical Engineering, Texas A the minimum supply airflow is not optimizedsince the optimized value is always equal to the designatedlower limit. In addition to the above changes, room tempera-ture setpoints in the exterior and interior zones are included asadditional optimization parameters,

15、 since space loads aredependent on these two parameters.In summary, five parameters are selected for optimizationin this study: exterior and interior zone room temperaturesetpoints, cold deck and hot deck leaving air temperaturesetpoints, and outside airflow rate. In addition, in the method-ology im

16、plemented in the PESE toolkit, options are providedto users to optimize any combination of these five parameters.This is helpful in evaluating savings based on the existingcontrol capability. For example, Baltazar (2006) noted that hismethodology assumed an economizer and predictably seemedto over-e

17、stimate savings at lower outside air temperatures inbuildings that do not have an economizer. In such cases, theuser can choose to estimate potential savings based on thecurrent system setting, or to determine the extra savingsachievable by installing an economizer.Limits on Optimization Parameter V

18、alues. To makethe optimization result useful, it is important to set appropriatelower and upper limits on the values of optimization parame-ters. These limits should be determined based on the specialrequirements of each application. However, the considerationsused to determine the limits in the cas

19、e study are given here forreference.Room temperature setpoints. ASHRAEs general designcriteria for commercial and public buildings can be adoptedwhen there is no specific requirement for room temperatureand relative humidity control. For example, 70F78F(21.1C25.6C) and up to 60% RH are acceptable fo

20、r offices(ASHRAE 2007a) during occupied periods; 65F85F(18.3C29.4C) and up to 70% RH can be used as reset valuesduring unoccupied periods.Cold deck and hot deck leaving air temperature setpoints:Limits on these setpoints usually vary from project to project.Following common EBCx practice in hot and

21、humid climates,the reset ranges can be 55F70F (12.8C21.1C) for colddeck and 70F110F (21.1C43C) for hot deck tempera-tures.Outside airflow rate. Minimum outside air supply in thebreathing zone required by ANSI/ASHRAE Standard 62.1-2007 (ASHRAE 2007b) can be adopted as a lower limit. Forexample, 5 cfm

22、/person (2.4 L/sperson) and 0.06 cfm/ft2(0.31 L/sm2) are generally required in offices; however, theoutside air requirement can be only 7 cfm/person (3.3 L/sperson) with no minimum requirement on cfm/ft2when a CO2sensor is available to maintain a CO2level of 1000ppm.Minimum Airflow Setting. Exterior

23、 and interior zoneminimum airflows are not parameters to be optimized by themethodology employed in this study. However, resettingminimum airflow is an important commissioning measure inVAV systems and usually has significant influence on theenergy use. In EBCx practice, the minimum airflow should b

24、echecked and reset if necessary for each individual VAV termi-nal box. This requires knowledge of the loads in each space aswell as design information and terminal box details. Since thismethodology is developed to assist in the early stages of anEBCx or energy audit process, the above information i

25、susually not available and not much effort can be expended todetermine minimum airflow. According to Taylor and Stein(2004), ANSI/ASHRAE Standard 62.1-2007 for ventilation,and ANSI/ASHRAE Standard 90.1-2007 for energy(ASHRAE 2007c), the minimum airflow during the occupiedperiod can be reset to the l

26、argest of the following: (1) theairflow required to meet the design heating load at a supply airtemperature that is not too warm (e.g., 85F 29.4C),(2) 30% of design airflow or 0.3 cfm/ft2(1.5 L/sm2) if thedesign airflow is oversized, or (3) the minimum breathingzone outside air required by ANSI/ASHR

27、AE Standard 62.1-2007. The minimum airflow during the unoccupied period canbe reset to zero in many cases.Space Load Calculation. In Baltazar-Cervantess (2006)implementation, space cooling and heating loads are calcu-lated based on fixed occupied period room temperaturesetpoints (e.g., 75F 23.9C). T

28、his can lead to inaccurateoptimization results when the room temperatures are opti-mized using unoccupied resets and seasonal resets because theconduction load makes up a significant fraction of the totalspace load and is proportional to the difference between theroom temperature and the outside air

29、 temperature. In officebuildings, for example, room temperature can have a relativelywide acceptable range: 70F78F (21.1C25.6C) duringoccupied periods and 65F85F (18.3C29.4C) duringunoccupied periods. Therefore, in this study, a space loadcalculation procedure is developed based on the modified binm

30、ethod and linked with the optimization procedure, so thespace load will be recalculated dynamically as room temper-ature setpoints change in the optimization process.Simulation of Buildings with Multiple Types ofSystems. Many buildings have more than one type of system.To make the methodology applic

31、able to such buildings, thefractions of exterior and interior zone areas served by eachtype of system are applied to calculated whole-building exte-rior and interior zone space loads. It is assumed that the spaceload is proportional to floor area. This assumption works wellfor buildings having each

32、type of system serving an entirefloor or several floors, or buildings having two different typesof systems serving the exterior zone and interior zone, respec-tively.Air-Handling Unit (AHU) Shut-Down Simulation.Shutting down the AHU(s) during unoccupied periods is acommon and effective ECM. When the

33、 AHU is turned onbefore the building is occupied again, it has to bring the room2011 ASHRAE 871temperature to setpoint in a short time. Observations of themeasured consumption data show that the cooling or heatingenergy consumption during the start-up period is usuallysignificant. In addition, durin

34、g a shut-down period, the AHUwill normally be turned on if a lower or upper limit on theroom temperature is reached. Therefore, the cooling and heat-ing energy use during the unoccupied period needs to be esti-mated in a reasonable manner. This energy use can beestimated to be approximately equal to

35、 the sum of the largesttwo components of the space load: the internal heat gain andthe conduction load.During the AHU shut-down period, the room temperaturechanges under the influence of internal heat gain and conduc-tion through the building envelope. As a result, the conductionload can be signific

36、antly different from that when the roomtemperature is kept at the occupied period setpoint. This chal-lenges one of the major limitations of the modified binmethod, which is based on time-averaging techniques anddoes not take the thermal capacitance of the space intoaccount. However, based on the me

37、asured data in an officebuilding in Texas, where AHU shutdown has been imple-mented, it is found that the average room temperature duringthe unoccupied period has an approximately linear relation-ship with the average outside air temperature. This finding isused to estimate the average conduction lo

38、ad during the unoc-cupied period. It is noted that the relationship can vary frombuilding to building depending on the buildings size,construction, internal heat gain, etc. Nevertheless, the rela-tionship obtained is used as the default in the methodologyimplemented in the PESE Toolkit; this relatio

39、nship can bemodified based on engineering considerations as warranted.Air-Side Simulation Models. Air-side simulationmodels for four common HVAC systems are included: single-duct and dual-duct constant volume systems, as well as single-duct and dual duct VAV systems. The models employed inBaltazar-C

40、ervantess (2006) methodology largely came fromthe modified bin method, which was developed by ASHRAETC4.7 and described in Knebel (1983). These models are usedin this study with several modifications to better represent theperformance of the systems for the conditions simulated. CASE STUDYIntroducti

41、onThe improved methodology implemented in the PESEToolkit was tested in a single-story building with a total areaof 19,363 ft2(1799 m2). The building consists of a largedisplay hall, offices, a small library, and a conference room. Itis generally open between 8:00 a.m. and 5:00 p.m., Mondaythrough F

42、riday, except on holidays. The HVAC system is asingle-duct variable-air-volume (SDVAV) system with termi-nal reheat VAV boxes. One air-handling unit (AHU) serves theentire building. Implementation of EBCx measures wascompleted in this building on November 2, 2007. The current energy use is first sim

43、ulated and the simula-tion calibrated to measured energy use. Next, energy savingsare estimated using single-parameter optimizations, i.e., opti-mizing one applicable parameter at a time, for better under-standing of the optimized profile of parameters and energyuses. Finally, potential energy savin

44、gs are estimated by aperforming multiple-parameter optimization, i.e., optimizingall the applicable parameters together. Simulation without OptimizationThe basic building information including dimensions,internal heat gain, envelope characteristics, etc., required inthe simulation with the PESE Tool

45、kit is collected by on-siteinvestigation. Information about the HVAC system is obtainedfrom the energy management system (EMS) with someparameters remaining uncertain. The subsequent single-parameter optimization requires that the values of theseparameters be determined, which is accomplished using

46、themethod of calibrated signatures developed by Wei et al.(1998). The “calibrated” input values used in the simulationare given in Figure 3, which shows the input interface of thePESE Toolkit. One year of hourly weather and measuredconsumption data for December 1, 2007, through November30, 2008, (po

47、st-EBCx) are sorted into 5F (2.8C) bins withoccupied and unoccupied periods distinguished, as shown inFigure 4. No parameter is selected for optimization at thispoint. The simulated annual energy use and costs after cali-bration are given in Table 1 as the baseline for optimization. Reset Minimum Ai

48、rflowFollowing the procedure described earlier, it is deter-mined that the exterior and interior zone minimum airflowduring the occupied period in this building is reset from7200 cfm (3398 L/s) and 11,080 cfm (5229 L/s) to 3820 cfm(1803 L/s) and 3600 cfm (1699 L/s), respectively, and theminimum airf

49、low during the unoccupied period is reset from6370 cfm (3006 L/s) and 9800 cfm (4625 L/s) to zero. Thesereset values are used in the following single and multipleparameter optimizations. Figure 5 compares the cooling, heat-ing, and fan power energy use before and after resetting theminimum airflow. The result shows that the savings that can beachieved from this minimum flow reset alone are very signif-icant: total reductions of 20% and 54%during occupied andunoccupied periods, respectively, as shown in Table 2.Single-Parameter Optimizatio