7. Passivhus Norden Sustainable Cities and Buildings. Method for comparison of Be10-calculated and actual energy use

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1 Copenhagen, August Passivhus Norden Sustainable Cities and Buildings Brings practitioners and researchers together Method for comparison of Be10-calculated and actual energy use SUMMARY This paper presents a method that aims at 1) providing a realistic forecast of the actual energy consumption for building operation in the design phase, and 2) enabling the comparison of Be10 calculated energy use and the actual energy use of buildings. The method is based on three-point estimations. The method is thereby working with energy performance in terms of probability rather than relying on the deterministic output values from Be10. The method has a range of benefits for building owners, designers, contractors and facility managers. The forecast provides a realistic building operation budget and identities parameters most important to energy use, i.e. the focus areas during design, planning and construction. The comparison is a quality assurance of whether the building is actually fulfilling the intended performance targets regarding energy use. The performance of the method relies on good estimates of the actual building operation schedules in the design phase and correction of inexpedient representation of the building physics in Be10. Furthermore, building owners should consider data logging by secondary meters to reduce the uncertainty of the comparison. KEYWORDS Energy performance, Performance gap INTRODUCTION The first Energy Performance of Buildings Directive (EPBD, 2002) requires that the energy performance of a new building in the European Union have to be certified to ensure that it fulfils the minimum national requirement. The requirement was made to ensure that the huge potential for energy-efficiency of buildings was realised and thereby contributing to overall aim of a fossil-free energy production system. This political agenda became even more clear in the recast of the EPBD from 2010 where it is stated that all new buildings constructed after 2020 should consume "near zero energy" for building operation (EPBD, 2010). The certification process varies from country to country and is very often based on a calculation of the expected energy use (Lausten et al., 2010). But does this calculated energy use correspond to the actual energy use? It is not the first time this concern is put forward, and findings from several investigations show that buildings do not operate as predicted during the design phase (Maile, 2010). This is a problem for the individual building owner when estimating realistic building operation budgets in the design phase. But the question also becomes important to society as a whole as near-zero energy buildings (that is actual and not calculated nearzero energy buildings) through EPBD is an important piece in the greater puzzle that aims at a fossilfree energy system. A research project funded by the Danish energy research and development program ELFORSK seeks to develop a method for comparison of calculated energy use according to the Danish national calculation method called Be10 (Aggerholm and Grau, 2008) and the actual energy use of buildings. The purpose of the method is 1) to get a more realistic forecast of the actual energy consumption for building operation in the design phase, and 2) to make quality assurance and fault detection of the final building. This paper presents the principles of this method and applies the method for analysis of an actual passivhaus certified kindergarten situated in the Copenhagen area, Denmark. METHODS Forecast of energy use for building operation in the design phase The method for forecast of the actual energy consumption for building operation in the design phase is based on an uncertainty analysis, the so-called three-point estimation (Fazar, 1959), of the Be10 calculation based on sensitivity analysis of the input parameters to the Be10 calculation. The step-wise approach is as follows: Page 1/7

2 1. A complete Be10 calculation for the designed building is produced. The input values and outputs of this calculation are considered to be the likely value, i.e. the best estimate of reality. 1. Estimation for sensitivity analysis of input parameters: a. Estimate of a pessimistic value of the input parameter (pessimistic > likely) b. Estimate of an optimistic value of the input parameter (optimistic < likely) 2. The pessimistic and optimistic values from step 2 are used for a one-at-the-time (OAT) sensitivity analysis (Gardner et al., 1980) in Be10. The result is a likely, maximum (pessimistic) and minimum (optimistic) estimate of the primary energy need for heating and electricity, respectively, for each input parameter. 3. The results from step 3 is used for a three-point estimation: a. Calculate for each OAT analysis in step 3 (A is the optimistic estimate, B is the pessimistic estimate and C is the expected estimate): (A+ 3C+ B) Mean value: M = 5 (B- A) Standard deviation: S = 5 2 Variance: V S b. Calculate the total mean value and standard deviation: Average mean value: Total standard deviation: n M n n 1 Mav = where n is the number of OAT analysis n S total = V 4. A confidence interval of M total +2S total, i.e. a 97% likelihood that the energy use is below this value, is calculated. The method assumes that there is no correlation between input parameters in the Be10 calculation. This is not entirely correct in theory but investigations has shown that correlation of inputs to Be10 is not of a significant scale (Wagner and Madsen, 2012) (Jensen and Jørgensen, 2014). The parameters to be estimated in step 2 can be divided into three different types of uncertainty: 5. Parameters the precision/uncertainty of the input parameters to Be10. There are in principle 269 input parameters to a Be10 calculation. In practice the calculation is only considered to be significantly sensitive to approx parameters. 6. The calculation method the representation of the building physics. There are a number of simplifications in Be10 that under certain conditions are critical to the precision of the output. First of all, Be10 is based on the quasi-static method in ISO (ISO, 2008) which is an overall simplification of the physics compared to an hourly-based method. Other simplifications in Be10 are the simple representation of window U-value (Petersen, 2014), thermal mass, representation of daylight, and infiltration. The effects of these simplifications are considered as uncertainties to the calculation result. It is not considered the scope of this paper to describe how this method handles these relatively complex physical phenomena. 7. Completeness is all of the mechanisms and sources of error handled? Be10 is considered to be complete enough for its purpose when taking the issues in point 2 into account. Comparison of actual and Be10 calculated energy use The method prepares for a comparison of actual and Be10 calculated energy use. The step-wise approach is as follows: 1. Required data: Page 2/7

3 a. Actual heating and electricity energy use for building operation for a full year. The granularity of the data is decisive for the magnitude of the uncertainty in the comparison. A paper by Petersen and Hviid (2012) identifies the minimum data needed for a comparison: total heating use, hourly electricity use, total tap water use, the hourly fan power used by the ventilation system. However, many uncertain assumptions are needed when processing this minimum dataset. The uncertainty is all thing being equal decreased if the total heating use was logged as monthly values (ultimately hourly and on room level), and if actual energy use for hot water, lighting, ventilation, heating and cooling coils, pumps and automation was logged hourly on secondary meters. Furthermore, indoor climate data such as hourly room temperature, CO 2 concentration, PIR sensors and other sensors would also decrease the uncertainty. b. The final Be10 calculation of the building approved by the authorities prior to the commissioning of the building. c. Actual (historic) hourly values of outdoor temperature, relative humidity and global solar irradiation for the year of operation. This data is used to generate the monthly mean weather data file used by Be Correct the Be10 calculation: a. Change the design weather data to the weather data of the investigated year of operation. b. Change input parameters if the actual operation data indicates that the actual operation has been different to the expected operation. Typical examples of such corrections are heating and cooling set points, operation time of mechanical ventilation, internal loads and hot water consumption. A deviation between expected and actual operation might be unintentional. In this case the comparison serves as fault detection but the calculation should nevertheless be corrected. The Be10 calculation should preferable be corrected for each month (the time step of Be10) using monthly mean values of actual data. 3. Use step 2-4 in the section Forecast of energy use for building operation in the design phase to add any uncertainty to the corrections. The uncertainties due to the calculation method are also added. 4. Calculate the confidence interval like in the section Forecast of energy use for building operation in the design phase. 5. Divide the actual energy use data the same way as in the Be10 calculated energy demand (space heating, domestic hot water, ventilation, lighting, pumps). 6. Examine whether the individual actual energy use from step 4 are within the confidence interval of the corrected Be10 calculation (incl. uncertainties). Remember that the calculated energy use for comparison should be the energy need before it is multiplied with primary energy factors. 7. If the individual actual energy use is not within the confidence interval from step 3, then a building inspection with a focus on verification of the parameters related to this specific energy use is recommended. Test case In this project, actual data from a total of seven low-energy buildings was collected. None of the buildings had the minimum data needed for comparison. The case used in this paper to test the method was the one closest to have the data needed. The building is a 1,140 m 2 passivhaus certified kindergarten situated in the Copenhagen area, Denmark. The constructions are considered to be very heavy, i.e. walls, floors and ceilings made of concrete, brick and tiles. RESULTS Forecast Page 3/7

4 The results of the uncertainty analysis used for forecast of the energy use for building operation in the design phase are depicted in table 1. Only the input parameters with a standard deviation (Std. dev.) of >0.2 is shown. The input parameters with a std. dev.<0.2 are represented with a single variance value. The result of the Be10 calculation is a primary energy use of 37.2 kwh/m 2 per year but taking into account the uncertainty of the calculation, the forecasted energy use is estimated to be 46.8 kwh/m 2 per year with a certainty of 97%. This uncertainty analysis did not take into account the risk of major operational faults or radically different use of the building and its systems. Table 1. The effect of a input parameter sensitivity analysis on the overall result of a Be10 calculation. All values are in kwh(primary)/m 2 heated gross area per year. Minimum Likely Maximum Mean Std. dev. Variance Hot water consumption Heat capacity Space heating due to weather Set point for room heating Lighting, installed effect In-use factor for lighting Time in use Infiltration Ventilation heating coil (due to set point for room heating) Minimum ventilation rate Ventilation heating coil (due to weather) Ventilation heating coil (uncertainty due to monthly mean method) U-value of windows Parameters with std. dev.< SUM Total standard deviation (S total ) 4.5 Confidence interval of M+2S total 46.8 The parameters in table 1 are sorted by their variance. This can help building designers, contractors and craftsmen to keep focus on the design parameters which has significant influence on the result. Analysis of actual energy use The case building has been in operation for two years and is ready for a comparison of the actual and Be10 calculated energy use. Table 2 shows the results from the two different phases: Forecast: the standard Be10 calculation (Be10) and the 97% certain forecast of the actual energy use design (expected). Analysis: the corrected Be10 calculation (corrected) and the actual energy use (actual). Page 4/7

5 Table 2. Overview of data for comparison between forecast and analysis. Be10 Forecast Expected (97%) Analysis Corrected (mean+2s total ) Actual Space heating (14.8+3) 19.4 Hot water (21.5+0) 21.5 Ventilation ( ) 2.7 Lighting N/A N/A Pumps etc N/A N/A Total (primary energy) N/A N/A The expected energy for space heating was lower than the actual use. The reason for this deviation was the initial input values to Be10. An interview with a senior staff member revealed that the actual time-in-use was much longer and that the actual internal load from persons was much lower. This was corrected in the Be10 calculation and the actual energy use for space heating came closer to, but still outside, the confidence interval of the corrected energy use. A follow-up interview revealed the deviation could be due to the use of venting during winter time and a significant heat loss due to the rather large traffic in and out of the building all day. Hourly actual heating data could help analyse this further. The expected energy use for hot water was much lower than the actual use. Again, the reason was a large difference in the initial input values to Be10 and the actual use. An explanation could be that the standard Be10 calculation value of 100 l/s m 2 per year for all other buildings than homes (which is 250 l/s m 2 per year) might be true for an office building but not for a kindergarten. Another explanation could be that much of the hot water is probably used for kitchen operation, i.e. process energy and whereby not part of the energy use for building operation. The kindergarten had secondary meters for electricity use of the mechanical ventilation and for appliances and lighting in different zones of the building. However, there was no logging of the meters making them useless for the analysis. The only logged electricity data was hourly values on the main meter of the building, see figure 1. Analysis of the power profile led to the estimate of electricity use for mechanical ventilation in table 2 but it was not possible to argue for an estimate of the actual energy use for electrical lighting as this estimate was very sensitive assumptions regarding the actual operation. Figure 1: Hourly electricity use logged by the main meter of the kindergarten. DISCUSSION In the test case, the method for forecasting energy use underestimated the actual energy use. The reason was partially due to inexpedient representation of the building physics in Be10 calculation method, and partially due to the fact that actual operation and use of the building deviated a lot from assumptions used for Be10 calculation. The analysis of the case building, where the input to the Page 5/7

6 calculation is corrected for lacks in the representation of the building physics as well as actual building operation and use, indicated that Be10 is able to provide fairly precise estimates of the actual energy consumption. Better forecasts are therefore possible if the Be10 calculation method is corrected for inexpedient representation of the building physics prior to calculating any uncertainty of input. Especially the representation of effective thermal mass in the building needs to be corrected. Furthermore, the likely value of input data regarding the building operation and use (e.g. hot water use and building time-in-use) should be considered carefully prior to calculating the uncertainty of the input. Otherwise there is a risk that the actual ( likely ) values is higher than the optimistic estimate or lower than the pessimistic value in the three-point estimation method. The consequence is that the forecast will end up being very different from the actual energy use. A practical experience from this study was that it can be quite difficult to get sufficient actual data to perform the analysis. It was only possible to get the total annual heating consumption and tap water use for the building as a whole. Electricity use was logged as hourly values but only for the building as a whole. Only a few buildings had secondary meters and when they did the data was not logged. All of the buildings had central building automation systems with a relevant data flow but no data was logged. What can be learned from this is that it is not enough that building owners demand meters and central building automation systems. They should also explicitly define requirements for logging of data. Executing the forecast as well as the analysis using the proposed method is currently a very timeconsuming process due to many manual data treatments (up to 6 hours for a trained person). The method should be automated before it can become practical applicable. Once automated the forecast is expected to take few seconds and the analysis to take a few minutes (depending on the detail level of actual data). CONCLUSIONS A method with two purposes is proposed: purpose 1) to get a more realistic forecast of the actual energy consumption for building operation in the design phase, and purpose 2) to compare calculated energy use and the actual energy use of buildings. Purpose 1 is highly valuable for building owners when estimating operation budgets. A derived benefit of purpose 1 is that the method calculates the variance of the individual design parameters. This helps building designers, contractors and craftsmen to rank design parameters by their variance thereby to focus on the parameters which has significant influence on the result. Purpose 2 is highly valuable to the building owner as it enables them to assess whether the building is actually fulfilling the intended performance targets regarding energy use. The method identifies any deviations from the intended performance. These deviations might be due to intentional changes of the building, its use or operation but they might also be due to faults in constructions, systems or operation of the building. The method thereby works as a fault detection tool of the final building. The method was tested on an actual passivhaus certified kindergarten. The test identified the need for better estimates of actual building operation schedules in the design phase and correction of inexpedient representation of the building physics in Be10. The test also showed that logging by secondary meters is essential to 1) the execution of the analysis and, 2) the uncertainty of the comparison of calculated and actual energy use. It is especially the electricity use for ventilation, lighting and cooling that should be metered separately. ACKNOWLEDGEMENT The author gratefully acknowledges the financial support of this work through the project Energy sinners in low energy buildings financed by the Danish energy research and development program ELFORSK. REFERENCES Aggerholm S. and Grau K SBi anvisning 213: Bygningers energibehov - Pc-program og beregningsvejledning (version ), Hørsholm, Denmark: The Danish Building Research Institute. EPBD Directive 2002/91/EC of the European Parliament and of the Council of 16 December 2002 on the energy consumption of buildings. EPBD Directive 2010/31/EU of the European Parliament and of the Council of 19 December 2010 on the energy consumption of buildings (recast). Page 6/7

7 Fazar W Program Evaluation and Review Technique, The American Statistician 13 (2), p.10. Gardner R.H., O'Niell R.V., Huff D.D., Mankin J.B. and Carney, J.H Application of Error Analysis to a Marsh Hydrology Model. Water Resource Research 16 (4), pp ISO ISO 13790, Energy performance of buildings: Calculation of energy use for space heating and cooling. Switserland: International Organization for Standardization. Jensen N.K.T. and Jørgensen T Koncept til sammenligning af Be10-beregnet energibehov og målt energiforbrug. M.Sc. thesis, Aarhus University (Denmark), 114 pages. Maile T Comparing measured and simulated building energy performance data. Ph.D. dissertation, Stanford University (California, USA), 145 pages. Petersen S The effect of weather data on glazing U-value in building performance simulation. In: Proceedings for 10th Nordic Symposium on Building Physics. Lund University (Sweden). Petersen S. and Hviid C.A The European Energy Performance of Buildings Directive: Comparison of calculated and actual energy use in a Danish office building. In Proceedings for 1st IBPSA-England conference on Building Simulation and Optimization. Loughborough University (England). Wagner M. and Madsen N Usikkerheder ved forudsigelse af bygningers ydeevne. M.Sc. thesis, Aarhus University (Denmark), 140 pages. Page 7/7