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An approach, able to easily and effectively integrate field measured data in whole Building Energy Simulation (BES) models is crucial to increase simulation accuracy for existing buildings. In this paper the robustness of a linear regression method for extracting transmission losses above ground (including air leakage) and ground heat loss parameters are analyzed. The regression method is evaluated on two documented and monitored multifamily buildings with mechanical supply and exhaust ventilation systems, with and without heat recovery.   

The obtained results are found to be robust, with variations less than 2% in the extracted estimates of transmission losses above ground (including air leakage) and with a high goodness of fit (R²>0.96) against measured data from two years. In addition, the estimations of the buildings ground heat loss were in good agreement with calculations in accordance with EN ISO 13370:2007. The high quality output from the used regression method serves as good prerequisites for the method to be used in conjunction with BES models to aid the analyst in a BES calibration process

It is generally accepted that the most correct decisions are made when the used support system provides the most accurate description of the starting point as possible. That is, in this case, a detailed initial description of a building, planned to be refurbished and evaluated with the building energy simulation software IDA ICE (v 4.5).In order to assess this statement, we have used two different models to predict energy savings due to different planned energy conservation measures (ECMs):

 - A basic model based on inputs from currently available standards and as-built drawings.

 - A calibrated model based on an analysis of measurements from two months, together with measured air handling unit parameters, hourly electricity usage and indoor temperatures.

The relative prediction differences between the models are investigated as well as compared with the actual outcome in a neighboring building where the analyzed ECMs have been implemented. The result indicates that a calibrated model should be used, in order to accurately determine the post-retrofit energy demand. However, if only investigation of ECMs which aims to decrease a buildings transmission loss is of interest, the findings suggest that BES calibration is of minor importance.

The follow-up of calculated and actual energy performance for new buildings is important to enable a learning process. Performance verification is however not a trivial task since the traditional energy use intensity indicator (EUI) can display large variations even for buildings with similar design and HVAC systems. Hence, there exists a risk for confusion between building owners and developers when predicted and actual outcome are compared using only this indicator. In this paper, simple methods, based on area normalization and regression analysis are investigated for interpretation of wide discrepancies in measured EUIs within four similar, newly built multifamily buildings in Umeå, Sweden. It was found that the discrepancies in specific annual energy demand were dependent on the area used for normalization but did not fully explain the variation in the EUIs. The utilization of linear regression for identification and comparison of the buildings heat-loss factor, (ventilation and transmission), and effective solar aperture provided further insights. The regression analysis indicated that the differences in EUIs were due to a combination of chosen area for normalization and solar gain and not the consequence of variations in actual U-values and HVAC systems. Due to the regression methods robustness against influence from the users, it was concluded that the method works well as a complement to the EUI indicator since it provides insights of the buildings thermal performance. This is often of interest to verify for the developer and the property owner since the thermal performance can be controlled in the construction process.