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Utilizing a regression approach for troubleshooting energy performance of Swedish buildings
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. (Industrial Doctoral School)ORCID iD: 0000-0002-7332-1521
2016 (English)In: CLIMA 2016 - proceedings of the 12th REHVA World Congress, 2016, Vol. 8Conference paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
2016. Vol. 8
Keyword [en]
Energy use intensity, EUI, Regression, Heat loss factor, Shape factor
National Category
Civil Engineering
URN: urn:nbn:se:umu:diva-92398ISBN: 87-91606-33-0 (vol 8)ISBN: 87-91606-36-5 (set)OAI: diva2:740855
CLIMA 2016 - 12th REHVA World Congress, 22-25 May 2016, Aalborg, Denmark

Originally published in manuscript form.

Available from: 2014-08-26 Created: 2014-08-26 Last updated: 2016-05-30Bibliographically approved
In thesis
1. A regression approach for assessment of building energy performance
Open this publication in new window or tab >>A regression approach for assessment of building energy performance
2014 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Reliable evaluation methods is needed to ensure that investments in energy conservation measures (ECMs) and the construction of new energy efficient buildings lives up to the promised and expected performance.

This thesis presents and evaluates a regression method for estimation of influential building parameters: transmission losses above ground (including air leakage), ground heat loss, and overall heat loss coefficient.

The analysis is conducted with separately metered electricity, heating and weather data using linear regression models based on the simplified steady-state power balance for a whole building.

The evaluation consists of analyzing the robustness of the extracted parameters, their suitability to be used as input values to building energy simulations (BES) tools. In addition, differences between uncalibrated and calibrated BES models are analyzed when they are used to calculate energy savings. Finally the suitability of using a buildings overall heat loss coefficient as a performance verification tool is studied.

The presented regression method exhibits high robustness and good agreement with theory. Knowledge of these parameters also proved beneficial in BES calibration procedures as well as in performance verifications. Thus, the presented method shows promising features for reliable energy performance assessments of buildings.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2014. 25 p.
building energy simulation, measurements, regression, calibration parameters
National Category
Engineering and Technology
urn:nbn:se:umu:diva-92465 (URN)978-91-7601-125-6 (ISBN)
2014-09-17, A302, Håken Gullessons väg 20, Umeå, 10:00 (English)
Available from: 2014-09-26 Created: 2014-08-26 Last updated: 2014-09-26Bibliographically approved
2. Improved building energy simulations and verifications by regression
Open this publication in new window or tab >>Improved building energy simulations and verifications by regression
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

It is common with significant differences between calculated and actual energy use in the building sector. These calculations are often performed with whole building energy simulation (BES) programs. In this process the analyst must make several assumptions about the studied building and its users. These calculations are often verified with measured data through the EUI benchmark indicator which is calculated by normalizing the annual energy use (from the grid) with the floor area. Due to the highly aggregated nature of the EUI indicator it is problematic to use this indicator to deduce erroneous assumptions in the calculations. Consequently, the learning process is often troublesome.

Against this background, the main aim of this thesis has been to develop methods that can provide feedback (key building performance parameters) from measured data which can be used to increase simulation accuracy and verify building performance. For the latter, regression models have been widely used in the past for verifying energy use. This thesis has the focus on the use of regression analysis for accurate parameter identification to be used to increase the agreement between BES predictions and actual outcome. For this, a BES calibration method based on input from regressed parameters has been developed which has shown promising features in terms of accurate predictions and user friendliness. The calibration method is based on input from regressed estimations of air-to-air-transmission losses, including air leakage (heat loss factor) and ground heat loss. Since it is known that bias models still can give accurate predictions, these parameters have been evaluated in terms of robustness and agreement with independent calculations. In addition, a method has been developed to suppress the bias introduced in the regression due to solar gain. Finally, the importance of calibrated simulations was investigated.

The regressed parameters were found to be robust with yearly variations in the heat loss factor of less than 2%. The regressed estimates of ground heat loss were also in good agreement with independent calculations. The robustness of the heat loss factor based on data from periods of substantial solar gain was also found to be high, with an average absolute deviation of 4.0%. The benefit with calibrated models was mainly found to be increased accuracy in predictions and parameters in absolute terms. With increased access to measured data and the promising results in this thesis it is believed that the presented regression models will have their place in future energy quantification methods for accessing energy performance of buildings. 

Abstract [sv]

Det är vanligt med betydande skillnader mellan beräknad och verklig energi användning inom byggnadssektorn. Dessa beräkningar utförs ofta med hjälp av byggnads energi simulerings (BES) program där användaren måste göra ett flertal antaganden om den aktuella byggnaden och dess brukare. Det beräknade resultatet kontrolleras ofta i ett senare skede mot byggnadens faktiska behov av energi från nätet. I denna kontroll är det dock svårt att särskilja den energimängd som byggnaden behöver och den del som är kopplad till brukaren. Detta gör att lärdomarna som kan dras i denna verifieringsprocess ofta blir begränsade.

Mot denna bakgrund, har det huvudsakliga syftet med denna avhandling varit att utveckla metoder som kan användas för att extrahera information om byggnadens prestanda från mätdata. De extraherade parametrarna skall kunna användas för att öka noggrannheten i prediktioner från BES modeller och för att verifiera byggnaders prestanda. Regression analys har ofta använts i det senare fallet i avseendet att verifiera energi användning. Denna avhandling fokuserar på att utveckla regressionsmodeller som ger en hög noggrannhet i modellens parametrar som möjliggör att de bl.a. kan användas för att kalibrera BES modeller och på så sätt minska den vanligt förekommande diskrepans mellan simulerat och faktiskt utfall. En BES kalibrerings metodik har utvecklats baserat på skattning av transmissions förluster ovan mark, inklusive luftläckage (värmeförlust koefficient) samt värmeförlust till mark (G) med hjälp av regressionsanalys. Denna kalibrerings metodik uppvisar lovande egenskaper i form av noggranna prediktioner och användarvänlighet. Goda prediktioner är dock ingen garanti för att modellens ingående parametrar är fysikaliskt rimliga. Därför har regressionsmodellernas parametrar utvärderats i termer av robusthet och överensstämmelse med oberoende beräkningar. Dessutom har en metod utvecklats för att minimerar solens inverkan på regressionsskattningarna. Slutligen har vikten av kalibrerade simuleringar undersökts.

Parametrarna i de framtagna regressionsmodellerna visade sig vara robusta, med årliga variationer i värmeförlust koefficient mindre än 2%. Ytterligare visade sig G var i god överensstämmelse med oberoende beräkningar. Robustheten i värmeförlustfaktorn baserad på data från perioder av betydande solstrålning konstaterades också att vara hög, med en genomsnittlig absolut avvikelse på 4.0%. Fördelen med kalibrerade modeller visade sig främst vara en ökad noggrannhet i prediktioner och modell parametrar i absoluta tal. Med ökad tillgång till mätdata och lovande resultat i denna avhandling är det författarens övertygelse att de presenterade regressionsmodellerna kommer att ha sin plats i framtida bedömnings metoder av byggnaders energiprestanda.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2016. 51 p.
Calibrated simulations, Energy signature, Regression, BES, IDA ICE, Multifamily buildings, EUI
National Category
Energy Engineering
urn:nbn:se:umu:diva-117248 (URN)978-91-7601-430-1 (ISBN)
Public defence
2016-03-17, N450, Naturvetarhuset, Umeå universitet, Umeå, 13:00 (English)
Available from: 2016-02-25 Created: 2016-02-24 Last updated: 2016-02-24Bibliographically approved

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