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A single-variate building energy signature approach for periods with substantial solar gain
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. Industrial Doctoral School, Umeå University, Umeå, Sweden.ORCID iD: 0000-0002-7332-1521
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
2016 (English)In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 122, 185-191 p.Article in journal (Refereed) Published
Abstract [en]

The use of regression analysis for the identification of building performance parameters based on measurements is often difficult due to collinearity between the outdoor temperature and the global solar radiation (S). This study proposes a method to overcome this issue. The proposed method is based on using the seasonal symmetry of S to pair data from time-periods equidistant from the winter solstice. In addition, a method to utilize synthetic data to fine-tune the paired-data approach is presented. To evaluate the paired-data approach, two years data from a multifamily building in Umeå was used to estimate the heat loss factor (air-to-air transmission including air leakage). The results were compared with results obtained when S was very low (S ≈ 0). It was found that, the fine-tuned paired-data approach resulted in a modest deviation in the heat loss factor with an average absolute deviation of 4.0%. The small deviation indicates that the paired-data approach can extend the use of single-variate regression models for accurate identification of heat loss factors to situations where the solar gain is substantial. The paired-data approach was also used to calibrate a commercial energy building simulation tool. 

Place, publisher, year, edition, pages
2016. Vol. 122, 185-191 p.
Keyword [en]
Regression, Energy signature models, IDA-ICE, Building, Solar gain
National Category
Building Technologies
Identifiers
URN: urn:nbn:se:umu:diva-117244DOI: 10.1016/j.enbuild.2016.04.040ISI: 000376833300018OAI: oai:DiVA.org:umu-117244DiVA: diva2:906393
Available from: 2016-02-24 Created: 2016-02-24 Last updated: 2016-08-29Bibliographically approved
In thesis
1. 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.
Keyword
Calibrated simulations, Energy signature, Regression, BES, IDA ICE, Multifamily buildings, EUI
National Category
Energy Engineering
Identifiers
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)
Opponent
Supervisors
Available from: 2016-02-25 Created: 2016-02-24 Last updated: 2016-02-24Bibliographically approved

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