Wie genau sind unsere Energiepässe?

Merzkirch, Alexander; Hoos, Thorsten; Maas, Stefan; Scholzen, Frank; Waldmann, Danièle

doi:10.1002/bapi.201410007

Cited by 7 publications

(6 citation statements)

References 2 publications

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“…This goes along well with the results of other studies [3], [4], [5], [6]. In Figure 3 the frequency distribution and the classification into low, normal and high consuming buildings according to Table 1 is shown.…”

Section: Public Building Stock In Luxembourgsupporting

confidence: 87%

Energy consumption of non-retrofitted institutional building stock in Luxembourg and the potential for a cost-efficient retrofit

Hoos

Merzkirch

Maas

et al. 2016

Energy and Buildings

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The public building stock of a country, consisting of schools, offices, accommodation facilities, single-and multi-family homes, accounts for a high consumption of electrical and heat energy. Therefore, this stock is often object to actions with the goal of lowering this energy usage by increasing the efficiency of those buildings. This is usually done by applying measures to the building envelope like insulation and/or new windows and by using more efficient HVAC technology. But often, in the initial state, the current energy consumption of such a stock is unknown or only known for single buildings. In this case, the calculation of energy and costs savings is either impossible or not exact. This paper shows a way to quantify and categorize the end-energy for heat use of the public building stock in Luxembourg, which consists of 1,744 Mio. m 2 gross area, while the information about this stock was incomplete in the first place.This analysis was done in cooperation with the national administration of public buildings.A certain amount of sample buildings was analyzed and then separated into three groups of low, normal and high end-energy use. The boundaries of these groups were chosen according to literature values, derived from European retrofit projects, which also served as the source for possible costs of renovations. This data was extrapolated to the whole stock. This information serves as a basis for future decisions concerning the retrofit of those buildings and makes a

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Section: Public Building Stock In Luxembourgsupporting

confidence: 87%

Energy consumption of non-retrofitted institutional building stock in Luxembourg and the potential for a cost-efficient retrofit

Hoos

Merzkirch

Maas

et al. 2016

Energy and Buildings

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“…We set U-values (Table 2) consistent with national reference values for existing buildings [78]. U-values for older buildings were adapted based on the recommendations of other studies [84] and a building period was added for constructions erected after 2008, when a new thermal regulation came into force in Luxembourg.…”

Section: Building Envelopementioning

confidence: 99%

A spatio-temporal life cycle assessment framework for building renovation scenarios at the urban scale

Mastrucci

Marvuglia

Benetto

et al. 2020

Renewable and Sustainable Energy Reviews

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Reducing the energy consumption of buildings is a priority for carbon emissions mitigation in urban areas. Building stock energy models have been developed to support decisions of public authorities in renovation strategies. However, the burdens of renovation interventions and their temporal distribution are mostly overlooked, leading to potential overestimation of environmental benefits. Life Cycle Assessment (LCA) provides a holistic estimation of environmental impacts, but further developments are needed to correctly consider spatio-temporal aspects.We propose a spatio-temporal LCA framework to assess renovation scenarios of urban housing stocks, integrating: 1) a geospatial building-by-building stock model, 2) energy demand modelling, 3) product-based LCA, and 4) a scenario generator.Temporal aspects are considered both in the lifecycle inventory and the lifecycle impact assessment phases, by accounting for the evolution of the existing housing stock and applying time-adjusted carbon footprint calculation.We apply the framework for the carbon footprint assessment of housing renovation in Esch-sur-Alzette (Luxembourg). Results show that the renovation stage represents 4% to 16% of the carbon footprint in the residual service life of existing buildings, respectively after conventional or advanced renovations. Under current renovation rates, the carbon footprint reduction would be limited to 3-4% by 2030. Pushing renovation rates to 3%, enables carbon reductions up to 28% by 2030 when combined with advanced renovations. Carbon reductions in the operational stage of buildings are offset by 8-9% due to the impacts of renovation. Using time-adjusted emissions, results in higher weight for the renovation stage and slightly lower benefits for renovation.

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“…Boundary values were identified according to the results of the GIS analysis (floor surface area), normative limits and reference calculation values [56,61], other studies [62,66] and qualified assumptions. A screening test (EE method) was run for each of the 12 building cohorts described in Section 4.1 using the function "morris" in the R package "sensitivity" [63] and setting the number of repetitions r to 10.…”

Section: Screening Of Input Parametersmentioning

confidence: 99%

“…Floor surface area (A n ) distributions were generated based on the building GIS data: lognormal distributions were fitted for each of the building cohorts and then tested against the original sample by means of the Kolmogorov-Smirnoff test [68]. The probability distributions for window-to-façade ratios, indoor set-point temperature and envelope tightness were identified according to relevant literature [19,51,62,66]. U-values were assumed to be normally distributed, similarly to Mauro et al [19], and centred on typical values for Luxembourg depending on the period of construction [61].…”

Section: Characterization Of the Probability Distribution Of Input Pamentioning

confidence: 99%

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Global sensitivity analysis as a support for the generation of simplified building stock energy models

Mastrucci

Pérez‐López

Benetto

et al. 2017

Energy and Buildings

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International audienceBuildings are responsible for 40% of total final energy consumptions in Europe. Numerous bottom-up models were recently developed to support local authorities in assessing the energy consumption of large building stocks and reduction potentials. However, current models rarely consider uncertainty associated to building usage and characteristics within the stock, resulting in potentially biased results. This study presents a generic model simplification approach using uncertainty propagation and stochastic sensitivity analysis to derive fast simplified (surrogate) models to estimate the current building stock energy use for improved urban planning. The methodology includes an engineering-based energy model as input to global sensitivity analysis (GSA) using the elementary effects (EE) screening and Sobol’ method for key parameters identification and regression analysis to derive simplified models for entire building stocks. The application to the housing stock of Esch-sur-Alzette (Luxembourg) showed that the parameters explaining most of the variability in final energy use for heating and domestic hot water are floor area, set-point temperature, external walls U-values, windows and heating system type. Results of the simplified models were validated against measured data and confirmed the validity of the approach for a simple yet robust assessment of the building stock energy use considering uncertainty and variability

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Wie genau sind unsere Energiepässe?

Cited by 7 publications

References 2 publications

Energy consumption of non-retrofitted institutional building stock in Luxembourg and the potential for a cost-efficient retrofit

Energy consumption of non-retrofitted institutional building stock in Luxembourg and the potential for a cost-efficient retrofit

A spatio-temporal life cycle assessment framework for building renovation scenarios at the urban scale

Global sensitivity analysis as a support for the generation of simplified building stock energy models

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