Uncertainty in developing supervisory demand-side controls in buildings: A framework and guidance

Kim, Sean Hay; Augenbroe, Godfried

doi:10.1016/j.autcon.2013.02.001

Cited by 23 publications

(8 citation statements)

References 24 publications

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“…In design calculations, occupancy is normally accounted for through a fraction profile, which determines their presence in the model and separately determines when they can operate building equipment. This profile is simplified by taking the average behavior of the occupants, and therefore, neglects temporal variations and atypical behavior (Kim and Augenbroe, 2013). Furthermore, occupant effects are related to specification uncertainty through assumed base loads (e.g., lighting and equipment), which make it difficult to determine how occupant profiles or wrong base load assumptions impact the energy performance.…”

Section: Scenario Uncertaintymentioning

confidence: 99%

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A Review of the Regulatory Energy Performance Gap and Its Underlying Causes in Non-domestic Buildings

et al. 2016

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This paper reviews the discrepancy between predicted and measured energy use in non-domestic buildings in a UK context with outlook to global studies. It explains differences between energy performance quantification and classifies this energy performance gap as a difference between compliance and performance modeling with measured energy use. Literary sources are reviewed in order to signify the magnitude between predicted and measured energy use, which is found to deviate by +34% with a SD of 55% based on 62 buildings. It proceeds in describing the underlying causes for the performance gap, existent in all stages of the building life cycle, and identifies the dominant factors to be related to specification uncertainty in modeling, occupant behavior, and poor operational practices having an estimated effect of 20-60, 10-80, and 15-80% on energy use, respectively. Other factors that have a high impact are related to establishing the energy performance target, such as early design decisions, heuristic uncertainty in modeling, and occupant behavior. Finally, action measures and feedback processes in order to reduce the performance gap are discussed, indicating the need for energy in-use legislation, insight into design stage models, accessible energy data, and expansion of research efforts toward building performance in-use in relation to predicted performance. Keywords: energy performance gap, energy use in buildings, predictions, measurements, feedback, postoccupancy evaluation HiGHLiGHTS 1. Classifies the performance gap and analyses its magnitude and underlying causes. 2. The regulatory energy gap is found to deviate by +34% with a SD of 55% based on 62 case study buildings. 3. Specification uncertainty, occupant behavior, and poor practice are dominant underlying causes with an estimated effect of 20-60, 10-80, and 15-80% on energy use, respectively. 4. Action measures to reduce the energy performance gap in contrast to the building life cycle are discussed. 5. There is a need to develop techniques to mitigate the magnitude and underlying causes of the performance gap.

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Section: Scenario Uncertaintymentioning

confidence: 99%

“…Different sources of uncertainty exist in the use of building simulation. de Wit (2001) classified specification, modeling, numerical, and scenario uncertainties, where heuristic uncertainty has been added to describe human-introduced errors as reported by Kim and Augenbroe (2013).…”

Section: Uncertainty In Building Energy Modelingmentioning

confidence: 99%

A Review of the Regulatory Energy Performance Gap and Its Underlying Causes in Non-domestic Buildings

et al. 2016

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“…For the purpose of this case study the following input distributions are described, either as discrete values or as uniform (min/max) ranges: It should be noted that some authors [155,166,167] argue that internal gains should not be included as inputs in a sensitivity analysis since they are a priori scenario dependent, and therefore cannot be regarded in the same manner as other design related parameters. However since the non-occupant component of the effective IHG's are partially influenced by the appliance and building services specification, this component of the IHG's has been included as an input parameter.…”

Section: Mathematically the Building Simulation Model Can Be Represenmentioning

confidence: 99%

An investigation into future performance and overheating risks in Passivhaus dwellings

McLeod

Hopfe

Kwan

2013

Building and Environment

203

126

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In response to UK government policy mandating the construction of 'zero carbon' homes by 2016 there have been significant changes in the way dwellings are being designed and built. Recent years have seen a rapid uptake in the adoption of the German Passivhaus standard as a template for ultra-low energy and zero carbon buildings in the UK. Despite genuine motivations to mitigate climate change and fuel poverty there is a lack of research investigating the long-term performance of Passivhaus buildings in a rapidly changing UK climate. This paper sets out to investigate whether Passivhaus dwellings will be able to provide high standards of thermal comfort in the future or whether they are inherently vulnerable to overheating risks. Scenario modelling using probabilistic data derived from the UKCP09 weather generator (WG) in conjunction with dynamic simulation and global sensitivity analysis techniques are used to assess the future performance of a range of typical Passivhaus dwellings relative to an identical Fabric Energy Efficiency Standard (FEES) compliant dwelling over its notional future lifespan. The emphasis of this study is to understand what impact climate change will pose to overheating risks for Passivhaus dwellings relative to the de facto (i.e. FEES) alternative, and which design factors play a dominant role in contributing to this risk. The results show that optimization of a small number of design inputs, including glazing ratios and external shading devices, can play a significant role in mitigating future overheating risks.

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“…The quantification of uncertainty on the probabilistic distribution for input parameters is the most difficult aspect on UA [13,21]. A number of studies from the high-performance building lab at Geogia Tech University [13,14,32,[42][43][44][45] has been looking at the quantification and understanding of uncertainty analysis for building performance simulation. In this study, the archetype model is representing several vintages and locations, and so different benchmark data are concatenated.…”

Section: Methodology Model Description: Building Conditionsmentioning

confidence: 99%

Sensitivity analysis of cooling demand applied to a large office building

Zeferina

Wood

Edwards

et al. 2021

Energy and Buildings

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Uncertainty in developing supervisory demand-side controls in buildings: A framework and guidance

Cited by 23 publications

References 24 publications

A Review of the Regulatory Energy Performance Gap and Its Underlying Causes in Non-domestic Buildings

A Review of the Regulatory Energy Performance Gap and Its Underlying Causes in Non-domestic Buildings

An investigation into future performance and overheating risks in Passivhaus dwellings

Sensitivity analysis of cooling demand applied to a large office building

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