The building performance gap: Are modellers literate?

Imam, Salah; Coley, David; Walker, Ian

doi:10.1177/0143624416684641

Cited by 118 publications

(61 citation statements)

References 24 publications

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“…The inputs used in the LENI calculations, Eq. 1, by the external consultants in these feasibility studies were based on assumptions and best guess estimates, this is congruent with other performance gap researchers who found that consultant's thermal modelling input assumptions were highly variable (Imam et al, 2017). The operational hours can easily be replaced by actual data collected in practice.…”

Section: Comparison Across the Three Study Buildingssupporting

confidence: 81%

“…Respectively, these are broadly: the regulatory requirements, for example, compliance with Part L2B Building Regulations in the United Kingdom; the static performance gap is a snapshot in time at a specific point, for example, when the building is first occupied and compared with designer's assumptions or models; the dynamic performance gap assumes a fluid and flexible changing of the space organically throughout time, for example, different types of building user or tenants. Imam et al (2017) recently investigated the internal "mental models"-the psychological representation of a real or imagined system (Craik, 1943)-of 108 thermal modellers and found a wide variability in their approaches to model inputs and ranking input parameters. Their findings include the observation that a quarter of modellers participating were making worse judgements than a random response and even the most experienced modellers, including external consultants, contributed great diversity (including some of the worst performance) to the overall results (Imam et al, 2017).…”

Section: Performance Gapmentioning

confidence: 99%

“…Imam et al (2017) recently investigated the internal "mental models"-the psychological representation of a real or imagined system (Craik, 1943)-of 108 thermal modellers and found a wide variability in their approaches to model inputs and ranking input parameters. Their findings include the observation that a quarter of modellers participating were making worse judgements than a random response and even the most experienced modellers, including external consultants, contributed great diversity (including some of the worst performance) to the overall results (Imam et al, 2017). This paper measures and assesses how retrofit upgrades to luminaires and controls in corridor areas perform in practice.…”

Section: Performance Gapmentioning

confidence: 99%

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Determining the Difference between Predicted vs. Actual Lighting Use in Higher Education Corridors

2017

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The performance gap is a measure of the difference between design assumptions and actual in field data. Estimating terms, specifically the operational hours of use, when making such design assumptions in order to predict the impact of lighting upgrades can potentially result in either an overestimate or underestimate of the savings to be made. In this paper, the background of performance gap measurement is outlined and field measurements are gathered and applied retrospectively to lighting upgrades in corridors. The lighting upgrade projects in three university buildings and their assumptions are explained in relation to the operational hours proposed using the industry "Energy assessment and reporting method." We then describe a simple and relatively inexpensive means of taking in field measurements using small unobtrusive environmental loggers to record the lighting use and occupancy. This method, which can be implemented prior to upgrade works or energy efficiency retrofits, reveals substantially different patterns of annual electricity consumption, and carbon dioxide equivalent emissions from those assumed by a priori estimates. Patterns of use emerge that include additional hours of use by cleaners, out of hours working, and weekend working not anticipated in original estimates. These results also suggest a valuable distinction between lighting on hours and occupancy hours not captured in the current "Energy assessment and reporting method." The results find the differences between predicted vs. actual data are considerably different, with lights on hours ranging from −67% lower to 25% and 138% higher when compared with predicted operational hours. We conclude that the estimates the industry uses in calculating energy efficiency upgrades should be accompanied by clear and adequate information about occupancy use. In our study, the consequence of reporting energy savings using assumptions and estimates in calculations resulted in a substantial overall underestimate of the savings achieved in practice.

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Section: Comparison Across the Three Study Buildingssupporting

confidence: 81%

Section: Performance Gapmentioning

confidence: 99%

Section: Performance Gapmentioning

confidence: 99%

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Determining the Difference between Predicted vs. Actual Lighting Use in Higher Education Corridors

2017

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“…However, optimistic assumptions are often made in the design phase and semi-stationary calculation methodologies are still commonly employed [7]. Further, the gap between simulated and measured performance is a general issue [8] and the benefits of "green" design practices should be critically evaluated [9,10],…”

Section: Introductionmentioning

confidence: 99%

Parametric Performance Analysis and Energy Model Calibration Workflow Integration—A Scalable Approach for Buildings

Manfren

Nastasi

2020

Energies

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High efficiency paradigms and rigorous normative standards for new and existing buildings are fundamental components of sustainability and energy transitions strategies today. However, optimistic assumptions and simplifications are often considered in the design phase and, even when detailed simulation tools are used, the validation of simulation results remains an issue. Further, empirical evidences indicate that the gap between predicted and measured performance can be quite large owing to different types of errors made in the building life cycle phases. Consequently, the discrepancy between a priori performance assessment and a posteriori measured performance can hinder the development and diffusion of energy efficiency practices, especially considering the investment risk. The approach proposed in the research is rooted on the integration of parametric simulation techniques, adopted in the design phase, and inverse modelling techniques applied in Measurement and Verification (M&V) practice, i.e., model calibration, in the operation phase. The research focuses on the analysis of these technical aspects for a Passive House case study, showing an efficient and transparent way to link design and operation performance analysis, reducing effort in modelling and monitoring. The approach can be used to detect and highlight the impact of critical assumptions in the design phase as well as to guarantee the robustness of energy performance management in the operational phase, providing parametric performance boundaries to ease monitoring process and identification of insights in a simple, robust and scalable way.Energies 2020, 13, 621 2 of 14 by assessing transparently the impact of human and technical factors [11]. With respect to human factors in particular, the effects of occupants' behaviour [12] and of their comfort preferences [13] on building performance are generally overlooked in the design phase. This paper aims to present a way to integrate modelling methodologies used across building life cycle phases, from design to operation, in a simple and scalable way. A residential building has been chosen as a case study. The building is a detached single family certified Passive House built in Italy, in the Province of Forlì-Cesena, in the Emilia Romagna region. It has been monitored for three years, learning incrementally insights by comparing the original design phase simulation data with actual measured data. Background and MotivationThe research work answers to the necessity of linking parametric performance analysis and model calibration from a conceptual and practical point of view. Building performance parametric and probabilistic analysis is an essential tool today to ensure robustness of performance and the importance of the Design of Experiments (DOE) is becoming clear [14-17], both for new and retrofitted buildings [18,19]. For example, accounting for the robustness of performance estimates with respect to economic indicators (e.g., in cost-optimal analysis [20-22]) is important because uncertainty can aff...

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“…', 1 particularly the negative impression of building physics modelling introduced to the public domain.…”

mentioning

confidence: 99%