Calibrating whole building energy models: An evidence-based methodology

Raftery, Paul; Keane, Marcus; O’Donnell, James

doi:10.1016/j.enbuild.2011.05.020

Cited by 228 publications

(120 citation statements)

References 16 publications

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“…Maile et al (2012) developed such a method using a formal representation of building objects to capture relationships between predicted and measured energy use on a detailed level and were able to identify and solve operational issues. Raftery et al (2011) argue that these calibration methods improve the quality of future models by identifying common mistaken assumptions and by developing best-practice modeling approaches. Reliability and accuracy of calibrated models depend on the quality of measured data used to create the model as well as the accuracy and limitation of the tools used to simulate the building and its systems (Coakley et al, 2012).…”

Section: Operational Managementmentioning

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: Operational Managementmentioning

confidence: 99%

“…In addition, there are often many constraints to going back to the building in order to make it more efficient, such as cost, reputational concerns, and liability (Robertson and Mumovic, 2013). A review of and methodology for calibration techniques are presented by Coakley et al (2014) and Raftery et al (2011), respectively.…”

Section: Operational Managementmentioning

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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“…This method is investigated in [24][25][26] and involves a repetitive alteration of performance parameters, until the desired minimum discrepancy is found between measured and modelled results. Though claims are made in literature that this technique is superior to Bayesian calibration, the process is much more time-consuming.…”

Section: Model Calibrationmentioning

confidence: 99%

Variations in the U-Value Measurement of a Whole Dwelling Using Infrared Thermography under Controlled Conditions

et al. 2018

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U-values of building elements are often determined using point measurements, where infrared imagery may be used to identify a suitable location for these measurements. Current methods identify that surface areas exhibiting a homogeneous temperature-away from regions of thermal bridging-can be used to obtain U-values. In doing so, however, the resulting U-value is assumed to represent that entire building element, contrary to the information given by the initial infrared inspection. This can be problematic when applying these measured U-values to models for predicting energy performance. Three techniques have been used to measure the U-values of external building elements of a full-scale replica of a pre-1920s U.K. home under controlled conditions: point measurements, using heat flux meters, and two variations of infrared thermography at high and low resolutions. U-values determined from each technique were used to calibrate a model of that building and predictions of the heat transfer coefficient, annual energy consumption, and fuel cost were made. Point measurements and low-resolution infrared thermography were found to represent a relatively small proportion of the overall U-value distribution. By propagating the variation of U-values found using high-resolution thermography, the predicted heat transfer coefficient (HTC) was found to vary between 183 W/K to 235 W/K (±12%). This also led to subsequent variations in the predictions for annual energy consumption for heating (between 4923 kWh and 5481 kWh, ±11%); and in the predicted cost of that energy consumption (between £227 and £281, ±24%). This variation is indicative of the sensitivity of energy simulations to sensor placement when carrying out point measurements for U-values.

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“…Even if the reliability of Building Energy Simulation (BES) techniques is always improving, important studies showed that the gap between effective and predicted energy requirement was still too significant (from 0.25 to 2.5 in [42]) to be considered a rigorous research tool. The Vertical Campus model and calibration was elaborated following [43,44]. The initial model (before the iterative revision process) was built as described in Section 2.4.…”

Section: Whole Building Model Calibration and Validation Proceduresmentioning

confidence: 99%

A Building Energy Efficiency Optimization Method by Evaluating the Effective Thermal Zones Occupancy

2012

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Abstract:Building energy efficiency is strongly linked to the operations and control systems, together with the integrated performance of passive and active systems. In new high quality buildings in particular, where these two latter aspects have been already implemented at the design stage, users' perspective, obtained through post-occupancy assessment, has to be considered to reduce whole energy requirement during service life. This research presents an innovative and low-cost methodology to reduce buildings' energy requirements through post-occupancy assessment and optimization of energy operations using effective users' attitudes and requirements as feedback. As a meaningful example, the proposed method is applied to a multipurpose building located in New York City, NY, USA, where real occupancy conditions are assessed. The effectiveness of the method is tested through dynamic simulations using a numerical model of the case study, calibrated through real monitoring data collected on the building. Results show that, for the chosen case study, the method provides optimized building energy operations which allow a reduction of primary energy requirements for HVAC, lighting, room-electricity, and auxiliary supply by about 21%. This paper shows that the proposed strategy represents an effective way to reduce buildings' energy waste, in particular in those complex and high-efficiency buildings that are not performing as well as expected during the concept-design-commissioning stage, in particular due to the lack of feedback after the building handover. OPEN ACCESSEnergies 2012, 5 5258 Keywords: building energy efficiency; dynamic simulation and validation; post-occupancy evaluation; equipment operations and management

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Calibrating whole building energy models: An evidence-based methodology

Cited by 228 publications

References 16 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

Variations in the U-Value Measurement of a Whole Dwelling Using Infrared Thermography under Controlled Conditions

A Building Energy Efficiency Optimization Method by Evaluating the Effective Thermal Zones Occupancy

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