Two-to-one discrepancy between measured and predicted performance of a ‘low-energy’ office building: insights from a reconciliation based on the DOE-2 model

Norford, L.K.; Socolow, Robert H.; Hsieh, E.S.; Spadaro, G.V.

doi:10.1016/0378-7788(94)90005-1

Cited by 168 publications

(83 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This resulted in the generated lighting schedules which were too simplistic and lacked verification against measured data [18]. Also, this lead to large discrepancies between simulated and measured lighting energy use [19][20]. Furthermore, the annual variation of actual lighting energy was not captured.…”

Section: Introductionmentioning

confidence: 99%

Data analysis and stochastic modeling of lighting energy use in large office buildings in China

Zhou

Yan

Hong

et al. 2015

Energy and Buildings

110

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Data analysis and stochastic modeling of lighting energy use in large office buildings in China

Zhou

Yan

Hong

et al. 2015

Energy and Buildings

110

View full text Add to dashboard Cite

show abstract

“…Norford et al (1994) found that uncalibrated energy predictions and actual energy consumption could differ by a factor of two. This would correspond to a coefficient of variation (standard deviation of prediction error divided by mean of actual value) of 100%.…”

Section: Practical Evaluation Of Accuracymentioning

confidence: 99%

Recalibration of the Complaint Prediction Model

Federspiel

Martin

Yan³

2004

HVAC&R Research

View full text Add to dashboard Cite

“…However, calibrating models so the simulations accurately represent the real building can improved predictions to within 6% (Norford et al, 1994). Altering the heating controls using an occupant comfort measure (predicted mean vote), Andersen et al (2007) found that a simulated building could vary in energy use by 324% from a low consumption scenario to a high consumption scenario.…”

Section: Building Performance Deviationsmentioning

confidence: 99%

“…Using suitable calibration techniques simulations can more accurately represent the aggregate behaviour of a real building, improving predictions to within 6% (Norford et al, 1994). Using 5 different bespoke models of occupancy, Menezes et al (2012) managed to improve their predictive accuracy from a 70% under prediction to a 3% under prediction.…”

Section: Introductionmentioning

confidence: 99%

On the multi-agent stochastic simulation of occupants in buildings

Chapman

Siebers

Robinson

2018

Journal of Building Performance Simulation

View full text Add to dashboard Cite

One of the principle causes for deviations between predicted and simulated performance of buildings relates to the stochastic nature of their occupants: their presence, activities whilst present, activity dependent behaviours and the consequent implications for their perceived comfort. A growing research community is active in the development and validation of stochastic models addressing these issues; and considerable progress has been made. Specifically models in the areas of presence, activities while present, shading devices, window openings and lighting usage.One key outstanding challenge relates to the integration of these prototype models with building simulation in a coherent and generalizable way; meaning that emerging models can be integrated with a range of building simulation software. This thesis describes our proof of concept platform that integrates stochastic occupancy models within a multi agent simulation platform, which communicates directly with building simulation software. The tool is called Nottingham Multi-Agent Stochastic Simulation (No-MASS).No-MASS is tested with a building performance simulation solver to demonstrate the effectiveness of the integrated stochastic models on a residential building and a non-residential building. To account for diversity between occupants No-MASS makes use of archetypical behaviours within the stochastic models of windows, shades and activities. Thus providing designers with means to evaluate the performance of their designs in response to the range of expected behaviours and to evaluate the robustness of their design solutions; which is not possible using current simplistic deterministic representations. No-MASS employs agent machine learning techniques that allow them to learn how to respond to the processes taking place within a building and agents can choose a strategy without the need for context specific rules.Employing these complementary techniques to support the comprehensive simulation of occupants presence and behaviour, integrated within a single platform that can readily interface with a range of building (and urban) energy simulation programs is the key contribution to knowledge from this thesis. Nevertheless, there is significant scope to extend this work to further reduce the performance gap between simulated and real world buildings.

show abstract

Two-to-one discrepancy between measured and predicted performance of a ‘low-energy’ office building: insights from a reconciliation based on the DOE-2 model

Cited by 168 publications

References 1 publication

Data analysis and stochastic modeling of lighting energy use in large office buildings in China

Data analysis and stochastic modeling of lighting energy use in large office buildings in China

Recalibration of the Complaint Prediction Model

On the multi-agent stochastic simulation of occupants in buildings

Contact Info

Product

Resources

About