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Abstract

Traditional building energy consumption calculation methods are characterised by rough approaches providing approximate figures with high and unknown levels of uncertainty. Lack of reliable energy resources and increasing concerns about climate change call for improved predictive tools.

A new approach for the prediction of building energy consumption is presented. The approach quantifies the uncertainty of building energy consumption by means of stochastic differential equations. The approach is applied to a general heat balance for an arbitrary number of loads and zones in a building to determine the dynamic thermal response under random conditions. Two test cases are presented.

The approach is found to work well, although computation time may be rather high. The results indicate that the impact of a stochastic description compared with a deterministic description may be modest for the dynamic thermal behaviour of buildings. However, for air flow and energy consumption it is found to be much more significant due to less “damping”.

Probabilistic methods establish a new approach to the prediction of building energy consumption, enabling designers to include stochastic parameters like inhabitant behaviour, operation, and maintenance to predict the performance of the systems and the level of certainty for fulfilling design requirements under random conditions.
Original languageEnglish
JournalEnergy and Buildings
Volume55
Pages (from-to)127–140
Number of pages14
ISSN0378-7788
DOIs
Publication statusPublished - 2012

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Energy utilization
Energy resources
Climate change
Differential equations
Damping
Uncertainty
Air
Hot Temperature

Keywords

  • Stochastic Differential Equations
  • Uncertainty Quantification
  • Building Thermal Behaviour
  • Occupants’ Behaviour
  • Net-Zero Energy Buildings
  • Building Simulation Tool

Cite this

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title = "Quantification of Uncertainty in Predicting Building Energy Consumption: a stochastic approach",
abstract = "Traditional building energy consumption calculation methods are characterised by rough approaches providing approximate figures with high and unknown levels of uncertainty. Lack of reliable energy resources and increasing concerns about climate change call for improved predictive tools.A new approach for the prediction of building energy consumption is presented. The approach quantifies the uncertainty of building energy consumption by means of stochastic differential equations. The approach is applied to a general heat balance for an arbitrary number of loads and zones in a building to determine the dynamic thermal response under random conditions. Two test cases are presented.The approach is found to work well, although computation time may be rather high. The results indicate that the impact of a stochastic description compared with a deterministic description may be modest for the dynamic thermal behaviour of buildings. However, for air flow and energy consumption it is found to be much more significant due to less “damping”.Probabilistic methods establish a new approach to the prediction of building energy consumption, enabling designers to include stochastic parameters like inhabitant behaviour, operation, and maintenance to predict the performance of the systems and the level of certainty for fulfilling design requirements under random conditions.",
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author = "Henrik Brohus and Christian Frier and Per Heiselberg and F. Haghighat",
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Quantification of Uncertainty in Predicting Building Energy Consumption : a stochastic approach. / Brohus, Henrik; Frier, Christian; Heiselberg, Per; Haghighat, F.

In: Energy and Buildings, Vol. 55, 2012, p. 127–140.

Research output: Contribution to journalJournal articleResearchpeer-review

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AU - Brohus, Henrik

AU - Frier, Christian

AU - Heiselberg, Per

AU - Haghighat, F.

PY - 2012

Y1 - 2012

N2 - Traditional building energy consumption calculation methods are characterised by rough approaches providing approximate figures with high and unknown levels of uncertainty. Lack of reliable energy resources and increasing concerns about climate change call for improved predictive tools.A new approach for the prediction of building energy consumption is presented. The approach quantifies the uncertainty of building energy consumption by means of stochastic differential equations. The approach is applied to a general heat balance for an arbitrary number of loads and zones in a building to determine the dynamic thermal response under random conditions. Two test cases are presented.The approach is found to work well, although computation time may be rather high. The results indicate that the impact of a stochastic description compared with a deterministic description may be modest for the dynamic thermal behaviour of buildings. However, for air flow and energy consumption it is found to be much more significant due to less “damping”.Probabilistic methods establish a new approach to the prediction of building energy consumption, enabling designers to include stochastic parameters like inhabitant behaviour, operation, and maintenance to predict the performance of the systems and the level of certainty for fulfilling design requirements under random conditions.

AB - Traditional building energy consumption calculation methods are characterised by rough approaches providing approximate figures with high and unknown levels of uncertainty. Lack of reliable energy resources and increasing concerns about climate change call for improved predictive tools.A new approach for the prediction of building energy consumption is presented. The approach quantifies the uncertainty of building energy consumption by means of stochastic differential equations. The approach is applied to a general heat balance for an arbitrary number of loads and zones in a building to determine the dynamic thermal response under random conditions. Two test cases are presented.The approach is found to work well, although computation time may be rather high. The results indicate that the impact of a stochastic description compared with a deterministic description may be modest for the dynamic thermal behaviour of buildings. However, for air flow and energy consumption it is found to be much more significant due to less “damping”.Probabilistic methods establish a new approach to the prediction of building energy consumption, enabling designers to include stochastic parameters like inhabitant behaviour, operation, and maintenance to predict the performance of the systems and the level of certainty for fulfilling design requirements under random conditions.

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