Building model calibration using energy and environmental data

Mohammad Royapoor,Tony Roskilly

doi:10.1016/j.enbuild.2015.02.050

Mohammad Royapoor, Tony Roskilly

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https://doi.org/10.1016/j.enbuild.2015.02.050

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Journal: Energy and Buildings	Publication Date: Feb 28, 2015
Citations: 209	License type: cc-by

Affiliation: Newcastle University

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A large number of randomly interacting variables combine to dictate the energy performance of a building. Building energy simulation models attempt to capture these perturbations as accurately as possible. The prediction accuracy of building energy models can now be better examined given the widespread availability of environmental and energy monitoring equipment and reduced data storage costs. In this paper a set of two calibrated environmental sensors together with a weather station are deployed in a 5-storey office building to examine the accuracy of an EnergyPlus virtual building model. Using American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Guide 14 indices the model was calibrated to achieve Mean Bias Error (MBE) values within ±5% and Cumulative Variation of Root Mean Square Error (CV(RMSE)) values below 10%. The calibrated EnergyPlus model was able to predict annual hourly space air temperatures with an accuracy of ±1.5°C for 99.5% and an accuracy of ±1°C for 93.2% of the time.

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The origins of building energy modelling can be traced back as early as 1920s with the development of response factor method for transient heat flow calculations [1]
ASHRAE Guide 14 considers a building model calibrated if hourly Mean Bias Error (MBE) values fall within ±10% and hourly CV(RMSE) values fall below 30%
Electricity carries the biggest CV(RMSE) in among the other two calibrated streams of data since electrical consumption is more closely related to occupant activity that deviates from the deterministic occupancy templates used in Energyplus model

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Introduction

The origins of building energy modelling can be traced back as early as 1920s with the development of response factor method for transient heat flow calculations [1]. The availability of computers in the 60s heralded a new dawn when, especially from early 80s the HVAC companies developed energy models for heating and cooling load calculations [2,3]. This trend accelerated as the 70s oil crises raised building energy standards, leading to greater energy efficiency and modelling methods that continue to this day [4]. The creation, maintenance and updating of virtual building models increasingly require greater levels of accuracy to enable more meaningful studies.

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