A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A model-based decision support tool for building portfolios under uncertainty
https://orcid.org/0000-0003-4118-3061
AbstractDespite abundant energy use data, few facilities managers have a good benchmark for tracking energy performance in commercial buildings. Building energy self-benchmarking is an effective means of comparing performance to expectations. This paper presents an improved theory for a decision support tool that can self-benchmark building energy performance, identify energy faults, and quantify their severity. Detailed building energy simulation modeling of a big-box retail store with open source software is accessible and economical to industry for generating performance benchmarks. Methods of parametric sampling and uncertainty analysis are enhanced with detailed parameter uncertainty characterization. Uncertainty and sensitivity analysis are used to adjust risk tolerance thresholds for each unique monitored end-use. A dynamic cost function allows utility theory to compute expected costs covering multiple criteria. Improved theory for decision support tool is tested on ten faulted model scenarios placed in three climate zones. Finally, we demonstrate fault response prioritization.
HighlightsModel uncertainty analysis provides context to support energy management decisions.Global sensitivity analysis is used to determine end-use risk-tolerance thresholds.Batch simulation with model uncertainty generates context for comparison.Ordinary users can alter a cost matrix and apply multi-criteria utility theory.Synthetic fault model testing shows tool capable of prioritizing faults by severity.
A model-based decision support tool for building portfolios under uncertainty
https://orcid.org/0000-0003-4118-3061
AbstractDespite abundant energy use data, few facilities managers have a good benchmark for tracking energy performance in commercial buildings. Building energy self-benchmarking is an effective means of comparing performance to expectations. This paper presents an improved theory for a decision support tool that can self-benchmark building energy performance, identify energy faults, and quantify their severity. Detailed building energy simulation modeling of a big-box retail store with open source software is accessible and economical to industry for generating performance benchmarks. Methods of parametric sampling and uncertainty analysis are enhanced with detailed parameter uncertainty characterization. Uncertainty and sensitivity analysis are used to adjust risk tolerance thresholds for each unique monitored end-use. A dynamic cost function allows utility theory to compute expected costs covering multiple criteria. Improved theory for decision support tool is tested on ten faulted model scenarios placed in three climate zones. Finally, we demonstrate fault response prioritization.
HighlightsModel uncertainty analysis provides context to support energy management decisions.Global sensitivity analysis is used to determine end-use risk-tolerance thresholds.Batch simulation with model uncertainty generates context for comparison.Ordinary users can alter a cost matrix and apply multi-criteria utility theory.Synthetic fault model testing shows tool capable of prioritizing faults by severity.
A model-based decision support tool for building portfolios under uncertainty
https://orcid.org/0000-0003-4118-3061
AbstractDespite abundant energy use data, few facilities managers have a good benchmark for tracking energy performance in commercial buildings. Building energy self-benchmarking is an effective means of comparing performance to expectations. This paper presents an improved theory for a decision support tool that can self-benchmark building energy performance, identify energy faults, and quantify their severity. Detailed building energy simulation modeling of a big-box retail store with open source software is accessible and economical to industry for generating performance benchmarks. Methods of parametric sampling and uncertainty analysis are enhanced with detailed parameter uncertainty characterization. Uncertainty and sensitivity analysis are used to adjust risk tolerance thresholds for each unique monitored end-use. A dynamic cost function allows utility theory to compute expected costs covering multiple criteria. Improved theory for decision support tool is tested on ten faulted model scenarios placed in three climate zones. Finally, we demonstrate fault response prioritization.
HighlightsModel uncertainty analysis provides context to support energy management decisions.Global sensitivity analysis is used to determine end-use risk-tolerance thresholds.Batch simulation with model uncertainty generates context for comparison.Ordinary users can alter a cost matrix and apply multi-criteria utility theory.Synthetic fault model testing shows tool capable of prioritizing faults by severity.
A model-based decision support tool for building portfolios under uncertainty
Boxer, Eric (author) / Henze, Gregor P. (author) / Hirsch, Adam I. (author)
Automation in Construction ; 78 ; 34-50
2017-01-18
17 pages
Article (Journal)
Electronic Resource
English
AMY , Actual Meteorological Year (weather data) , BMS , building management system , CDD , cooling degree days , CDF , cumulative distribution function , ESTool , Energy Signal Tool , EUI , energy use intensity , FAR , false alarm ratio , FDD , fault detection and diagnostics , FM , facilities manager(s) , HDD , heating degree days , HVAC , heating ventilation and air conditioning , LHS , Latin Hypercube sampling , NGAS , natural gas , NREL , National Renewable Energy Laboratory , OAT , one at a time (local sensitivity analysis method of parameter screening) , PDF , probability distribution function , REFR , refrigeration , SA , sensitivity analysis , SR , signal priority ratio , UA , uncertainty analysis , WBE , whole building energy
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