Sensitivity analysis of medical centers energy consumption with EnergyPlus
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2017Subject/s
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Abstract
Sensitivity analysis plays a vital role in building energy analysis. It is done to clarify which are the crucial variables affecting building thermal performance and to evaluate quantitatively those effects. It can be conducted both through energy simulation models and real case observations. The present paper is devoted to the description of the sensitivity analysis techniques that are able to extract the most effective parameters on the energy consumption of a commercial building, particularly medical centers. Energy consumption in medical centers, generally, depends on several parameters ranging from technical and geometrical aspects to climatic conditions. This paper is focused on the application of sensitivity analysis in term of energy consumption in medical centers through a benchmark simulation model which is developed by National Renewable Energy Laboratory (NREL) in order to classify the most effective parameters on energy consumption of a large hospitals. © 2017 IEEE.
Sensitivity analysis plays a vital role in building energy analysis. It is done to clarify which are the crucial variables affecting building thermal performance and to evaluate quantitatively those effects. It can be conducted both through energy simulation models and real case observations. The present paper is devoted to the description of the sensitivity analysis techniques that are able to extract the most effective parameters on the energy consumption of a commercial building, particularly medical centers. Energy consumption in medical centers, generally, depends on several parameters ranging from technical and geometrical aspects to climatic conditions. This paper is focused on the application of sensitivity analysis in term of energy consumption in medical centers through a benchmark simulation model which is developed by National Renewable Energy Laboratory (NREL) in order to classify the most effective parameters on energy consumption of a large hospitals. © 2017 IEEE.





