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Electricity consumption of medical plug loads in hospital laboratories: Identification, evaluation, prediction and verification

handle: 11420/7090
Evaluating the energy demand of heating, ventilation and air conditioning (HVAC) as well as lighting equipment through standardized calculation methods has become a self-evident measure for planning and optimizing non-residential buildings in recent years. For the case of hospitals however, information about the magnitude of electricity consumption caused by the vast amounts of medical equipment is still lacking. Not least due to the strongly growing use of such electrically operated devices in an increasingly complex environment, electricity has become the major energy cost driver in modern hospitals. Against this background this paper presents a model approach based on over 33,500 h of measurements within a modern University Medical Center of Hamburg/Germany to assess the time-dependent course as well as the weekly sum of the demand for electrical energy due to medical laboratory plug loads. This assessment method allows for approximating the electricity demand of the installed equipment as a supplement to the established prediction methods for the electricity demand of HVAC, lighting, etc. It was found that only a few plug load groups contribute the greater part of the total electrical energy demand. Cumulative load predictions for a full building were possible with an error of less than 6%.
- Hamburg University of Technology Germany
Laboratory equipment, Internal loads, Medical equipment, Plug loads, Hospital energy consumption
Laboratory equipment, Internal loads, Medical equipment, Plug loads, Hospital energy consumption
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).31 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
