A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Prediction of plug loads in office buildings: Simplified and probabilistic methods
https://orcid.org/0000-0001-5727-2646
https://orcid.org/0000-0003-4764-261X
Highlights Relationship between occupants’ presence patterns and plug loads studied. Simplified and stochastic models of plug loads proposed and evaluated. The simplified model provided fairly reasonable predictions of annual plug loads. The stochastic model performed better in terms of plug loads’ peak and distribution.
Abstract To predict buildings’ energy use, multiple systems and processes must be considered. Next to factors such as building fabric and construction, indoor environmental control systems, and weather conditions, the energy demand attributable to buildings’ internal heat gains resulting from inhabitants, lighting, and equipment usage also needs to be addressed. Given this background, the present contribution focuses on plug loads in office buildings associated mainly with computers and peripherals. Using long-term observational data obtained from a continuously monitored office building in Vienna, we specifically explore the relationship between inhabitants’ presence, installed power for equipment, and the resulting electrical energy use. The findings facilitate the formulation of both simplified and probabilistic office plug loads predictions methods. Thereby, the model evaluation results suggest that the non-stochastic model provides fairly reasonable predictions of annual energy use associated with plug loads. However, the stochastic plug load model – together with a stochastic occupancy model – outperforms the simplified model in predicting the plug loads peak and distribution.
Prediction of plug loads in office buildings: Simplified and probabilistic methods
https://orcid.org/0000-0001-5727-2646
https://orcid.org/0000-0003-4764-261X
Highlights Relationship between occupants’ presence patterns and plug loads studied. Simplified and stochastic models of plug loads proposed and evaluated. The simplified model provided fairly reasonable predictions of annual plug loads. The stochastic model performed better in terms of plug loads’ peak and distribution.
Abstract To predict buildings’ energy use, multiple systems and processes must be considered. Next to factors such as building fabric and construction, indoor environmental control systems, and weather conditions, the energy demand attributable to buildings’ internal heat gains resulting from inhabitants, lighting, and equipment usage also needs to be addressed. Given this background, the present contribution focuses on plug loads in office buildings associated mainly with computers and peripherals. Using long-term observational data obtained from a continuously monitored office building in Vienna, we specifically explore the relationship between inhabitants’ presence, installed power for equipment, and the resulting electrical energy use. The findings facilitate the formulation of both simplified and probabilistic office plug loads predictions methods. Thereby, the model evaluation results suggest that the non-stochastic model provides fairly reasonable predictions of annual energy use associated with plug loads. However, the stochastic plug load model – together with a stochastic occupancy model – outperforms the simplified model in predicting the plug loads peak and distribution.
Prediction of plug loads in office buildings: Simplified and probabilistic methods
https://orcid.org/0000-0001-5727-2646
https://orcid.org/0000-0003-4764-261X
Highlights Relationship between occupants’ presence patterns and plug loads studied. Simplified and stochastic models of plug loads proposed and evaluated. The simplified model provided fairly reasonable predictions of annual plug loads. The stochastic model performed better in terms of plug loads’ peak and distribution.
Abstract To predict buildings’ energy use, multiple systems and processes must be considered. Next to factors such as building fabric and construction, indoor environmental control systems, and weather conditions, the energy demand attributable to buildings’ internal heat gains resulting from inhabitants, lighting, and equipment usage also needs to be addressed. Given this background, the present contribution focuses on plug loads in office buildings associated mainly with computers and peripherals. Using long-term observational data obtained from a continuously monitored office building in Vienna, we specifically explore the relationship between inhabitants’ presence, installed power for equipment, and the resulting electrical energy use. The findings facilitate the formulation of both simplified and probabilistic office plug loads predictions methods. Thereby, the model evaluation results suggest that the non-stochastic model provides fairly reasonable predictions of annual energy use associated with plug loads. However, the stochastic plug load model – together with a stochastic occupancy model – outperforms the simplified model in predicting the plug loads peak and distribution.
Prediction of plug loads in office buildings: Simplified and probabilistic methods
Mahdavi, Ardeshir (author) / Tahmasebi, Farhang (author) / Kayalar, Mine (author)
Energy and Buildings ; 129 ; 322-329
2016-08-05
8 pages
Article (Journal)
Electronic Resource
English
Prediction of plug loads in office buildings: Simplified and probabilistic methods
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