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A. Mahdavi, F. Tahmasebi, M. Kayalar:
"Prediction of plug loads in office buildings: Simplified and probabilistic methods";
Energy and Buildings, 129 (2016), S. 322 - 329.

(no german version - see english version) To predict buildings´ energy use, multiple systems and processes must be considered. Next to factorssuch 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, andequipment usage also needs to be addressed. Given this background, the present contribution focuseson plug loads in office buildings associated mainly with computers and peripherals. Using long-termobservational data obtained from a continuously monitored office building in Vienna, we specificallyexplore the relationship between inhabitants´ presence, installed power for equipment, and the resultingelectrical energy use. The findings facilitate the formulation of both simplified and probabilistic officeplug loads predictions methods. Thereby, the model evaluation results suggest that the non-stochasticmodel 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 simplifiedmodel in predicting the plug loads peak and distribution.

To predict buildings´ energy use, multiple systems and processes must be considered. Next to factorssuch 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, andequipment usage also needs to be addressed. Given this background, the present contribution focuseson plug loads in office buildings associated mainly with computers and peripherals. Using long-termobservational data obtained from a continuously monitored office building in Vienna, we specificallyexplore the relationship between inhabitants´ presence, installed power for equipment, and the resultingelectrical energy use. The findings facilitate the formulation of both simplified and probabilistic officeplug loads predictions methods. Thereby, the model evaluation results suggest that the non-stochasticmodel 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 simplifiedmodel in predicting the plug loads peak and distribution.

Occupancy, Plug loads, Equipment, Electrical Energy use, stochastic model, non-stochastic method

http://www.sciencedirect.com/science/article/pii/S0378778816307071