Open AccessIdentification of Characteristic Heat Load Profiles of Different Usage Units in Non-Residential Buildings
Author(s) -
Christoph Matschi,
Guillaume Vogt,
Isabell Nemeth
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/290/1/012102
Subject(s) - simultaneity , scale (ratio) , heat load , identification (biology) , environmental science , energy consumption , reliability (semiconductor) , peak load , conformity , computer science , transient (computer programming) , energy (signal processing) , statistics , mathematics , automotive engineering , engineering , geography , power (physics) , physics , botany , cartography , classical mechanics , quantum mechanics , biology , law , political science , electrical engineering , thermodynamics , operating system
For energy-efficient design of district heating networks and their components, knowledge about load profiles and the peak simultaneity are of crucial importance. Heating load profiles are needed in high temporal and spatial resolution as well as information about their composition. Due to high computational and temporal effort for transient calculations of a whole district heating network a less complex method is needed. For this reason different areas of use of non-residential buildings are analyzed separately to identify their characteristical variations and main influences on their individual load profiles to finally superpose their load profile in one overall building/district heat load profile. In a first step similar use areas in four buildings are calculated transiently and the deviation of the results were analyzed. Additionally, the building age and the associated structural-physical parameters are varied to get results for different building age classes. In a second step the profiles are superposed up to the district scale by using the area as scale factor. The gained district heat load profile is compared to time series of the observed consumption in order to assess the reliability of the method. The first results show promising conformity of modelled and measured energy demand. So the method will be applied to several buildings with varying structural-physical parameters and geometries.