A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Comparison of four building archetype characterization methods in urban building energy modeling (UBEM): A residential case study in Kuwait City
HighlightsAccuracy problems for archetype-based Urban Building Energy Models are discussed.A Bayesian calibration method for urban archetype parameters is introduced.An energy model is generated as a case study for a residential district in Kuwait.Three common archetype definition methods are compared with calibration method.Calibration reduced EUI distribution errors a 14–35% against typical models.
AbstractSignificant research effort has gone into developing bottom-up, physics based, urban building energy modeling (UBEM) tools, which allow evaluating district-wide energy demand and supply strategies. In order to reduce the simulation inputs required in UBEM, groups of buildings are typically classified into representative “archetypes”. This approach necessarily simplifies the real diversity of occupant behaviors and construction variations, reducing in turn the variety of predicted building energy demands. To further understand the limitations of archetype-based modeling, this paper evaluates four characterization methods of increasing complexity, by comparing simulation results to measured Energy Use Intensity (EUI) distributions of 336 residential buildings in a district in Kuwait City. The first two methods follow a typical deterministic approach, organizing buildings into one and four archetypes respectively, while the third method introduces probabilistic uncertainty modeling for occupant related parameters. The fourth method introduces a new “Bayesian” calibration technique for archetype parameters, and applies it to occupant parameters in method three. In a first step, frequency calibrated distributions for occupancy, lighting power density, plug load density and cooling set points are generated through parametric modeling from a test set consisting of about half of the 336 buildings. The resulting joint distribution of parameters is then sampled using Latin Hyper Cube (LHC) for the probabilistic simulation of the remaining urban model. The comparison of simulated EUI distributions with measured data suggests that the proposed probabilistic Bayesian calibration leads to significantly closer predictions, with a maximum error in the 10th and 90th percentiles of 15% against 55%, 60% and 29% for the other three methods.
Comparison of four building archetype characterization methods in urban building energy modeling (UBEM): A residential case study in Kuwait City
HighlightsAccuracy problems for archetype-based Urban Building Energy Models are discussed.A Bayesian calibration method for urban archetype parameters is introduced.An energy model is generated as a case study for a residential district in Kuwait.Three common archetype definition methods are compared with calibration method.Calibration reduced EUI distribution errors a 14–35% against typical models.
AbstractSignificant research effort has gone into developing bottom-up, physics based, urban building energy modeling (UBEM) tools, which allow evaluating district-wide energy demand and supply strategies. In order to reduce the simulation inputs required in UBEM, groups of buildings are typically classified into representative “archetypes”. This approach necessarily simplifies the real diversity of occupant behaviors and construction variations, reducing in turn the variety of predicted building energy demands. To further understand the limitations of archetype-based modeling, this paper evaluates four characterization methods of increasing complexity, by comparing simulation results to measured Energy Use Intensity (EUI) distributions of 336 residential buildings in a district in Kuwait City. The first two methods follow a typical deterministic approach, organizing buildings into one and four archetypes respectively, while the third method introduces probabilistic uncertainty modeling for occupant related parameters. The fourth method introduces a new “Bayesian” calibration technique for archetype parameters, and applies it to occupant parameters in method three. In a first step, frequency calibrated distributions for occupancy, lighting power density, plug load density and cooling set points are generated through parametric modeling from a test set consisting of about half of the 336 buildings. The resulting joint distribution of parameters is then sampled using Latin Hyper Cube (LHC) for the probabilistic simulation of the remaining urban model. The comparison of simulated EUI distributions with measured data suggests that the proposed probabilistic Bayesian calibration leads to significantly closer predictions, with a maximum error in the 10th and 90th percentiles of 15% against 55%, 60% and 29% for the other three methods.
Comparison of four building archetype characterization methods in urban building energy modeling (UBEM): A residential case study in Kuwait City
Cerezo, Carlos (author) / Sokol, Julia (author) / AlKhaled, Saud (author) / Reinhart, Christoph (author) / Al-Mumin, Adil (author) / Hajiah, Ali (author)
Energy and Buildings ; 154 ; 321-334
2017-08-14
14 pages
Article (Journal)
Electronic Resource
English
Building archetype characterization for mass-housing energy efficiency through a UBEM approach
| BASE | 2023
Building archetype calibration for effective urban building energy modeling
| BASE | 2017