\## Model Simplification on Energy and Comfort Simulation Analysis for Residential Building Design in Hot and Arid Climate
Authors: Sara Elhadad, Chro Hama Radha, István Kistelegdi, Bálint Baranyai, and János Gyergyák
Journal: Energies
Year: 2020
Volume: 13
Article number: 1876
DOI: 10.3390/en13081876
Published: 12 April 2020
\### Keywords
Model simplifications, thermal and visual comfort, energy performance, IDA ICE, residential building.
\### Research focus
The study evaluates how simplifying a residential building energy model affects the accuracy of energy performance, thermal comfort, indoor air quality, visual comfort, modeling time, and simulation time.
The research focuses on a multifamily residential building in a hot and arid climate. The reference building is located in New Minia, Egypt. The simulation was carried out using IDA Indoor Climate and Energy.
\### Aim of the study
The main aim of the paper is to assess the impact of model simplifications through different scenarios, considering simulation time, modeling time, and the accuracy level of the derived results in both energy demand and thermal comfort in residential houses.
The study compares a detailed reference model with simplified models where the number of thermal zones is reduced step by step.
\### Case study building
The reference building is a residential building in New Minia, Egypt. It was built in 2005 and consists of nine apartments.
The ground floor includes one apartment and consists of a lounge, dining room, bathroom, and kitchen, with a total floor area of 180 m².
Each repeated floor includes two identical apartments, with a net floor area of 220 m². Every apartment includes a reception, master bedroom, two children rooms, bathroom, and kitchen.
\### Simulation methodology
The detailed base model treats each building space as a separate thermal zone. Four simplification scenarios were then tested by reducing the number of thermal zones.
The scenarios were:
BS
Base model. Each building space is modeled as a single zone.
Number of thermal zones: 64
S1
Floor by floor, all identically oriented spaces with the same function are merged into one zone with the same operation schedules, use, and other similar characteristics.
Number of thermal zones: 14
S2
The same oriented spaces with the same use for all of the four floors are combined into one thermal zone.
Number of thermal zones: 8
S3
All rooms on the same floor are merged into one thermal zone.
Number of thermal zones: 4
S4
The entire building is modeled as one single thermal zone.
Number of thermal zones: 1
\### Energy performance results
IDA ICE was used to simulate energy consumption and indoor comfort performance of the studied building for the base model and all simplification scenarios.
In the base model, cooling demand accounts for 67% of the total energy consumption, while heating demand accounts for 18%. Lighting, facility, equipment, tenant, and domestic hot water account for 15% of the total energy consumption.
Compared with the base model, the change in total energy consumption was:
S1: +5.8%
S2: +9.5%
S3: +7.1%
S4: +4.0%
Although S4 showed the smallest change in total energy consumption, it was not the most accurate scenario because the heating and cooling deviations balanced each other out.
\### Modeling time and calculation time
The detailed base model required 215 minutes of modeling time and 86 minutes of calculation time.
The simplified scenarios reduced both modeling and calculation time.
BS
Modeling time: 215 minutes
Calculation time: 86 minutes
S1
Modeling time: 45 minutes
Calculation time: 32 minutes
S2
Modeling time: 35 minutes
Calculation time: 14 minutes
S3
Modeling time: 22 minutes
Calculation time: 23 minutes
S4
Modeling time: 11 minutes
Calculation time: 5 minutes
The scenarios saved 79% to 95% of modeling time and 63% to 94% of calculation duration compared with the base model.
\### Thermal comfort assessment
Predicted Mean Vote, or PMV, was used as one of the main indices to assess thermal comfort in an occupied zone.
For the whole building, the annual hours of PMV Category B were:
BS: 7781 hours
S1: 6642 hours
S2: 7787 hours
S3: 6906 hours
S4: 7717 hours
The study found that, in general, a consistent calculated thermal comfort sensation was observed in each model, but larger simplifications produced greater deviations in specific zones.
\### Indoor air quality assessment
Carbon dioxide concentration was applied as an indicator of indoor air quality.
The study estimated the number of annual hours when the CO₂ concentration level was above 1000 ppm.
For the whole building, the annual hours with CO₂ concentration above 1000 ppm were:
BS: 2248 hours
S1: 2130 hours
S2: 2086 hours
S3: 2058 hours
S4: 2116 hours
The results showed that the distribution of CO₂ concentration had great inhomogeneity in the different sized thermal zones.
\### Daylight factor assessment
Daylighting was assessed as a visual comfort parameter.
The study focused on the Daylight Factor, or DF, which represents the illuminance performance of spaces under mixed sky circumstances.
The required value of DF for Minia city was calculated as 2.1%.
In the base model, 21.3% of the floor area was adequately daylighted.
Compared with the base model, the simplified scenarios produced different daylight performance values. The largest difference occurred in S4, where the single-zone simplification strongly affected daylight distribution.
\### Optimal simplification scenario
To determine the optimal scenario, the study considered