Sensitivity and Uncertainty Analysis of Building Parameters integrating energy, daylight and thermal comfort

Abstract

The objective of this thesis was to perform multidisciplinary sensitivity and uncertainty analysis of the building parameters integrating thermal comfort, energy and daylight. The case study of a Danish office building was used for performing the simulations. The study evaluated the suitability of developed methodology for further usage in the early design stages of the building projects. A literature review was conducted to summarize existing methods for sensitivity and uncertainty analysis as well as to identify the most commonly used variables. Eighteen building parameters such as window types, opaque constructions insulation thicknesses, infiltration rate, ventilation rate, setpoint and setback temperatures, internal gains, and surface optical properties were tested. Total energy consumption, number of rooms compiled with Danish Building Regulations thermal comfort requirements and spatial daylight autonomy, were used as the result indicators. After validation of the statistical model of Pearson’s method, Spearman’s rank-order correlation method was chosen for this study. Both the sampling method and sample size were carefully determined for the generation of sampling. Results of the study included the most crucial parameters for the case building optimization. In addition, the results showed the quantification of the uncertainty in according to the results. Filtering the best-performing multi-disciplinary results led to the identification of the best performance input regions according to the input parameters. A significant outcome of the study was the development of DIVA code and Python code in IES VE which could be further readjusted and reused based on the consultant needs.

Popular Abstract

Sensitivity and Uncertainty Analysis of Building Parameter’s Integrating Energy, Daylight and Thermal Comfort.
The building industry is under continuous development and several changes in legislation and building codes concerning energy efficiency have been implemented in recent times. Reducing the building’s energy consumption and optimizing its energy performance are the two key-target areas for both architects and engineers. Prediction of the building’s energy consumption is not only a significant step towards finalizing the design process but is also central for classifying the building based on its energy performance. Therefore, the main objective of this study was the development of a methodology for performing multidisciplinary sensitivity and uncertainty analysis of building parameters affecting energy, daylight and thermal comfort, at the same time.
The proof of the presented concept was based on a case study of an office building located in Copenhagen, Denmark. The developed methodology included the stepwise process for performing statistical analysis, starting from the definition of input / output parameters, through development of automatization codes, to completing analysis of simulated data for the specific building case.
Eighteen building parameters were tested including window types, insulation thicknesses of materials, infiltration rate (i.e. flow rate of outside air into the building), occupancy rate and type (i.e. density of the people and the occupancy schedule), setpoint and setback temperatures (i.e. temperature levels for occupied and non-occupied periods), internal heat gains (i.e. heat emitted by people, lighting and equipment), and surface optical properties (i.e. surface reflectance), among others. Three different results indicators, including total energy consumption, spatial daylight autonomy, and number of rooms in compliance with Danish Building Regulations thermal comfort requirements, were used for the assessment of results. In total five hundred eighty simulations were performed. The results identified the most crucial parameters for the case building optimization and quantified how results could change depending on the uncertainties in the input parameters. Multidisciplinary evaluation (i.e. analysis involving daylight, energy and thermal comfort at the same time) of the results was achieved by gradually adding filters representing different result indicators (spatial daylight autonomy, total energy consumption, and number of rooms fulfilling thermal comfort requirements), which led to the identification of the design inputs parameters’ spans yielding the best performance.
Although, the numerical results of the sensitivity and uncertainty analysis presented in this thesis are valid for the investigated case only, general conclusions can still be drawn. Energy results were found to be mostly influenced by occupancy rate, auxiliary ventilation rate, and inlet air temperature, while thermal comfort results were mostly affected by infiltration rate, cooling setpoint, and the window type. Infiltration rate was found to influence both the energy consumption and the thermal comfort considerably. Insulation thickness of opaque constructions, occupancy type, and internal gains had a very low correlation level with either of them. It should also be noted that higher inlet air temperature and lower auxiliary ventilation rate resulted in lower energy consumption but resulted in worse performance of thermal comfort. Among setpoint and setback temperatures, heating setpoint temperature affected the total energy consumption the most, whereas cooling setpoint temperature influenced the thermal comfort to the highest extent. Total energy was most significantly influenced by the occupancy rate. Hence, for energy simulations the occupancy rate shall be chosen as precisely as possible. In terms of daylight performance, window type had the highest impact on the daylight results. The multidisciplinary evaluation of the results suggested that window types with low g-values performed better in terms of energy and thermal comfort, but worse in terms of daylight.
Overall, the study proves the suitability of the developed methodology for further use in the early design stages of the building projects. The codes developed under this project can be adopted and used to reduce the time needed for the optimization process.

• author: Tan, Tian and Szumanska, Magdalena
• supervisor: Saqib Javed; Iason Bournas; Frederik Blum Winther
• organization: Division of Energy and Building Design; Department of Architecture and Built Environment
• course: AEBM01 20191
• year: 2019
• type: H2 - Master's Degree (Two Years)
• subject: Technology and Engineering
• keywords: Sensitivity analysis, Uncertainty analysis, Multidisciplinary analysis, Building parameters, Energy consumption, Daylight, Thermal comfort, Optimization
• language: English
• id: 8996388
• date added to LUP: 2019-10-09 11:31:19
• date last changed: 2019-10-09 11:31:19