Sensitivity analysis of an EnergyPlus model identifies how uncertainty in an output can be allocated to uncertainty in the input parameters of a process model. Sensitivity analysis is useful for identifying which parameters need more attention during model design and which input parameters influence simulation results the most. Influence of the construction materials and number of people, so-called occupancy, on the room temperature and incoming air ventilation temperature of a house can be obtained by sensitivity analysis. Performing a crude sensitivity analysis that shows the impact of the uncertainty with respect to changes of individual values for these parameters identifies the most significant individual contributors to variability in results.
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Simulation software
Simulation software is based on the process of modeling a real phenomenon with a set of mathematical formulas. It is, essentially, a program that allows the user to observe an operation through simulation without actually performing that operation. Simulation software is used widely to design equipment so that the final product will be as close to design specs as possible without expensive in process modification.
Energy-Plus software
EnergyPlus is a whole building energy simulation program that engineers, architects, and researchers use to model both energy consumption — for heating, cooling, ventilation, lighting, and process and plug loads — and water use in buildings. Its development is funded by the U.S. Department of Energy Building Technologies Office. EnergyPlus is a console-based program that reads input and writes output to text files. Several comprehensive graphical interfaces for EnergyPlus are also available.
Main features of Energy Plus software
BCVTB interface software
The Building Controls Virtual Test Bed (BCVTB) is a software environment that allows users to couple different simulation programs for co-simulation, and to couple simulation programs with actual hardware. For example, the BCVTB allows to simulate a building in EnergyPlus and the HVAC and control system in Modelica, while exchanging data between the software as they simulate.
Programs that are linked to the BCVTB
Buildings and model description
A modern house which is located in Upper Austria is considered for the sensitivity analysis of construction materials. The building to be simulated is a modern two-story house with a cellar. The volume of the building is approximately 761 m^3. The house is located at Hagenberg in Upper Austria. The walls are made of 25 cm thick bricks without insulation except for the cellar. The windows and glassdoors are standard double glazed with an intermediate layer of air.
We have used EnergyPlus for simulating the house model.For building our simulation framework we have used the software tool Building Controls Virtual Test Bed (BCVTB). We can define for example a heating control of an EnergyPlus building model with the control logic implemented in MATLAB.
An elementary school is considered for the sensitivity analysis of occupancy.
Schedules were selected to model typical variation in school daily operations, although the authors acknowledge that schools can also operate on twelve-month calendars or with extended night school hours. Variability for energy model inputs is defined by assigning different sets of 24-hour diversity factors for weekdays, weekends, holidays, etc. to the maximum load of each end-use (occupants, lighting, equipment, etc.).
Experiments of sensitivity analysis of the material properties
The experiments were performed in the following way:
Influence of the material properties in the house were tested. First a framework using BCVTB, EnergyPlus and MATLAB have been created so that the values can be sent to EnergyPlus online to overwrite the outside temperature. Secondly, a batch file is set up to do the following:
- change the EnergyPlus input file with a different value of the material property
- call BCVTB to run the co-simulation between EnergyPlus and MATLAB
- run a script to calculate the MAE of the real and simulated indoor temperature
- move to the next value of the range (if not finished) and go to (1).
Following this procedure mean absolute error (MAE) can be calculated for all values of all ranges from bellow Table. It assumed that the material properties are independent of each other. Therefore, each material property will be varied at a time, leaving the others constant at the default values (from EnergyPlus)and measured the mean absolute error (MAE) between the real indoor and the simulated temperatures. The range of material properties was given by an expert. The specific room under study has a lot of fenestration, so it is not so surprising to see that the influence of the solar transmittance of the windows is the most influential of all material properties analyzed. The next influential factor is the conductivity of the bricks, followed by the thermal absorptance and the specific heat of the bricks.
The most influential properties of the materials analyzed (bricks and glasses) are the solar transmittance of the glasses and the conductivity of the bricks.
Experiments of sensitivity analysis of the occupancy
Uncertainties regarding behavior of building occupants limit the ability of energy models to accurately predict actual building performance. The first step in crude uncertainty analysis is the assessment of plausible ranges of values for model parameters. In this case, it was first necessary to identify the salient model parameters characterizing the building occupant.The parameters that had the most impact on total energy use are listed according to importance for both warm and cold climates.
Important parameters in a warm climate zone:
- Equipment load(High)
- Ventilation rate(High)
- Equipment load(Low)
- Infiltration rate(High)
- Ventilation rate(Low)
Important parameters in a cold climate zone:
- Infiltration rate(Low)
- Ventilation rate(Low)
- Occupant schedule(High)
- Equipment load(Low)
- Equipment load(High)
In order to insure that the correct numbers of occupants are present at any given hour, it is necessary to multiply all diversity factors by all occupant loads for each space and sum the total occupant count for the building Analysis shows that the elementary school model is sensitive to occupant inputs to approximately the same degree in both cold and warm climates (results for all-high and allow inputs vary by approximately +65% / -40% from the all-medium case in both climates). Peak demand is somewhat more sensitive to occupant inputs in cold climates (+25% / -30%) than warm (+/- 20%).