What is the impact of an approach that generates feasible ranges of design parameters on target EUI (kWh/m2/yr) and decision making?

 

PROJECT INFORMATION

Submitted by:  CHEOL-SOO

Firm Name:

ASHRAE Climate Zone: 4A

Building/Space Type:

  • Education

Who performed the simulation analysis?  

  • University

What tools were used for the simulation analysis? 

  • Design Builder + Eplus

What phase of the project was analysis conducted?  

  • Post Occupancy

What are the primary inputs of the analysis?  Energy model, weather data, measured monthly energy use

What are the primary outputs of the analysis?  Feasible ranges of design parameters meeting target EUI, Sensitivity ranking of design parameters, Probability density function of EUI, Inferred energy performance of the existing building after energy retrofit

PROCESS

List the investigations questions that drove your analysis process.

The current building performance simulation workflow takes a forward approach where building simulation tools find simulation output (e.g. EUI) given design parameters. In contrast, the inverse approach presented in this case study can find feasible ranges of design parameters given a target EUI.

How was simulation integrated into the overall design process?

simulation_integrated

How did you set up the simulation analysis and workflow?

1) Developed an energy model of the existing building using DesignBuilder. 2) Conducted one of the Monte Carlo samplings, Latin Hypercube Sampling using MATLAB. The number of simulation samples was 1,000. 3) Because Bayesian calibration demands significant simulation running time, a reduced-order surrogate model, Artificial Neural network (ANN) Model, was constructed based on 1,000 simulation samples generated at Step 2. The ANN model was made using Keras, the Python deep learning library. 4) Ten uncertain parameters were estimated using Bayesian calibration using PyMC3, a Python package for Bayesian statistical modeling. 5) The Sobol method, one of the global sensitivity analysis methods, was conducted to identify significant design parameters. The Sobol is suitable for complex nonlinear models and can consider interactions effects. The Sobol analysis was conducted using SALib, an open-source sensitivity analysis library written in Python. After the Sobol analysis, four significant design parameters were identified including heating COP, cooling COP, window SHGC, and infiltration. 6) Bayesian inference, an inverse uncertainty analysis method, was conducted to obtain plausible ranges of four design parameters which could meet the target EUI (less than 90 kwh/m2/yr) and 30% energy savings target.

How did you visualize the results to the design team? What was successful about the graphics that you used to communicate the data?

We visualized the results using an ‘inverse’ parallel coordinates chart. Please note that the conventional parallel coordinates chart starts from design parameters (from left) to EUI (to right), while our ‘inverse’ parallel coordinates chart starts from the target EUI to feasible ranges of design parameters. When building stakeholders change target EUI on the left, they can see how the input (target EUI) influences feasible ranges of design parameters (output).

Most importantly, what did you learn from the investigation?  How did simulation and its outputs influence the design of the project?

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