Net-Zero Navigator

An online platform for the exploration of new building designs in relation to "net-zero ready" targets.

Free. Open-source. Visual. Interactive.

Beta version coming September 2020.

A project by

University of Victoria OPEN Technologies

Why use NZN?

Net-Zero Navigator allows building designers to leverage advancements in machine-learning based "surrogate modelling" to drastically improve their workflow in finding high-performance building designs.

It features a set of surrogate models of archetype buildings which can be explored using intuitive visualizations via a Web interface. Advanced users can access the underlying code in the cloud, to apply the process of surrogate modelling and visualization to their own building models.

The Net-Zero Navigator platform has two main interfaces:

About Surrogate Modelling

Surrogate modelling fits a machine-learning model (we don't call it AI, this isn't SkyNet) over a set of samples from a simulation program, providing a fast 'surrogate' for the underlying detailed model. This paper provides a review of recent developments in this area.

Surrogate modelling provides a way to generate approximate solutions which are easily accurate enough for early-stage design, and incredibly fast to evaluate, without requiring a vast number of samples or a lot of time to fit the model. As an example of this, Figure 1 gives details of a substantial surrogate model, which has 32 parameters spanning geometry, materials, set points, ventilation and building systems. The plot compares the actual EnergyPlus results (x-axis) with the surrogate model result (y-axis), and gives some summary statistics like the R² score of 0.996 and the mean absolute percentage error (MAPE) of 3.65%. The model was fitted on 8,000 EnergyPlus samples (total run time ~ 24 hours on a 16 core machine). The surrogate model returns a result in 32 microseconds which is 7.5 million times faster than an EnergyPlus simulation, also meaning we can generate a million samples in 32 seconds. Our codebase is organised to make it easy to integrate the machine learning methods needed to build surrogate models with the parametric building energy models needed to generate the underlying samples.

Additional Information

Base model: Medium office building

EnergyPlus run time per sample: 4 minutes

EnergyPlus samples for fitting: 8,000

EnergyPlus samples for testing: 2,000

Surrogate training time: ~2 minutes

Surrogate evaluation time per sample: 32µs

Parameters varied (32):

  • Orientation
  • Building Storeys
  • Occupancy
  • Wall Insulation Thickness
  • Roof Insulation Thickness
  • Floor Mass Thickness
  • Wall Mass Thickness
  • Window to Wall Ratio
  • Window U-Value
  • Window SHGC
  • Horizontal Shading
  • Infiltration Rate
  • Lighting Power Density
  • Daylighting Sensors
  • Plug Loads
  • Server Room Loads
  • Min humidity setpoint
  • Max humidity setpoint
  • Cooling setpoint
  • DHW
  • Ventilation Effectiveness Clg
  • Ventilation Effectiveness Htg
  • Sensible Heat Recovery
  • Latent Heat Recovery
  • Fan Power
  • Air-side Conditioning Ratio
  • Heating Plant FUEL:ELEC
  • Heating Fuel NG:OTHER
  • Heating Plant Performance FUEL
  • Heating Plant Performance ELEC
  • Cooling Plant Performance
  • Pumping Power
  • Hydronic Ratio

Tech stack

About us

The Net-Zero Navigator platform is being developed by the Energy in Cities group at the University of Victoria together with OPEN Technologies and the ETA lab at UBC.

Contact us to learn more!