Digital Twin technology for decarbonising any built environment.
Integrated analysis tools for the design & retrofit of buildings.
Create a sustainable masterplan for a city, community or campus.
Optimise building performance at an individual level or across a portfolio.
Analyse the feasibility of energy network decarbonisation strategies
A customisable range of operational dashboards, portfolio management and community engagement tools.
Exceptional room & zone loads analysis for building & HVAC design.
Predict building energy consumption, CO2 emissions, peak demands, energy cost & renewable production.
All Consultancy Projects
In our latest Python in the VE series article we look at working with ApacheHVAC.
We create series of functions to help access VE ApacheHVAC component data. We use these functions to navigate the ApacheHVAC object airside and waterside hierarchies and GET ApacheHVAC component results. We also demonstrate adding this result data to a Pandas dataframe, manipulating the dataframe and exporting the dataframe to a variety of output formats.
As we have seen a VE data model can be complex, by building reusable functions we can automate the work and the scripts we create stay readable. By creating functions that reflect ApacheHVAC airside, waterside, loops etc. we work in terms that users will find familiar. We utilise nested DICTs as introduced in previous articles. Pandas is a powerful tool; once in a dataframe data analysis, plotting and export is straightforward.
Check out the full guide here
View sample HVAC data here: www.iesve.com/support/faq/zip/working-with-hvac.zip
You can view other articles in my VE Python series HERE.