Changing schedule based on Artificial Neural Network using values from energy plus

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Hi @mechyai,

thank you so much for replying. I spend last two weeks trying to understand EMS python API and it is still super hard to understand it.

I found that using plugin method 'EnergyPlusPlugin' might be easier for a beginner like me. Is it possible to do all the process like sensing, reading ANN models and actuating using the python plugin mode?

thanks..

@prp92 I am not sure as I have only used used EnergyPlus as a "library" with the Python EMS API, not Python as a plugin in EnergyPlus. I made this workflow decision a while ago so I don't remember all my motivations, but I think I remember choosing running E+ as a library in Python because of the ability to keep my namespace (for instance NN model params) in Python for the longevity of the simulation and Python script. If I remember correctly, Python plugin's namespace only persist within the individual call, you would have to read/write your NN model params at each call.

@prp92, also I believe if using Python as a plugin you have to bottle your ENTIRE Python workflow into the single plugin script. When using EnergyPlus as a library in Python, I can have a normal programming workflow, say with a bunch of PyTorch NN stuff, in Python, then only pass EnergyPlus specific things via a callback function at specific calling points. If I take an OOP approach in Python, I can pass a whole slew of variables and objects (.ie NN models, etc.) into EnergyPlus, preserving namespace inside and outside E+ for the entire simulation run period.

@prp92 using E+ as a library in Python with the EMS Python API should make sense once you understand how to pass callback functions and calling points. The callback function is the single piece of Python code that gets passed to E+ at runtime, the calling point is specifically when this happens.

@prp92 see @jmarrec great notebook at "Run EnergyPlus in API mode" to see examples of how to use E+ as a library/API