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Gnu-RL

This repo stores the code used for [Link to BuildSys Paper], along with a demonstration in an EnergyPlus model.

Install Related Packages

The following two packages were used. Install following their documentation.

  • Gym-Eplus
    • This package is an OpenGym AI wrapper for EnergyPlus.
    • The demonstration in this repo uses EnergyPlus version 8.6, but the Gym-plus package is applicable to any EnergyPlus version 8.x.
  • mpc.torch
    • This package is a fast and differentiable model predictive control solver for PyTorch.

Install other packages by,

$ pip install -r requirements.txt

(To be Confirmed)

Set up Simulation Environments

  • Read the documentation of Gym-Eplus on setting up simulation environments.
  • Place the model and weather files in the eplus_env folder under the corresponding location in the Gym-Eplus folder.
  • Register the environments following this table. A __init__.py for registeration is included. But, check that it matches your own file placement.
Environment Name Model File (*.idf) Configuration File (*.cfg) Weather File (*.epw)
5Zone-sim_TMY2-v0 5Zone_Default.idf variables_Default.cfg pittsburgh_TMY2.epw
5Zone-control_TMY3-v0 5Zone_Control.idf variables_Control.cfg pittsburgh_TMY3.epw
5Zone-sim_TMY3-v0 5Zone_Default.idf variables_Default.cfg pittsburgh_TMY3.epw

Overview

An example is provided here, which provides more details.

For Offline Pretraining,

$ python Imit_EP.py

For Online Learning,

$ python PPO_MPC_EP.py

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gnu-rl's Issues

How do you train your model

Hi:
Control the central air conditioning temperature constant, how to use the cooling water temperature, frozen water temperature, outdoor temperature, water flow rate and other data training model

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