This repo stores the code used for [Link to BuildSys Paper], along with a demonstration in an EnergyPlus model.
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)
- 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 |
An example is provided here, which provides more details.
For Offline Pretraining,
$ python Imit_EP.py
For Online Learning,
$ python PPO_MPC_EP.py