Gym-Eplus

This environment has been only tested in Ubuntu 16.04 OS.

It is highly recommended to use official EnergyPlus Python interface. This project was created before the official one. The project is no-longer maintained.

This environment wraps the EnergyPlus-v-8-6 into the OpenAI gym environment interface.

Installation

EnergyPlus is platform dependent. So this repository does not include the EnergyPlus software. Please download the EnergyPlus-v-8-6 from https://energyplus.net/downloads, extract it, and place it to the directory eplus_env/envs/EnergyPlus-8-6-0.

The environment depends on BCVTB-1.6.0 (https://simulationresearch.lbl.gov/bcvtb). There is no need to re-install it since this repository already had it. But BCVTB-1.6.0 is compiled with Java-1.8. Make sure you have Java-1.8 on your OS.

$ virtualenv virt_env --python=python3
$ source virt_env/bin/activate
$ pip install gym
$ pip install -e Gym-Eplus/

Usage

Overview

Gym-Eplus is implemented based on EnergyPlus ExternalInterface function. The EnergyPlus model should be configured based on the guidelines here (https://simulationresearch.lbl.gov/bcvtb/releases/latest/doc/manual/tit-EnePluCon.xhtml).

Create a new environment

A new environment should be registered in eplus_env/init.py file.

EnergyPlus simulation output

EnergyPlus logs its own output. The output will be stored under the directory $pwd/Eplus-env-{environment name}-res{x}/Eplus-env-sub_run{x}/output. The "sub_run" directory is the directory for each episode (each EnergyPlus simulation run period) that the environment runs.

Public attributes

• start_year: EnergyPlus simulation start year, int.
• start_mon: EnergyPlus simulation start month, int.
• start_day: EnergyPlus simulation start day of the month, int.
• start_weekday: EnergyPlus simulation start weekday, int. 0 is Monday.
• env_name: The environment name.

Example

import gym;
import eplus_env;

env = gym.make('Eplus-demo-v1');
curSimTime, ob, isTerminal = env.reset(); # Reset the env (creat the EnergyPlus subprocess)
while not isTerminal:
    action = [20, 20];
    curSimTime, ob, isTerminal = env.step(action);
curSimTime, ob, isTerminal = env.reset(); # Start a new episode (creat a new EnergyPlus subprocess)
while not isTerminal:
    action = [20, 20];
    curSimTime, ob, isTerminal = env.step(action);
env.end_env(); # Safe termination of the environment after use. 

Related Publications

1. Zhiang Zhang and Khee Poh Lam. 2018. Practical implementation and evaluation of deep reinforcement learning control for a radiant heating system. In Proceedings of the 5th Conference on Systems for Built Environments (BuildSys '18). ACM, New York, NY, USA, 148-157. DOI: https://doi.org/10.1145/3276774.3276775