Google Smart Buildings Control

This repository accompanies Goldfeder, J., Sipple, J., Real-World Data and Calibrated Simulation Suite for Offline Training of Reinforcement Learning Agents to Optimize Energy and Emission in Office Buildings, currently under review at Neurips 2024, and builds off of Goldfeder, J., Sipple, J., (2023). A Lightweight Calibrated Simulation Enabling Efficient Offline Learning for Optimal Control of Real Buildings, BuildSys ’23, November 15–16, 2023, Istanbul, Turkey

Getting Started

The best place to jump in is the Soft Actor Critic Demo notebook, available in notebooks/SAC_Demo.ipynb

This will walk you through:

1. Creating an RL (gym compatible) envronment

2. Visualizing the env

3. Training an agent using the Tensorflow Agents Library

Before you run this notebook, make sure to go through the setup instructions below to ensure the notebook runs successfully.

Setup

Follow these steps to setup locally before you run the notebooks/SAC_Demo.ipynb notebook. Note: this will only work on linux, as some libraries are not supported by other operating systems.

1. Clone the repository

2. Ensure you have protoc and ffmpeg installed, as well as python >=3.10.12 and <3.12. You can install these running sudo apt install -y protobuf-compiler and sudo apt install -y ffmpeg

3. Create a virtual environment by running python -m venv .venv. Then, install poetry with pip install poetry

4. Install the dependencies by running poetry install

5. Build the .proto files at smart_control/protointo python files by running cd smart_control/proto && protoc --python_out=. smart_control_building.proto smart_control_normalization.proto smart_control_reward.proto && cd ../..

6. Modify the value of VIDEO_PATH_ROOT at smart_control/simulator/constants.py. This is the path where simulation videos will be stored

7. Now in the notebooks/SAC_Demo.ipynb notebook, modify the values of data_path, metrics_path, output_data_path and root_dir. In particular, data_path should point to the sim_config.gin file at smart_control/configs/sim_config.gin

8. Now you are ready to run the notebooks/SAC_Demo.ipynb notebook

Real World Data

In addition to our calibrated simulator, we released 6 years of data on 3 buildings, for further calibration, and to use, in conjunction with the simulator, for training and evaluating RL models. The dataset is part of Tensorflow Datasets