global-model-for-home-battery-controller

Overview

This model was trained using a reinforcement learning (RL) method to control home batteries. We trained this global RL model on 90 diverse Belgian households with the objective of reducing daily energy cost. We proposed a transfer learning framework to finetune this pretrained global model on limited data from a target household that you would like to use this model for. To read more about our transfer learning framework, data, and RL-based method we encourage you to follow our research published in BuildSys 2024.

• Multi-source Transfer Learning in Reinforcement Learning-based Home Battery Controller

Model description

The model is a neural network which gets 6 arguments as an input and outputs Q-values for all possible actions (in this case 5 actions). The input arguments to the model are described as below. All inputs have to be normalized between -1 and 1.

The output of the model is a vector 5 elements which indicates Q-values for all possible actions. the best action is an action with the highest Q-value.

o = (value for discharge, value for half discharge, value for idle, value for half charge, value for charge)

How to use

To load the trained model, you can use the following code:

global_model = nn.Sequential(nn.Linear(6,256),
                                       nn.ReLU(),
                                       nn.Linear(256,128),
                                       nn.ReLU(),
                                       nn.Linear(128,5))
global_model.load_state_dict(torch.load('global_model_q_network.pt'))

Support, License and Copyright

The algorithm was developed by AI4E research Group at UGhent. If you need any support, don’t hesitate to reach out to us at [email protected]

Citation

@article{madahi2024multi,
  title={Multi-source Transfer Learning in Reinforcement Learning-based Home Battery Controller},
  author={Seyed Soroush Karimi Madahi, Toon Van Puyvelde, Gargya Gokhale, Bert Claessens, and
  Chris Develder},
  booktitle={Proceedings of the 11th ACM International Conference on Systems for Energy-Efficient
  Buildings, Cities, and Transportation},
  year={2024}
  }