This repository contains the code to run simulations for the "An Efficient and Accurate Personalized Peer-to-Peer Machine Learning Approach for Home Thermal Modeling" paper, submitted in ACM e-Energy 2023 conference. The repository includes the implementation of the temporal and spacial abstraction suggested in the paper as well as the Thermal P3 algorithm on a simulated environment. The \mobile folder contains the implementation of the P3 algorithm on Android devices.

We use the Ecobee dataset available in https://bbd.labworks.org/ds/bbd/ecobee to train personalized thermal models. The dataset should be downloaded to the /data folder. Run the ecobee.py script for before the first run of the algorithm to generate the preprocessed data.

We have implemented the P3 algorithm using two ML engines. First, using Tensorflow for running on the Linux server (used for performance evaluations). Second, using Numpy only (N3) to support ML training on android devices. To configure the ML Engine, update the following line in src/conf.py ML_ENGINE = "Tensorflow" # "N3" or "Tensorflow" NB: Android implementation does not support Tensorflow.

• To select a given cluster for training our of the 6 clusters generated during the preprocessing phase (), set the id of the cluster in one for the main files (mainP3.py, mainFL.py, mainCL.py) as follows:

  cluster_id = 0
  

  To ignore clustering use:

  cluster_id = None
  

• To build the random topology of the P2P network, we use the graph density parameter rho to estimate the number of neighbors for each nodes. In our experiments, we use the following:

  • rho=0.01: to have around 1% neighbors for each peer.
  • rho=0.05: to have around 10% neighbor for each peer.
  • rho=0.3: to have around 30% neighbor for each peer.

  To set a value for rho in mainP3.py use:

  cluster_id = None
  rho = 0.05
  limit = 10
  topology = metadata_graph(homes_metadata, dist="euclidian", rho=rho, limit=limit)
  

The main algorithm parameters are the following:

To reproduce the experiments of model performance in the paper use the following command:

• To run Centralized Learning (CL)

python mainCL.py

• To run Federated Learning (FL)

python mainFL.py

• To run P2P Learning (P3 algorithm)

python mainP3.py

You can configure every file using the args variable.

topology = random_graph(models, rho=0.95)  # 0, 0.4, 0.7, 0.95, 0.99

To perform energy analysis of P3 on the Linux server (Ubuntu 20.04), we developed two methods of energy readings:

• Evaluating the whole program by running the run.sh script.
• Evaluating a given method of the algorithm using python decorators.

NB: you need to disable virtualization from the bios as we shield the program to one physical core.

We have used the following packages: powerstat, cset-shield, cpupower.

To measure the energy consumption of the whole program run the following:

./run.sh -c 0 -p avg -r 1 -d 2 -e "python mainP3.py"

Run ./run.sh -h to get a list of the available options and what are used for.

To measure the energy consumption of a given method, use the @measure_energy decorator.

For example to evaluation the energy consumption of the local learning step, add the following:

@measure_energy
def local_training(self, device='cpu', inference=True):
	log('event', 'Starting local training ...')
	...

End.