This Kmeans model was trained on daily consumption data from 44 smart meters located in the city of Terni and provided by ASM Terni, the local utility operator. During the training phase the model divided the data into clusters of different shapes. The goal was to find clusters that would be ideal for demand response (DR) programs. Consult the clusterCenters.png file for a visual representation of each of the 12 clusters.

The instructions below are meant to guide you through deploying the model on a server with MLFlow. They also contain an example of how to use the model after deployment.

Instructions:

1. Set up a new virtual environment with: python3 -m venv .venv
2. Activate virtual environment: ./.venv/bin/activate
3. Install requirements: pip3 install -r requirements.txt
4. Serve the model. It will be listening on port 4000: mlflow models serve -m kmeansEuclidean12 --no-conda -p 4000
5. Use curl to query the model. It should receive a JSON file containing an array of 1x24 vectors (which correspond to time series of l2-normalised electricity consumption) and respond by assigning each time series to a cluster

Example Query:

curl http://127.0.0.1:4000/invocations -H 'Content-Type: application/json' -d '{"inputs": [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1354716821903498, 0.3935744688045296, 0.46222493205905163, 0.42077999460368204, 0.40343804111067944, 0.03816305794295266, 0.09221543418398288, 0.09583805528477521, 0.30921402493063427, 0.22870934114079539, 0.14969316007927091, 0.14372121539230384, 0.11081275142363932, 0.1159059414879889, 0.11011333447772179, 0.11230125415409159, 0.11267786327799414]]}'

Response: [0]