Clustering-Framework-for-Resedential-Demand-Profiles

With the spirit of reproducible research, this repository contains all the codes required to produce the results in the manuscript:

Jain, M., AlSkaif, T. and Dev, S.(2020). A Clustering Framework for Residential Electric Demand Profiles. In: International Conference on Smart Energy Systems and Technologies (SEST).

Scripts

• read_data.py: reads the raw data from the CSV file that contains the electric load consumption data of multiple households for the year 2018-19.
• main.py: main program. Currently, it does the following tasks:
  • loads the data : read_data.py
  • pre-processesing : utils.utils.preProcessing_clustering
  • dimensionality reduction : utils.dimReduction.dim_reduction_PCA, and dim_reduction_FA
  • plot elbow heuristics : utils.dimReduction.elbowHeuristic_PCA, and elbowHeuristic_FA
  • optimal no. of clusters (Spectral Clustering) : utils.spectral.optimalKspectral
  • perform Spectral Clustering : utils.spectral.spectralClustering_KM_KNN_Euc
  • optimal no. of clusters (K-Means Clustering) : utils.kmeans.optimalK
  • perform K-Means Clustering : utils.kmeans.kmeans
  • perform objective validation : utils.spectral.validate_spectral_clusters, and objectiveValidation.validate_clusters
  • plot results for subjective validation : utils.utils.plotClusters
• utils directory contains the helpful functions which are used in the main.py

Note:

The dataset used in this project can not be disclosed due to external reasons. However, one may feel free to use/modify the code as per the requirement.