# A Clustering Framework for Residential Electric Demand Profiles

arXiv: https://arxiv.org/abs/2105.08537 · HTML: https://mlsanity.com/paper/2105.08537/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 17 May 2021.

## Builds on (most cited first, 2 of 2)

- [Detecting Rainfall Onset Using Sky Images](https://mlsanity.com/paper/1610.06667/) (2016)
- [Forecasting Precipitable Water Vapor Using LSTMs](https://mlsanity.com/paper/2006.15165/) (2020)

## Built on by (most cited first, 3 of 3)

- [Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images](https://mlsanity.com/paper/2211.01885/) (2022)
- [Graph-based Local Climate Classification in Iran](https://mlsanity.com/paper/2110.09209/) (2021)
- [A semantic web approach to uplift decentralized household energy data](https://mlsanity.com/paper/2208.10265/) (2022)

## Abstract (arXiv)

The availability of residential electric demand profiles data, enabled by the large-scale deployment of smart metering infrastructure, has made it possible to perform more accurate analysis of electricity consumption patterns. This paper analyses the electric demand profiles of individual households located in the city Amsterdam, the Netherlands. A comprehensive clustering framework is defined to classify households based on their electricity consumption pattern. This framework consists of two main steps, namely a dimensionality reduction step of input electricity consumption data, followed by an unsupervised clustering algorithm of the reduced subspace. While any algorithm, which has been used in the literature for the aforementioned clustering task, can be used for the corresponding step, the more important question is to deduce which particular combination of algorithms is the best for a given dataset and a clustering task. This question is addressed in this paper by proposing a novel objective validation strategy, whose recommendations are then cross-verified by performing subjective validation.