This repository contains: A notebook in R language presenting a methodology, in line with the Data Science process, for clustering electricity consumption patterns at the household level. Sixty files selected from the GoiEner dataset to illustrate the methodology. Links: The notebook hosted here is also available on Google Colab. The GoiEner smart meter data can be found here. A 90-minute video of a detailed presentation of the notebook, with precise explanations for its follow-up, can be viewed on YouTube (Carlos Quesada, ECEMP 2022 Conference). A scientific paper with more information is being prepared and will be linked here when published
The present-day advances in technologies provide the opportunities to pave a road from conventional ...
The intention of this thesis is to discover associations between demography and electric usage patte...
A massive amount of electricity usage may be accessed on an everyday and hourly basis due to the adv...
During the last few decades, citizens around western countries became more and more sensible to ener...
International audienceAll stakeholders in the energy field acknowledge a growing need for higher con...
The availability of increasing amounts of data to electricity utilities through the implementation o...
We propose a cluster analysis approach for organizing, visualizing and understanding households’ ele...
The world community aims toward lowering thedependency of energy from fossil fuels, due to its negat...
Master's thesis in Computer scienceThe need to change the source of electricity generation is appare...
The 2020 International Workshop on Advanced Analytics and Learning on Temporal Data (AALTD 2020),Ghe...
In this paper, we investigate a critical problem in smart meter data mining: computing electricity c...
This paper presents an innovative and scalable methodology named CONDUCTS (CONsumption DUration Curv...
The availability of smart meter data allows defining innovative applications such as demand response...
Electricity consumption is characterized not only by its total amount, but also its temporal course,...
In this article, the Grade Correspondence Analysis (GCA) with posterior clustering and visualization...
The present-day advances in technologies provide the opportunities to pave a road from conventional ...
The intention of this thesis is to discover associations between demography and electric usage patte...
A massive amount of electricity usage may be accessed on an everyday and hourly basis due to the adv...
During the last few decades, citizens around western countries became more and more sensible to ener...
International audienceAll stakeholders in the energy field acknowledge a growing need for higher con...
The availability of increasing amounts of data to electricity utilities through the implementation o...
We propose a cluster analysis approach for organizing, visualizing and understanding households’ ele...
The world community aims toward lowering thedependency of energy from fossil fuels, due to its negat...
Master's thesis in Computer scienceThe need to change the source of electricity generation is appare...
The 2020 International Workshop on Advanced Analytics and Learning on Temporal Data (AALTD 2020),Ghe...
In this paper, we investigate a critical problem in smart meter data mining: computing electricity c...
This paper presents an innovative and scalable methodology named CONDUCTS (CONsumption DUration Curv...
The availability of smart meter data allows defining innovative applications such as demand response...
Electricity consumption is characterized not only by its total amount, but also its temporal course,...
In this article, the Grade Correspondence Analysis (GCA) with posterior clustering and visualization...
The present-day advances in technologies provide the opportunities to pave a road from conventional ...
The intention of this thesis is to discover associations between demography and electric usage patte...
A massive amount of electricity usage may be accessed on an everyday and hourly basis due to the adv...