Analysis and Clustering of Residential Customers Energy Behavioral Demand Using Smart Meter Data

By Stephen A. Haben, Colin Singleton, Peter Grindrod. IEEE Transactions on Smart Grid, 2016

340 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/b9e8fe9aae6920663af2dd06a915f4a639f2d389

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Chongqing Kang
2. Qixin Chen
3. Yi Wang
4. Qing Xia
5. David Flynn
6. Benoit Couraud
7. S. Elizondo-González
8. Sonam Norbu

In Transportation

1. Chongqing Kang
2. Yi Wang
3. Qixin Chen

Citations in the corpus

• Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review (2020, methodology): “Nomenclature AIS Artificial Immune System AI Artificial Intelligence AMI Advanced Metering Infrastructure ANN Artificial Neural Networks BRP Balance Responsible Party CNN Convolutional Neural Network DER Distributed Energy Resources DR Demand-Side Response DSO Distribution System Operator EV Electri”
• Clustering of Electricity Consumption Behavior Dynamics Toward Big Data Applications (2016, background): “For another example, four time periods named overnight period, breakfast period, day-time period, and evening period, can be approximately chosen through some statistics [41].”
• Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges (2018, methodology): “Four key time periods, which described different peak demand behaviors, coinciding with common intervals of the day were identified in [98], and then a finite mixture-model-based clustering was used to discover ten distinct behavior groups describing customers based on their demand and variability.”