Household Energy Consumption Segmentation Using Hourly Data
By Jungsuk Kwac, June A. Flora, Ram Rajagopal. IEEE Transactions on Smart Grid, 2014
489 citations recorded by the source corpus.
Subfields: Building and Construction, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/8513a7a250aa760414da722205484ac41b8b34f7
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Building and Construction
In Electrical and Electronic Engineering
- Chongqing Kang
- Qixin Chen
- Yi Wang
- Tao Hong
- Stephen A. Haben
- David Flynn
- Benoit Couraud
- Xiao-Jun Zeng
Citations in the corpus
- Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges (2018, methodology): “Both adaptive k-means and hierarchical clustering were used to obtain the typical load shapes of all the consumers within a certain error threshold in [148].”
- Analysis and Clustering of Residential Customers Energy Behavioral Demand Using Smart Meter Data (2016, background): “The clusters can be used to identify groups of customers who are suitable for various demand reduction initiatives [8].”
- Hybrid CNN-LSTM Model for Short-Term Individual Household Load Forecasting (2020, unknown): “[45]–[47]).”
- Big Data Meet Green Challenges: Big Data Toward Green Applications (2016, methodology): “Then, facing enormous generated raw data, Kwac et al.[55] proposed an encoding system using a preprocessed dictionary.”
- Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review (2020, methodology): “The most widespread categorisation of consumers for DR purposes is based on their load profiles [68,82,84,85,90,91,95,97].”
- Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities (2015, background): “Kwac et al. [19] utilized smart meter data for customer segmentation.”
- Clustering-based short-term load forecasting for residential electricity under the increasing-block pricing tariffs in China (2018, background): “Households with similar income levels often have similar electricity consumption behaviours [12, 29, 37]; therefore, it makes sense to group them in the same segment.”