An electric energy consumer characterization framework based on data mining techniques

By V. Figueiredo, F. Rodrigues, Zita A. Vale, J. Gouveia. IEEE Transactions on Power Systems, 2005

431 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/5767bce6772910fe70b5b9b4a28b9e3206343950

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. A. Nizar
2. Zhao Yang Dong
3. June A. Flora
4. Ram Rajagopal
5. Jungsuk Kwac
6. Y. Wang
7. Xinghuo Yu
8. Damminda Alahakoon

Citations in the corpus

• Household Energy Consumption Segmentation Using Hourly Data (2014, methodology): “Some significant contributions in segmentation are [3], [4], [16], [17], [19], [20].”
• Power Utility Nontechnical Loss Analysis With Extreme Learning Machine Method (2008, background): “Moreover, several studies conducted before use clustering analysis to group all the customers based on their pattern similarity [6], [12], [30], however, the clustering process in this study focus more on individual customer in order to group similar pattern existed in each customer that may due to…”