| Full text | |
| Author(s): |
Iwashita, Adriana Sayuri
[1]
;
Rodrigues, Douglas
[2]
;
Gastaldello, Danilo Sinkiti
[3]
;
de Souza, Andre Nunes
[3]
;
Papa, Joao Paulo
[2]
Total Authors: 5
|
| Affiliation: | [1] Univ Fed Sao Carlos, Dept Comp, Rod Washington Luis, Km 235, BR-13565905 Sao Carlos, SP - Brazil
[2] Sao Paulo State Univ, Dept Comp, Av Eng Luiz Edmundo Carrijo Coube 14-01, BR-17033360 Bauru, SP - Brazil
[3] Sao Paulo State Univ, Dept Elect Engn, Av Eng Luiz Edmundo Carrijo Coube 14-01, BR-17033360 Bauru, SP - Brazil
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | COMPUTERS & ELECTRICAL ENGINEERING; v. 95, OCT 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
Non-technical losses stand for the energy consumed but not billed, affecting the energy grid as a whole. Such an issue somehow prevails in developing countries, harming the quality of energy and preventing social programs benefit from tax revenues. Machine learning techniques can help mitigate it by mining information from fraudsters and legal users for further decision-making. In this paper, we deal with a steady increase of dataset size, i.e., the incremental learning problem, which can cope with datasets regularly provided by energy companies, requiring the learner to be updated constantly. Since repeating the entire learning process might be prohibitive, adjusting the model to the new data shows to be a better choice. We propose an incremental Optimum-Path Forest approach with k-nn neighborhood that is considerably more efficient for training than its counterpart version, with experiments validated in general-purpose datasets and also in the context of non-technical losses identification. (AU) | |
| FAPESP's process: | 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction? |
| Grantee: | Alexandre Xavier Falcão |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 19/07665-4 - Center for Artificial Intelligence |
| Grantee: | Fabio Gagliardi Cozman |
| Support Opportunities: | Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program |
| FAPESP's process: | 18/21934-5 - Network statistics: theory, methods, and applications |
| Grantee: | André Fujita |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 17/02286-0 - Probabilistic models for commercial losses detection |
| Grantee: | André Nunes de Souza |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |