Power Utility Nontechnical Loss Analysis With Extreme Learning Machine Method

By A. Nizar, Zhao Yang Dong, Y. Wang. IEEE Transactions on Power Systems, 2008

355 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/81f40a75802dcff2278f24c22205952b0b330285

How researchers see this paper

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

In Artificial Intelligence

1. G. Huang
2. Yuan Lan
3. Dianhui Wang
4. Chi-Man Vong
5. Liyanaarachchi Lekamalage Chamara Kasun
6. Zuo Bai
7. Rongxing Lu
8. Xuemin Shen

In Automotive Engineering

1. Damminda Alahakoon
2. Xinghuo Yu

In Electrical and Electronic Engineering

1. G. Huang
2. Chongqing Kang
3. Tao Hong
4. Yuan Lan
5. Dianhui Wang
6. Qixin Chen
7. Yi Wang
8. Chi-Man Vong

Citations in the corpus

• Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey (2016, methodology): “Knowledge gathered from customers’ load profiles could also be used to identify, detect, and predict behavior irregularities or abnormalities that ultimately may be due to faulty metering or human intervention and fraud [51],”
• Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges (2018, background): “Rather than clustering all load profiles, clustering was only conducted within an individual consumer to obtain the typical and atypical load profiles in [88].”
• Local Receptive Fields Based Extreme Learning Machine (2015, background): “It presents competitive accuracy with superb efficiency in many different applications such as biomedical analysis [14], [15], chemical process [16], system modeling [17], [18], power systems [19], action recognition [20], hyperspectral images [21], etc.”
• Energy-theft detection issues for advanced metering infrastructure in smart grid (2014, methodology): “also investigated the efficiency of the SVM technique, Extreme Learning Machine (ELM) and its online sequential ELM (OS-ELM) variant for identification of abnormal load behavior indicating energy theft based on a load-profile evaluation[51-53].”
• Nontechnical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines (2010, neutral): “Due to the problem associated with NTLs in electric utilities [11] methods for efficient management of NTLs [12], protecting revenue in the distribution industry [13], [14] and detecting fraud electricity consumers [15] have been proposed.”
• Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine (2014, methodology): “In practice, ELM has been used in many different applications, including both regression and classification tasks [20]–[22].”
• Extreme learning machines: a survey (2011, background): “Recently, ELM techniques have received considerable attention in computational intelligence and machine learning communities, in both theoretic study and applications [41–44, 50, 51, 78, 80, 91, 94–119].”