Integrated expert system applied to the analysis of non-technical losses in power utilities

Intelligent Decision Support Systems

“…Various scientific examinations are being performed in the area of decision support via text analytics and mining, and decision support systems are being developed (Chan and Franklin 2011;Abrahams et al 2012;Gerber 2014;He 2013;Khare and Chougule 2012;Rao and Dey 2011;Rexer et al 2012;Toivonen et al 2006;León et al 2011;Froelich and Ananyan 2008;Cao et al 2011;Li and Wu 2010;Jiao et al 2007;Holton 2009). The examinations first mentioned are described in brief henceforth.…”

“…This IES include several modules: text mining module for analysis of inspector commentaries and extraction of additional information on the customer, data mining module to draw up the rules that determine the customer estimate consumption and the Rule Based Expert System module to analyze each customer using the results of the text and data mining modules. IES is used as a Decision support system (DSS), as it contains another module which provides a report with additional information about the customer and a summarized result that the inspectors can use to reach a decision (León et al 2011). Froelich and Ananyan (2008) accented that not only companies, governments and individuals are able to make use of decision support via text mining competitive advantage but numerous industries can benefit from text mining including insurance, finance, consumer products, manufacturing, healthcare, life sciences, hospitality, retail, transportation, information technology, government, and education as well.…”

Data science for building energy management: A review

“…Their predictive analysis tool, supplemented by a binary quest tree classification method, was used to discovered association rules in the data. These same authors [57] proposed an expert system for NTL detection. They used regression to study the consumption trends of customers, text mining techniques to analyze inspector commentaries, and association rules to extract additional customer information from the electric company.…”

Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detection

“…Introduction 45 Losses are a serious issue facing utility distribution companies. 46 In power distribution companies, there are two types of losses: 47 technical losses and non-technical losses (NTLs).The NTLs are 48 caused by illegal manipulations or faults in consumer facilities, 49 and the amount of consumption cannot be registered by the sys- 50 tem, whereas technical losses are caused by physical effects (e.g., 51 the Joule effect) that are due to the power distribution. The tech-52 nical losses can be forecasted with a low error rate and might be 53 reduced by means of using better facilities.…”