Reducing power companies billing costs via empirical bayes and seasonality remover. (May 2019)

• Record Type: Journal Article
• Authors: Rodrigues, Alexandre; Martinuzzo, Lucas; Varejao, Flavio Miguel; Silva Souza, Vítor E.; Oliveira-Santos, Thiago
• Is Part Of: Engineering applications of artificial intelligence. Volume 81(2019)
• Page Start: 387
• Page End: 396
• Publication Date: 2019-05
• Subjects: Empirical bayes -- Energy consumption -- Classification -- Seasonality; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.01.007
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Billing errors increase the costs of power companies and lower their reliability as perceived by customers. The majority of these errors are due to wrong readings that occur when employees of power companies visit the customers to read electrical meters and issue the bills. To prevent such errors, prediction techniques calculate a predicted value for each customer based on the values of their previous readings, plus a tolerance around this value, sending bills to be inspected by analysts if the reading extrapolates the established range. However, such analysis increases the personnel cost of the power company. In addition, wrongly printed bills lead to possible lawsuits and fines that might also affect the costs and reliability of the power company. The main focus of this work is to minimize personnel cost by reducing the number of correct readings sent to unnecessary analysis, while protecting the power company credibility by not increasing the number of bills with wrong values sent to clients in the process. The proposed solution uses Empirical Bayes methods along with a method to consider seasonal behavior of customers. The methodology was applied to a dataset comprising 35, 704, 489 measurements from 1, 330, 989 different customers of a Brazilian power company. The results show that the new methodology was able to decrease the number of correct bills sent to analysis without lowering the reputation of the company.