Big Data Analytics for Spatio-Temporal Service Orders Demand Forecasting in Electric Distribution Utilities

Vitor Hugo Ferreira, Rubens Lucian da Silva Correa, Angelo Cesar Colombini, Márcio Zamboti Fortes, Flávio Luis de Mello, Fernando Carvalho Cid de Araujo and Natanael Rodrigues Pereira
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Vitor Hugo Ferreira: Electrical Engineering Department, Universidade Federal Fluminense, Rua Passo da Pátria, 156, Bloco D, Niterói 24210-240, Brazil
Rubens Lucian da Silva Correa: Electrical Engineering Department, Universidade Federal Fluminense, Rua Passo da Pátria, 156, Bloco D, Niterói 24210-240, Brazil
Angelo Cesar Colombini: Electrical Engineering Department, Universidade Federal Fluminense, Rua Passo da Pátria, 156, Bloco D, Niterói 24210-240, Brazil
Márcio Zamboti Fortes: Electrical Engineering Department, Universidade Federal Fluminense, Rua Passo da Pátria, 156, Bloco D, Niterói 24210-240, Brazil
Flávio Luis de Mello: Electronic and Computing Engineering Department, Universidade Federal do Rio de Janeiro, Av. Athos da Silveira Ramos, 149, Bloco A, Rio de Janeiro 21941-909, Brazil
Fernando Carvalho Cid de Araujo: HOC Soluções em TI, Rua da Conceição, 125, Centro, Niterói 24020-006, Brazil
Natanael Rodrigues Pereira: Energisa, Rua Manoel dos Santos Coimbra, 184, Bandeirantes, Cuiabá 78010-040, Brazil

Energies, 2021, vol. 14, issue 23, 1-16

Abstract: This paper presents a big data analytics-based model developed for electric distribution utilities aiming to forecast the demand of service orders (SOs) on a spatio-temporal basis. Being fed by robust history and location data from a database provided by an energy utility that is using this innovative system, the algorithm automatically forecasts the number of SOs that will need to be executed in each location in several time steps (hourly, monthly and yearly basis). The forecasted emergency SOs demand, which is related to energy outages, are stochastically distributed, projecting the impacted consumers and its individual interruption indexes. This spatio-temporal forecasting is the main input for a web-based platform for optimal bases allocation, field team sizing and scheduling implemented in the eleven distribution utilities of Energisa group in Brazil.

Keywords: utility analytics; prediction analytics; big data in power system operation; advance statistics for energy; spatio-temporal forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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