Forecasting demand model for emergency maintenance orders based on climatic factors in an electricity distribution system

Authors

  • Lucas Machado Basso Universidade Federal de Santa Maria (UFSM)
  • Jéssica de Assis Dornelles
  • Verônica Maurer Tabim
  • Vinícius Jaques Garcia

DOI:

https://doi.org/10.14488/1676-1901.v21i1.4163

Keywords:

Demand forecast. Data mining. Emergency maintenance. Multiple regression. Electricity distributor.

Abstract

Forecasting the demand for maintenance orders is essential for planning the manpower, materials, and infrastructure resources needed to answer calls and ensure the availability of the electricity distribution system. The objective of this study is to develop a demand forecasting model for emergency maintenance orders in an electricity distribution system considering climatic factors that may affect this demand. This study presents applied research as to its nature, exploratory as to its objectives, and with a quantitative approach. The working method has four main stages: (i) mining of emergency maintenance order data in relation to climatic factors; (ii) determining the appropriate demand forecasting method; (iii) elaborating a demand forecast model for emergency orders considering climatic factors; and (iv) validation of the demand forecast model based on collected data. As a result, the demand forecasting model for emergency maintenance orders based on climatic factors for an electricity distribution system was obtained.

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Author Biography

Lucas Machado Basso, Universidade Federal de Santa Maria (UFSM)

Engenheiro de Produção pela Universidade Federal de Pelotas (UFPel) e Mestre em Engenharia de Produção na Universidade Federal de Santa Maria (UFSM). 

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Published

2021-03-15

How to Cite

Basso, L. M., Dornelles, J. de A., Tabim, V. M., & Garcia, V. J. (2021). Forecasting demand model for emergency maintenance orders based on climatic factors in an electricity distribution system. Revista Produção Online, 21(1), 74–104. https://doi.org/10.14488/1676-1901.v21i1.4163

Issue

Vol. 21 No. 1 (2021)

Section

Papers

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