Towards Assessing the Electricity Demand in Brazil: Data-Driven Analysis and Ensemble Learning Models

João Vitor Leme, Wallace Casaca, Marilaine Colnago and Maurício Araújo Dias
Additional contact information
João Vitor Leme: Department of Energy Engineering, São Paulo State University (UNESP), Rosana, SP 19274-000, Brazil
Wallace Casaca: Department of Energy Engineering, São Paulo State University (UNESP), Rosana, SP 19274-000, Brazil
Marilaine Colnago: Department of Energy Engineering, São Paulo State University (UNESP), Rosana, SP 19274-000, Brazil
Maurício Araújo Dias: Faculty of Science and Technology (FCT), São Paulo State University (UNESP), Presidente Prudente, SP 19060-900, Brazil

Energies, 2020, vol. 13, issue 6, 1-20

Abstract: The prediction of electricity generation is one of the most important tasks in the management of modern energy systems. Improving the assertiveness of this prediction can support government agencies, electric companies, and power suppliers in minimizing the electricity cost to the end consumer. In this study, the problem of forecasting the energy demand in the Brazilian Interconnected Power Grid was addressed, by gathering different energy-related datasets taken from public Brazilian agencies into a unified and open database, used to tune three machine learning models. In contrast to several works in the Brazilian context, which provide only annual/monthly load estimations, the learning approaches Random Forest, Gradient Boosting, and Support Vector Machines were trained and optimized as new ensemble-based predictors with parameter tuning to reach accurate daily/monthly forecasts. Moreover, a detailed and in-depth exploration of energy-related data as obtained from the Brazilian power grid is also given. As shown in the validation study, the tuned predictors were effective in producing very small forecasting errors under different evaluation scenarios.

Keywords: energy forecasting; data-driven analysis; machine learning; Brazilian power grid (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/6/1407/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/6/1407/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:6:p:1407-:d:333831

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().