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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Leme, Joao Vitor [1] ; Casaca, Wallace [1, 2] ; Colnago, Marilaine [1] ; Dias, Mauricio Araujo [3]
Total Authors: 4
[1] Sao Paulo State Univ UNESP, Dept Energy Engn, BR-19274000 Rosana, SP - Brazil
[2] Ctr Math Sci Appl Ind CeMEAI, BR-13566590 Sao Carlos, SP - Brazil
[3] Sao Paulo State Univ UNESP, Fac Sci & Technol FCT, BR-19060900 Presidente Prudente, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: ENERGIES; v. 13, n. 6 MAR 2020.
Web of Science Citations: 0

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. (AU)

FAPESP's process: 18/15965-5 - Electricity and price forecasting in hydrothermal scheduling: models and applications via machine learning
Grantee:João Vitor de Moraes Leme
Support type: Scholarships in Brazil - Scientific Initiation
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:José Alberto Cuminato
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC