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.
Highlights
Electricity is part of a composite market that involves generation, transmission, and consumption agents
The first calculated statistic was the Coefficient of Variation (CV), which gave us 13.39%. Such a percentage indicates that the variation of electricity demand in Brazil is homogeneous, i.e., it basically follows a stable distribution, as the CV establishes a measure of dispersion between the standard deviation and the average
This study focused on the study of electricity load prediction in the Brazilian Interconnected
Summary
Electricity is part of a composite market that involves generation, transmission, and consumption agents Such a free market has become highly competitive in recent years, leveraging the participation of several investors, electric companies, and public agencies [1]. As most system operational decisions occur as a response to data gathered and processed at the control center, the use of data-driven platforms are crucial to get useful information and make intelligent choices [2] These data-guided frameworks are important in the Brazilian context—as the goal of our work—since the national power grid is currently operated by a general grid operator that arbitrates when and how much each power plant will produce from official computer models [3]. This means that, in most cases, the wholesale market prices in Brazil are determined by the opportunity costs of these renewable power plants, based on the acquired data and supply and demand tendencies [4]
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