The overview of electric energy in Brazil is influenced by a variety of complex factors and
nonlinear relationships, making forecasting challenging. With the increasing demand for
energy and growing environmental concerns, it is crucial to seek solutions based on clean and
renewable energy practices, aiming to make the energy market more sustainable. These
practices aim to reduce waste and optimize the efficiency of processes involved in the operation
of electricity distribution and generation technologies. A promising approach to enable
sustainable energy is the application of forecasting techniques for various variables in the
energy market. This thesis proposes an empirical analysis of the use of regressors to make
predictions in the databases of the Price of Settlement Differences (PLD) in the Brazilian
market and wind speed in wind turbines in Northeast Brazil, through principal component
analysis. We aim to provide significant information about machine learning techniques that
can be employed as effective tools for time series prediction in the electric sector. The results
obtained may encourage the implementation of these techniques to extract knowledge about the
behavior of the Brazilian energy system. This is particularly relevant, given that energy prices
often exhibit seasonality, high volatility, and peaks, and wind power generation is widely
influenced by weather conditions. To model the prediction of these two time series, we use the
database on the Price of Settlement Differences (PLD), focusing especially on the average
energy price of the Brazilian National System. The most relevant variables are related to
hydrological conditions, electrical load, and fuel prices for thermal units. For collecting
variables related to wind energy, two distinct locations in the Northeast region of Brazil were
considered: Macau and Petrolina. For the prediction study, we use a Multilayer Perceptron
Neural Network (MLP), a Long Short Term Memory (LSTM), Auto-Regressive Integrated
Moving Average (ARIMA), and Support Vector Machine (SVM) to determine baseline results
in prediction. To enhance the results of these regressors, we employ two different prediction
approaches. One approach involves combining deep artificial neural network techniques based
on the Canonical Genetic Algorithm (AG) meta-heuristic to adjust the hyperparameters of MLP
and LSTM regressors. The second strategy focuses on machine committees, which include
MLP, decision tree, linear regression, and SVM in one committee, and MLP, LSTM, SVM, and
ARIMA in another. These approaches consider two types of voting, voting average (VO) and
voting weighted average (VOWA), to assess the impact on the performance of the machine
committee.
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