Name: Pedro Junior Zucatelli
Type: PhD thesis
Publication date: 21/05/2021

Namesort descending Role
Davidson Martins Moreira Advisor *

Examining board:

Namesort descending Role
Ana Paula Meneguelo Internal Examiner *
Davidson Martins Moreira Advisor *
Erick Giovani Sperandio Nascimento Co advisor *
Neyval Costa Reis Jr. Internal Examiner *

Summary: It is known that one of the main constituents of modern society is energy, which is necessary
to create consumer goods based on natural resources and to supply many of the services that
human beings have favored. It is a fact that since the first industrial revolution there has been
an exponential increase in emissions of greenhouse gases into the atmosphere, potentiating
global warming and, consequently, climate change, air pollution, and health problems.
Therefore, scientific studies applied to sustainable technologies are justified to guarantee
quality and increase the generation of energy from alternative sources to supply this demand.
Inserted in this context, wind energy is a sustainable alternative in full development in Brazil
and Uruguay, sites that contemplated this research on the short-term and medium-term wind
power forecasting using a hybrid model based on computational intelligence and wavelet
decomposition. The general objective of this study was to evaluate and implement
improvements in the forecast of wind power generation in the short and medium-term, 1 h to
168 h ahead, in microscale spatial resolution using computational simulation methodology
applying supervised machine learning by artificial neural network and the decomposition of
temporal signals using Wavelets transform. This effort aimed to meet the shortage in this matter
and the demand of the electric energy production and distribution sector in Brazil and Uruguay,
to enable an improvement in the use of wind power in current projects and future exploration,
production, and commercialization of this energy source. It is noteworthy that this hybrid
forecasting model originated a low computational cost tool designed to provide such forecasts
to electric energy concessionaires, generators and distributors, and even to the electric system
operators. The results achieved in this research proved that the discrete Meyer wavelet function
among 48 studied functions has less associated error for the application of filtering and
decomposition of wind speed signals, becoming the most efficient for such application, and the
use of these filtered data in the feed of recurrent neural networks it was effective for mediumterm wind speed forecasting and medium-term wind power forecasting, and short-term wind
power ramp forecasting in tropical and subtropical sites.

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