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Advancing Sustainable Energy Solutions: AI Hybrid Renewable Energy Systems With Hybrid Optimization Algorithms And Multi-Objective Optimization In Portugal
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Orientador(es)
Resumo(s)
The effects of global warming are increasingly evident in our daily lives, with climate change,
due to the evolution of the economy and the population, therefore it is necessary to take an
active role in the development of solutions aimed at achieving a sustainable and carbon
neutral future.
The energy sector is one of the sectors of our society that contributes to global
warming, with the use of coal, oil, natural gas, among others. This is therefore a sector that
would benefit from the development of sustainable energy solutions, aimed at an energy
transition focused on the use of natural resources. The use of renewable energy sources is,
however, associated with the challenge of being intermittent, since it depends on the weather
conditions, which is why it is necessary to combine two or more renewable energy sources to
create a hybrid renewable energy system, to guarantee the system's reliability.
This is the purpose of this dissertation, the optimization of a hybrid renewable energy
system in the context of Portugal. The research began with a literature review in the field of
artificial intelligence and energy, and nine studies were identified from the systematic
literature review, which enabled the problem to be formulated. With this a hybrid renewable
energy system integrating solar panels and wind turbines was designed and optimized using a
hybrid algorithm. The hybrid algorithm developed integrates the Particle Swarm Optimization
and Grey Wolf Optimization algorithms, with the aim of combining the advantages of each
and thus approaching the global optimum. This algorithm was also implemented in parallel,
to overcome the difficulties experienced in terms of execution time and use of computing
resources.
Three experiments were carried out on the hybrid algorithm developed to try to
achieve the global minimum, however, it was only in the third experiment that it was possible
to identify a local minimum with an ABF close to the global minimum. For this to be possible,
experiment 3 integrated the changes mentioned in the two initial experiments as being
necessary for a more efficient optimization.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
Palavras-chave
Data Science Artificial Intelligence Hybrid Renewable Energy Systems Multi-objective optimization SDG 11 - Sustainable cities and communities SDG 13 - Climate action