Renato Fernandes Cantão

• Abstract

  With the increasing demand for energy different forms of electrical energy yield arise. Among them there is the photovoltaic solar energy, a renewable, abundant and low cost energy source whose applications depend directly on the solar irradiation intensity. In this project one proposes the use of Supervised Machine Learning techniques in order to implement models capable of predicting so…

  Solar irradiation forecast for solar photovoltaic systems using machine learning: a supervised learning approach to forecast solar irradiation in the state of São Paulo, Brazil, BP.IC

• Abstract

  The stock exchange is extremely dynamic, making operations in stock market a complex task with high intrinsic risk. In contrast, the financial return can be far superior to other investment modalities. Attempts to anticipate asset pricing movements in such environment can significantly reduce the risk of operation, although there is not a consensus on the possibility of making this type o…

  Use of machine learning as an indication of price movement trends in the financial market, BP.IC

• Abstract

  In this work we propose to assess the application of deep neural networks as surrogate models for traditional differential models. Neural networks learning process will be guided not only by a training set -- as usual -- but also by adding constraints imposing characteristics from the phenomenon being modelled. The training set will be formed from samples coming from solutions of model pr…

  Studies on the use of neural networks as surrogate models for partial differential equations, BP.IC

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