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223 results on '"Balestrassi, Pedro Paulo"'

4. Statistics and Engineering

Author

Balestrassi, Pedro Paulo, Romão, Estevão Luiz, Magnani, Lorenzo, Editor-in-Chief, Aliseda, Atocha, Editorial Board Member, Longo, Giuseppe, Editorial Board Member, Sinha, Chris, Editorial Board Member, Thagard, Paul, Editorial Board Member, Woods, John, Editorial Board Member, Zambroni de Souza, Antonio Carlos, editor, Verkerk, Maarten J., editor, and Ribeiro, Paulo Fernando, editor

Published
2022
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48. A nonlinear time-series prediction methodology based on neural networks and tracking signals

Author

Bianchesi, Natália Maria Puggina, Matta, Cláudia Eliane da, Streitenberger, Simone Carneiro, Romão, Estevão Luiz, Balestrassi, Pedro Paulo, and Costa, Antônio Fernando Branco

Subjects
Nonlinear time series, Tracking signals, Design of Experiments, Time series forecasting, Neural networks
Abstract

Paper aims This paper presents a nonlinear time series prediction methodology using Neural Networks and Tracking Signals method to detect bias and their responsiveness to non-random changes in the time series. Originality This study contributes with an innovative approach of nonlinear time series prediction methodology. Furthermore, the Design of Experiments was applied to simulate datasets and to analyze the results of Average Run Length, identifying in which conditions the methodology is efficient. Research method Datasets were generated to simulate different nonlinear time series by changing the error of the series. The methodology was applied to the datasets and the Design of Experiments was implemented to evaluate the results. Lastly, a case study based on total oil and grease was performed. Main findings The results showed that the proposed prediction methodology is an effective way to detect bias in the process when an error is introduced in the nonlinear time series because the mean and the standard deviation of the error have a significant impact on the Average Run Length. Implications for theory and practice This study contributes to a discussion about time series prediction methodology since this new technique could be widely used in several areas to improve forecast accuracy.

Published
2022

49. A multiobjective portfolio optimization for energy assets using D-Optimal design and mixture design of experiments

Author

Leal, Gustavo dos Santos, Romão, Estevão Luiz, Reis, Daniel Leal de Paula Esteves dos, Balestrassi, Pedro Paulo, and Paiva, Anderson Paulo de

Subjects
Multivariate analysis, D-Optimal design, Portfolio optimization, Mixture design
Abstract

Paper aims Frequently, researchers try to find a better way to allocate assets in order to have maximum return and low variability in a portfolio as diverse as possible. This paper aims to apply D-Optimal Design in the context of Mixture Design and portfolio optimization to efficiently select the runs of the proposed experimental design. Originality A new approach to find the optimal weights that maximize the returns and minimize the risk using D-Optimal Design was used. A multi-response optimization problem considering returns, variability and entropy as functions of the weights was proposed. However, as there is a significant correlation between the objective functions, a Factor Analysis combined with FMSE to dimensionality reduction was used. Research method All the steps for both stages of the methodology applied in this paper are presented below: select real time series; predict one step ahead; generate a D-Optimal mixture design; apply weights and generate mathematical models; solve the optimization problem. Main findings Using the desirability method, the optimal values were determined, obtaining approximately 79% for the compound desirability function. The proposed method presented a 16.80% higher return with a 4.98% higher risk exposure if compared against Naïve method. Implications for theory and practice The proposed methodology can be applied to any portfolio optimization study. Mixture Design studies have already been proposed for modeling portfolio optimization problems. However, the D-Optimal Design proved to be adequate, which represents less computational effort.

Published
2022

50. Multivariate Strategy Using Artificial Neural Networks for Seasonal Photovoltaic Generation Forecasting.

Author

Moreira, Max Olinto, Kaizer, Betania Mafra, Ohishi, Takaaki, Bonatto, Benedito Donizeti, Zambroni de Souza, Antonio Carlos, and Balestrassi, Pedro Paulo

Subjects
ARTIFICIAL neural networks, STATISTICAL reliability, ELECTRIC power systems, PRINCIPAL components analysis, SPRING, PERCENTILES, DEMAND forecasting
Abstract

Electric power systems have experienced the rapid insertion of distributed renewable generating sources and, as a result, are facing planning and operational challenges as new grid connections are made. The complexity of this management and the degree of uncertainty increase significantly and need to be better estimated. Considering the high volatility of photovoltaic generation and its impacts on agents in the electricity sector, this work proposes a multivariate strategy based on design of experiments (DOE), principal component analysis (PCA), artificial neural networks (ANN) that combines the resulting outputs using Mixture DOE (MDOE) for photovoltaic generation prediction a day ahead. The approach separates the data into seasons of the year and considers multiple climatic variables for each period. Here, the dimensionality reduction of climate variables is performed through PCA. Through DOE, the possibilities of combining prediction parameters, such as those of ANN, were reduced, without compromising the statistical reliability of the results. Thus, 17 generation plants distributed in the Brazilian territory were tested. The one-day-ahead PV generation forecast has been considered for each generation plant in each season of the year, reaching mean percentage errors of 10.45% for summer, 9.29% for autumn, 9.11% for winter and 6.75% for spring. The versatility of the proposed approach allows the choice of parameters in a systematic way and reduces the computational cost, since there is a reduction in dimensionality and in the number of experimental simulations. [ABSTRACT FROM AUTHOR]

Published
2023
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