Multivariate Strategy Using Artificial Neural Networks for Seasonal Photovoltaic Generation Forecasting

Max Olinto Moreira, Betania Mafra Kaizer, Takaaki Ohishi, Benedito Donizeti Bonatto, Antonio Carlos Zambroni de Souza and Pedro Paulo Balestrassi ()
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Max Olinto Moreira: Federal Institute of Education, Science and Technology—South of Minas Gerais, Carmo de Minas 37472-000, MG, Brazil
Betania Mafra Kaizer: Institute of Production Engineering and Management, Federal University of Itajubá, Itajubá 37500-903, MG, Brazil
Takaaki Ohishi: Electrical and Computer Engineering Faculty, State University of Campinas, Campinas 13083-970, SP, Brazil
Benedito Donizeti Bonatto: Institute of Electrical Systems and Energy, Federal University of Itajubá, Itajubá 37500-903, MG, Brazil
Antonio Carlos Zambroni de Souza: Institute of Electrical Systems and Energy, Federal University of Itajubá, Itajubá 37500-903, MG, Brazil
Pedro Paulo Balestrassi: Institute of Electrical Systems and Energy, Federal University of Itajubá, Itajubá 37500-903, MG, Brazil

Energies, 2022, vol. 16, issue 1, 1-30

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.

Keywords: photovoltaic forecasting; principal component analysis; design of experiments; artificial neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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