Título: Análise de Previsão de Geração Fotovoltaica na Região Metropolitana de Fortaleza Usando Técnicas de Aprendizado de Máquina: Um Estudo de Caso
Autores: Leonardo A. Vasconcelos de Oliveira, Romulo Cesar Cunha Lima, Jose Daniel de Alencar Santos
Resumo: This paper presents a case study for analysis and prediction of photovoltaic generation in the metropolitan region of Fortaleza-CE, applying machine learning techniques in the paradigms of time series prediction and system identification. Computational simulations were performed using linear (least squares) and non-linear (artificial neural networks and kernel methods) estimators that are part of the state-of-the-art in machine learning. In time series scenario, using only power measurements, the best results were obtained with the MLP network, with a prediction horizon of seven days ahead. In system identification, using power and solar radiation measurements, the least squares estimator achieved the best performance among all tested estimators, even in the free simulation scenario, i.e., infinite steps ahead.
Palavras-chave: Photovoltaic Generation, Electric Power, Machine Learning, Time Series Prediction, System Identification.
Páginas: 8
Código DOI: 10.21528/CBIC2023-047
Artigo em pdf: CBIC_2023_paper047.pdf
Arquivo BibTeX: CBIC_2023_047.bib