Título: Comparing Neural Network Models for Photovoltaic Power Generation Prediction
Autores: Carlos Henrique Andrade, Tiago Vieira, Ícaro Araújo, Gustavo Melo, Erick Barboza, Davi Brito and Igor Torres
Resumo:
Research on alternative energy sources has been
increasing for the past years due to environmental concerns
and the depletion of fossil fuels. Since photovoltaic generation
is intermittent, one needs to predict solar incidence to alleviate
problems due to demand surges in conventional distribution systems.
Many works have used Long Short-Term Memory (LSTMs)
to predict generation. However, to minimize computational costs
related to retraining and inference, LSTMs might not be optimal.
Therefore, in this work, we compare the performance of MLP
(Multilayer Perceptron), Recurrent Neural Networks (RNNs),
and LSTMs for the task mentioned above. We used the solar
radiance measured throughout 2020 in the city of Maceió (Brazil),
taking into account periods of 2 hours for training to predict
the next 5-minutes. Hyperparameters were fine-tuned using an
optimization approach based on Bayesian inference to promote
a fair comparison. Results showed that the MLP has satisfactory
performance, requiring much less time to train and forecast. Such
results can contribute, for example, to improving response time
in embedded systems.
Palavras-chave:
Multi-Layer Perceptrons, Recurrent Neural Networks, Renewable energy sources, PV power forecasting.
Páginas: 8
Código DOI: 10.21528/CBIC2021-110
Artigo em pdf: CBIC_2021_paper_110.pdf
Arquivo BibTeX: CBIC_2021_110.bib