Título: Metaheuristic based MLP-SARIMAX HybridizationOne Hour Ahead Solar Radiation Forecasting
Autores: Hugo Abreu Mendes, João de Oliveira, Paulo Mattos, Alex Coutinho Pereira, Eduardo Boudoux Jatoba, Jose Bione de Melo Filho, Elisabete Joana Paes Barreto and Manoel Marinho.
Resumo:
Within the context of clean energy generation, solar radiation forecast is applied for photovoltaic plants to increase maintainability and reliability. Statistical models of time series like ARIMA and machine learning techniques help to improve the results. Hybrid Statistical + ML are found in all sorts of time series forecasting applications. This work presents a new way to automate the SARIMAX modeling, nesting PSO and ACO optimization algorithms, differently from R’s AutoARIMA, its searches optimal seasonality parameter and combination of the exogenous variables available. This work presents 2 distinct hybrid models that have MLPs as their main elements, optimizing the architecture with Genetic Algorithm. A methodology was used to obtain the results, which were compared to LSTM, CLSTM, MMFF and NARNN-ARMAX topologies found in recent works. The obtained results for the presented models is promising for use in automatic radiation forecasting systems since it outperformed the compared models on at least two metrics.
Palavras-chave:
Solar radiation, time series, machine learning, optimization, autoML.
Páginas: 8
Código DOI: 10.21528/CBIC2021-9
Artigo em pdf: CBIC_2021_paper_9.pdf
Arquivo BibTeX: CBIC_2021_9.bib