Aplicações e desafios da inteligência artificial na previsão da radiação solar: uma revisão sistemática
DOI:
https://doi.org/10.55761/abclima.v36i21.18872Palavras-chave:
Aprendizado de máquina, Aprendizado Profundo, Modelo de previsão, Redes Neurais ArtificiaisResumo
A previsão precisa da radiação solar é fundamental para diversas áreas, como energia solar e agricultura. A Inteligência Artificial (IA) tem se mostrado uma ferramenta poderosa para aprimorar essas previsões. Este estudo analisou diversas pesquisas que utilizam IA para prever a radiação solar. As técnicas mais comuns incluem redes neurais, máquinas de vetores de suporte e aprendizado profundo. Essas técnicas são capazes de identificar padrões complexos nos dados e relacionar a radiação solar com fatores como temperatura, umidade e nebulosidade. Os modelos de IA são treinados com grandes conjuntos de dados meteorológicos e de radiação solar, o que lhes permite aprender a fazer previsões mais precisas. As métricas utilizadas para avaliar o desempenho desses modelos incluem o erro médio quadrático (RMSE), o coeficiente de determinação (R²) e o erro percentual absoluto médio (MAPE). A revisão demonstra que a IA tem um grande potencial para melhorar a previsão da radiação solar. As previsões mais precisas podem otimizar a produção de energia solar, melhorar a gestão da irrigação e auxiliar em diversos outros processos que dependem da radiação solar.
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