Técnicas de aprendizado de máquina para a predição de eventos extremos de sobreirradiância em Natal-RN
The current dissertation is centered on the forecasting of Overirradiance within intervals of up to five minutes, achieved through the utilization of machine learning methodologies. Overirradiance, a phenomenon characterized by solar irradiance surpassing anticipated values under clear-sky condition...
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| Formato: | Dissertação |
| Idioma: | pt_BR |
| Publicado em: |
Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/55192 |
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| Resumo: | The current dissertation is centered on the forecasting of Overirradiance within intervals of up to five minutes, achieved through the utilization of machine learning methodologies. Overirradiance, a phenomenon characterized by solar irradiance surpassing anticipated values under clear-sky conditions at the Earth’s surface, has generated scholarly interest within the sphere of solar energy research and its implications for photovoltaic power generation systems. To date, no dedicated studies investigating the application of
Machine Learning techniques for forecasting this phenomenon have been identified. In pursuit of this aim, the performance of four distinct machine learning algorithms
has been meticulously examined: Random Forest, Support Vector Machines (SVM), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM) neural networks. The
present study endeavors to bridge a lacuna in research by scrutinizing the feasibility and
efficacy of these algorithms in predicting Overirradiance events, thereby augmenting the
comprehension and pragmatic application of this phenomenon within solar energy systems. |
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