The use of renewable energies, such as Photovoltaic (PV) solar power, is necessary to meet the growing energy consumption. PV solar power generation has intrinsic characteristics related to the climatic variables that cause intermittence during the generation process, promoting instabilities and insecurity in the electrical system. One of the solutions for this problem uses methods for the Prediction of Solar Photovoltaic Power Generation (PSPPG). In this context, the aim of this study is to develop and compare the prediction accuracy of solar irradiance between Artificial Neural Network (ANN) and Long-Term Short Memory (LSTM) network models, from a comprehensive analysis that simultaneously considers two distinct sets of exogenous meteorological input variables and three short-term prediction horizons (1, 15 and 60 min), in a controlled experimental environment. The results indicate that there is a significant difference (p < 0.001) in the prediction accuracy between the ANN and LSTM models, with better overall prediction accuracy skill for the LSTM models (MAPE = 19.5%), except for the 60 min prediction horizon. Furthermore, the accuracy difference between the ANN and LSTM models decreased as the prediction horizon increased, and no significant influence was observed on the accuracy of the prediction with both sets of evaluated meteorological input variables.

The growth in the use of solar energy has encouraged the development of techniques for shortterm prediction of solar photovoltaic energy generation (PSPEG). Machine learning with Artificial Neural Networks (ANNs) is the most widely used technique to solve this problem. However, comparative studies of these networks with distinct structural configurations, input parameters and prediction horizon, have not been observed in the literature. In this context, the aim of this study is to evaluate the prediction accuracy of the Global Horizontal Irradiance (GHI), which is often used in the PSPEG, generated by ANN models with different construction structures, sets of input meteorological variables and in three short-term prediction horizons, considering a unique database. The analyses were performed with controlled environment and experimental configuration. The results suggest that ANNs using the input GHI variable provide better accuracy (approximately 10%), and their absence increases error variability. No significant difference (p>0.05) was identified in the prediction error models trained with distinct meteorological input data sets. The prediction errors were similar for the same ANN model in the different prediction horizons, and ANNs with 30 and 60 neurons with one hidden layer demonstrated similar or higher accuracy than those with two hidden layers.

Uma das maneiras de suprir o crescente consumo de energia elétrica com o uso de fontes de energia limpa e renovável, tal como a solar fotovoltaica. No entanto, este tipo de geração possui intermitências que aumentam a instabilidade e a insegurança da rede energética. Umas das soluções para este problema consiste no estudo de métodos para a Predição da Geração de Energia Solar Fotovoltaica (PGESF). Neste contexto, o presente estudo comparou a acurácia de predição de modelos de Redes Neurais Artificiais (RNA), publicado em [14], a partir de duas bases de dados (datasets) distintas e três diferentes horizontes de predição de curto prazo. Os resultados sugerem que o uso de diferentes variáveis meteorológicas e o tamanho do dataset influenciam significativamente (p-valor<0,001) na acurácia dos modelos. Além disso, a acurácia de predição dos modelos diminuiu conforme o horizonte de predição aumentou.

Uma das maneiras de suprir o crescente consumo de energia elétrica é por meio da energia solar fotovoltaica. No entanto, as condições climáticas promovem instabilidade neste tipo de geração de energia, limpa e renovável. Este estudo integra uma pesquisa sobre a predição de geração de energia solar fotovoltaica. O objetivo consiste em desenvolver uma ferramenta para captura automatizada de informações meteorológicas e imagens de satélite para uma determinada localização geográfica, utilizando serviços meteorológicos disponíveis na Internet. Os resultados preliminares são satisfatórios e demonstram a adequabilidade do método utilizado. A solução será disponibilizada (open-source) e contribuirá para o desenvolvimento de projetos que necessitem de captura automatizada das informações utilizadas.