Navegando por Assunto "Photovoltaic energy generation"

Agora exibindo 1 - 2 de 2

Acesso aberto (Open Access)
Ferramenta computacional para dimensionamento e avaliação de desempenho de sistemas fotovoltaicos conectados à rede elétrica
(Universidade Federal do Pará, 2012-10-11) PINTO FILHO, Gilberto Figueiredo; MACÊDO, Wilson Negrão; http://lattes.cnpq.br/3386249951714088
This dissertation presents the utilization of some mathematical models present on literature which represent the energy processing steps on Grid-Connected Photovoltaic Systems (GCPV). Besides, two models regarding the power limitation due to inverter’s temperature and the electric losses are proposed. All models are implemented at MATLAB GUIDE enviroment which allows the analysis, helps on the design and makes possible the operacionality simulation and and energy contribution of GCPV with diferent sizes. The work presents the program interface developed and its data validation by comparing it with experimental data. Some energy forecasting for five Brazilian cities are shown at the end of the work with examples on how analyze the data generated by the program.
Acesso aberto (Open Access)
Previsão da irradiação solar utilizando método ensemble para seleção de atributos e algoritmos de aprendizado de máquina
(Universidade Federal do Pará, 2023-06-20) MEJIA, Edna Sofia Solano; AFFONSO, Carolina de Mattos; http://lattes.cnpq.br/2228901515752720
Accurate forecasting of solar irradiance is essential for effective management of power systems with significant photovoltaic generation. Machine learning algorithms, which leverage historical data and patterns to make predictions, play a crucial role in this task. One key aspect is the use of ensemble models that combine the predictions of multiple algorithms to improve forecast accuracy and reliability. In this study, ensemble models are utilized to enhance the forecasting performance by aggregating the predictions of different algorithms. Moreover, the paper proposes an ensemble feature selection method, which involves identifying the most relevant input parameters and their related past observations. This approach aims to optimize the input features used by the machine learning algorithms, ensuring that only the most pertinent information is considered for accurate solar irradiance forecasts. By leveraging the strengths of multiple algorithms and selecting the most informative features, the ensemble approach offers a robust framework for improving the accuracy of solar irradiance predictions. The performance of several machine learning algorithms, including ensemble models, is compared for solar irradiance forecasting on days with different weather patterns using endogenous and exogenous inputs. The algorithms considered are AdaBoost, SVR, RF, XGBT, CatBoost, VOA, and VOWA. The proposed ensemble feature selection relies on the RF, IM, and Relief algorithms. The forecast accuracy is evaluated based on several metrics using a real database of the city of Salvador, Brazil. Different weather forecasts are considered: 1 hour, 2 hours, 3 hours, 6 hours, 9 hours, and 12 hours in advance. Numerical results show that the proposed ensemble feature selection improves forecast accuracy, and that the VOWA model selected with the best-performing algorithms presents forecasts with higher accuracy than the other algorithms at different forecast time horizons. This research demonstrates the effectiveness of ensemble models and feature selection techniques in enhancing solar irradiance forecasting, providing valuable insights for efficient power system management.