Navegando por Assunto "Time series"

Agora exibindo 1 - 3 de 3

Acesso aberto (Open Access)
Modelo de previsão hidrológica utilizando redes neurais artificiais: um estudo de caso na bacia do Rio Xingu- Altamira-Pa
(Universidade Federal do Pará, 2019-10-10) SILVA, Arilson Galdino da; CASTRO, Adriana Rosa Garcez; http://lattes.cnpq.br/5273686389382860
Knowledge about the extent of riverbed overflow is extremely necessary for the determination of areas at risk. The City of Altamira-PA, located on the banks of the Xingu River, historically suffers from extreme events of floods that provoke floods, causing great damages to the population. Considering the problem, this paper presents a monthly level prediction system of the Xingu River based on neural networks perceptron of multiple layers. For the development of the system, rainfall data were used in the basin and sub-basins of the Xingu River, and SST information (Sea Surface Temperature) from 1979 to 2016. The Satisfactory results demonstrate the great applicability of Artificial Neural Networks to the flood prediction problem, as compared to other methodologies have greater precision in finding solutions for nonlinear problems. For the treatment and selection of the input variables, the correlation approach was used, with the objective of improving the accuracy of the results, thus selecting the best information with their respective lags, in which they are inserted in three prediction scenarios: model with rainfall data, model with sea surface temperature information and application using the SST junction with rainfall. To measure the prediction capacity of the proposed methods, the Mean Squared Error (MSE) and coefficient of determination (R²) values were obtained for the best strategy, using only oceanic variables, SST, being the values 2,99x104 and 0,9991 considering, mainly, the treatment of input values of the Neural Network.
Acesso aberto (Open Access)
Modelo híbrido baseado em séries temporais e redes neurais para previsão da geração de energia eólica
(Universidade Federal do Pará, 2018-08-30) ALENCAR, David Barbosa de; OLIVEIRA, Roberto Célio Limão de; http://lattes.cnpq.br/4497607460894318; AFFONSO, Carolina de Mattos; http://lattes.cnpq.br/2228901515752720
The electric power generation through wind turbines is one of the practically inexhaustible alternatives sources of electric power. It is considered a source of clean energy, but still requires a lot of research to develop science and technologies that ensure uniformity in generation, providing a greater participation of this source in the energy matrix in Brazil as in the world, because the wind presents abrupt variations speed, density, and other important variables. In wind-based electrical systems, each forecast horizon is applied to a specific segment, forecast of minutes, hours, weeks, months, and future years of wind behavior, in order to evaluate the availability of energy for the next period, relevant information in the dispatch of the generating units and in the control of the electric system. This thesis aimed to develop ultra-short, short, medium and long-term prediction models of wind speed, based on computational intelligence techniques, using Artificial Neural Networks, SARIMA models and hybrid models and to predict the generation capacity of power for each horizon. For the application of the methodology, the meteorological variables of the database of the national environmental data system SONDA, Petrolina station, were used for the period from January 1st, 2004 to March 31st, 2017. The performance of the models was compared with 5, 10 and 20 steps forward, considering minutes, hours, days, weeks, months and years as the forecast horizon. The hybrid model obtained better response in the forecasts, among which the hour horizon was highlighted.
Acesso aberto (Open Access)
Previsão de geração de energia fotovoltaica utilizando transformação de séries temporais em imagens e redes neurais convolucionais bidimensionais
(Universidade Federal do Pará, 2023-10-26) MONTEIRO, Diego Ramiro Melo; CASTRO, Adriana Rosa Garcez; http://lattes.cnpq.br/5273686389382860
This research presents a novel approach based on a Bidimensional Convolutional Neural Network (CNN) and techniques for transforming time series data into images, such as Gramian Angular Field (GAF) and Recurrence Plot (RP), for short-term forecast of electricity generation from a photovoltaic microgrid connected to the electrical grid, located at the Center of Excellence in Energy Efficiency of the Amazon (Centro de Excelência em Eficiência Energética da Amazônia –CEAMAZON) at the Federal University of Pará (Universidade Federal do Pará –UFPA). The GAF and RP techniques were employed to transform the time series data into images, which were used as input for the CNN. More accurate electricity generation forecasts enable users to better estimate the potential costs for grid implementation and the payback periods, as well as assess the available load capacity that can be connected to the system with higher precision. The prediction results using GAF and RP with a 2D CNN were compared with results obtained using other established neural network architectures in the field, such as Multilayer Perceptron and 1D CNNs, yielding satisfactory Root Mean Square Error (RMSE) values. This demonstrates the applicability of using images generated from the transformation of photovoltaic time series data in a 2D CNN for this problem.