Navegando por Assunto "Wind speed"

Agora exibindo 1 - 2 de 2

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 séries temporais no sistema elétrico brasileiro utilizando preditores baseados em aprendizado de máquina: uma análise empírica
(Universidade Federal do Pará, 2024-04-05) CONTE, Thiago Nicolau Magalhães de Souza; OLIVEIRA, Roberto Célio Limão de; http://lattes.cnpq.br/4497607460894318; https://orcid.org/0000-0002-6640-3182
The overview of electric energy in Brazil is influenced by a variety of complex factors and nonlinear relationships, making forecasting challenging. With the increasing demand for energy and growing environmental concerns, it is crucial to seek solutions based on clean and renewable energy practices, aiming to make the energy market more sustainable. These practices aim to reduce waste and optimize the efficiency of processes involved in the operation of electricity distribution and generation technologies. A promising approach to enable sustainable energy is the application of forecasting techniques for various variables in the energy market. This thesis proposes an empirical analysis of the use of regressors to make predictions in the databases of the Price of Settlement Differences (PLD) in the Brazilian market and wind speed in wind turbines in Northeast Brazil, through principal component analysis. We aim to provide significant information about machine learning techniques that can be employed as effective tools for time series prediction in the electric sector. The results obtained may encourage the implementation of these techniques to extract knowledge about the behavior of the Brazilian energy system. This is particularly relevant, given that energy prices often exhibit seasonality, high volatility, and peaks, and wind power generation is widely influenced by weather conditions. To model the prediction of these two time series, we use the database on the Price of Settlement Differences (PLD), focusing especially on the average energy price of the Brazilian National System. The most relevant variables are related to hydrological conditions, electrical load, and fuel prices for thermal units. For collecting variables related to wind energy, two distinct locations in the Northeast region of Brazil were considered: Macau and Petrolina. For the prediction study, we use a Multilayer Perceptron Neural Network (MLP), a Long Short Term Memory (LSTM), Auto-Regressive Integrated Moving Average (ARIMA), and Support Vector Machine (SVM) to determine baseline results in prediction. To enhance the results of these regressors, we employ two different prediction approaches. One approach involves combining deep artificial neural network techniques based on the Canonical Genetic Algorithm (AG) meta-heuristic to adjust the hyperparameters of MLP and LSTM regressors. The second strategy focuses on machine committees, which include MLP, decision tree, linear regression, and SVM in one committee, and MLP, LSTM, SVM, and ARIMA in another. These approaches consider two types of voting, voting average (VO) and voting weighted average (VOWA), to assess the impact on the performance of the machine committee.