Aplicações de redes neurais e previsões de disponibilidade de recursos energéticos solares

Fiorin, Daniel V.; Martins, Fernando Ramos; Schuch, Nelson Jorge; Pereira, Enio Bueno

doi:10.1590/s1806-11172011000100009

Cited by 18 publications

(19 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Fortunately, it inherits the speed advantage of the Gauss-Newton algorithm and the stability of the steepest descent method (Yu et al, 2011). The gradient is computed by the Jacobian matrix transpose that has the first derivation of the cost function as a function of the errors (Fiorin et al, 2011). For the stopping criterion of the optimization methods, the mean squared error variation per epoch is used, that means that the process ends when the variation is small enough.…”

Section: Multilayer Perceptronmentioning

confidence: 99%

Twenty-four Hours Ahead Solar Irradiance Forecast Based on Artificial Neural Network, ARIMA and SARIMA

Ferraz

Souza

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

View full text Add to dashboard Cite

Over the past years, the Brazilian electricity matrix has shown a tendency to increase the diversification with the insertion of renewable energy as solar and wind. However, the change in the electric power scenario leads to greater complexity in the planning and operation of the system due to its intermittency and the impossibility of storing both natural resources. In order to mitigate this problem, predictive systems have been sought to accurately forecast short-term irradiance and wind speed. The purpose of this paper was to predict irradiance 24 hours ahead from a method based on artificial intelligence, neural network and applying a data pre-processing stage which consisted of obtaining the stochastic components of the daytime data. Thus, the nighttime data were eliminated and the value of each daytime sample was subtracted from the calculated daily trend curve value at its corresponding hour, and finally, the data were normalized. In addition, statistical methods, such as integrated auto-regressive of moving average and integrated auto-regressive of seasonal moving average, were used in order to compare the results achieved, and the artificial neural network presented better results. Resumo: Nos últimos anos, a matriz elétrica brasileira tem apresentado uma tendência de maior diversificação com a inserção de fontes renováveis como solar e eólica. Porém, essa mudança do cenário elétrico tem resultado em uma maior complexidade no planejamento e operação do sistema em função da intermitência dessas fontes e a impossibilidade de armazenamento desses recursos naturais. Com o intuito de mitigar esse problema, tem-se buscado a melhor técnica de previsão dessas fontes a curto prazo. Para esse trabalho, realizou-se a predição da irradiância no horizonte de 24 horas, a partir do método baseado em inteligência artificial, a rede neural. Vale ressaltar que aplicou-se uma etapa de pré-processamento dos dados que consistiu na obtenção dos componentes estocásticos dos dados diurnos, com isso, dados noturnos foram eliminados, e o valor de cada amostra do período diurno é descontado o valor correspondente ao ponto da curva de tendência diária naquele instante, e por fim, os dados foram normalizados. Ademais, utilizou-se métodos estatísticos como auto-regressivo integrado de média móvel e auto-regressivo integrado de média móvel sazonal a fim de comparar os resultados alcançados, sendo que a rede neural apresentou melhor resultado.

show abstract

Section: Multilayer Perceptronmentioning

confidence: 99%

Twenty-four Hours Ahead Solar Irradiance Forecast Based on Artificial Neural Network, ARIMA and SARIMA

Ferraz

Souza

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

View full text Add to dashboard Cite

show abstract

“…ANN is able of storing knowledge and understanding the complex non-linear relationship between output and input data, covering regression problems, forecasting models and other applications in different fields [25][26][27].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Assessment of ANN and SVM models for estimating normal direct irradiation (Hb)

Santos

Escobedo

Teramoto

et al. 2016

Energy Conversion and Management

View full text Add to dashboard Cite

a b s t r a c tThis study evaluates the estimation of hourly and daily normal direct irradiation (H b ) using machine learning techniques (ML): Artificial Neural Network (ANN) and Support Vector Machine (SVM). Time series of different meteorological variables measured over thirteen years in Botucatu were used for training and validating ANN and SVM. Seven different sets of input variables were tested and evaluated, which were chosen based on statistical models reported in the literature. Relative Mean Bias Error (rMBE), Relative Root Mean Square Error (rRMSE), determination coefficient (R 2 ) and ''d" Willmott index were used to evaluate ANN and SVM models. When compared to statistical models which use the same set of input variables (R 2 between 0.22 and 0.78), ANN and SVM show higher values of R 2 (hourly models between 0.52 and 0.88; daily models between 0.42 and 0.91). Considering the input variables, atmospheric transmissivity of global radiation (kt), integrated solar constant (H sc ) and insolation ratio (n/N, n is sunshine duration and N is photoperiod) were the most relevant in ANN and SVM models. The rMBE and rRMSE values in the two time partitions of SVM models are lower than those obtained with ANN. Hourly ANN and SVM models have higher rRMSE values than daily models. Optimal performance with hourly models was obtained with ANN4 h (rMBE = 12.24%, rRMSE = 23.99% and ''d" = 0.96) and SVM4 h (rMBE = 1.75%, rRMSE = 20.10% and ''d" = 0.96). Optimal performance with daily models was obtained with ANN2 d (rMBE = À3.09%, rRMSE = 18.95% and ''d" = 0.97) and SVM2 d (rMBE = 0.60%, rRMSE = 19.39% and ''d" = 0.97). ANN and SVM models improved H b estimations as compared with other results from the literature. SVM has better performance than ANN to estimate H b , and it should be the first option of choice.

show abstract

“…The ANN technique analogously to the human nervous system has nodes in one or more layers, and connections, called synapses link these. According to Fiorin et al (2011), this method is capable of storing knowledge, and its use covers problems of adjustment functions, pattern recognition, predictive modeling and other applications in different areas. This method has a high capacity for self-organization and temporal processing that enables to solve various problems of high complexity.…”

Section: Artificial Neural Network Regression (Ann-regression)mentioning

confidence: 99%

Prediction of Tropospheric Ozone at Rio De Janeiro City From Primary Polluents and Meteorological Factors Using Artificial Neural Networks (Ann) and Support Vector Machine (Svm) Regression

Oliveira¹,

Luna

Paredes³

et al. 2015

Anais Do XX Congresso Brasileiro De Engenharia Química

View full text Add to dashboard Cite

Tropospheric ozone is well known as an extremely important factor due to its strong environmental impact. The air quality depends on emissions, meteorology and topography. NO 2 , NO, NOx, CO, O 3 , scalar wind speed, solar radiation, temperature, and relative humidity (HUM) were monitored. These data sets were collected by the mobile station monitoring located at PUC-Rio and UERJ between 2011 and 2012, from the Secretary of the Environment of Rio de Janeiro. This study aimed at the prediction of O 3 from primary pollutants and meteorological factors. The obtained results of ANN and SVM regression techniques were acceptable to the dataset UERJ presenting coefficient of determination (R 2) for validation, 0.9122 and 0.9152, and the square root of the mean square error of cross validation (RMECV) 7.66 and 7.85, respectively. Regarding the data set of PUC-Rio, the two techniques gave inferior results.

show abstract

Aplicações de redes neurais e previsões de disponibilidade de recursos energéticos solares

Cited by 18 publications

References 27 publications

Twenty-four Hours Ahead Solar Irradiance Forecast Based on Artificial Neural Network, ARIMA and SARIMA

Twenty-four Hours Ahead Solar Irradiance Forecast Based on Artificial Neural Network, ARIMA and SARIMA

Assessment of ANN and SVM models for estimating normal direct irradiation (Hb)

Prediction of Tropospheric Ozone at Rio De Janeiro City From Primary Polluents and Meteorological Factors Using Artificial Neural Networks (Ann) and Support Vector Machine (Svm) Regression

Contact Info

Product

Resources

About