Simulation studies are carried out for the proposed model, the persistence model, and a RBF neural network- based model by using real wind speed and wind power data obtained from

Simulation studies are carried out for the proposed model, the persistence model, and a RBF neural network-based model by using real wind speed and wind power data obtained from

Ultra short term wind speed forecasting based on support vector machine with combined kernel function and similar data RESEARCH Open Access Ultra short term wind speed forecasting based

Short-term forecasting in power grids has been addressed with various methods, including machine learning (support vector machines [2], Deep Neural Networks [3, 4, 5]) and

To further testify the superiority of the proposed model over the persistence and RBF-SVM models, the three models were applied to predict wind power for 900 days (April 11,

Keywords: smart metering; short term electrictity forecasting; neural networks; support vector machines; forecast

He, Y, Liu, R, Li, H, Wang, S & Lu, X 2017, 'Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula

This paper proposed a hybrid method based on SVR and KH algorithm to predict the load data with more accuracy.. Therefore, in the first step, by the use of the training

If (Time is evening peak and Forecasted load is Medium and Temperature Deviation is cold and Temporal Availability is no) then Allowed Load is 2)(AC and Heater is

Keywords: short‑term electricity price forecasting, hybrid models, time series, ARIMA models, support vector regression, transmission congestion, Nord Pool electricity

For a real case study with 66 wind power plants, all the different sparse structures of the LASSO-VAR model shows a better performance than the Persistence, AR and VAR models, and

model is displayed and LSSVM gives the best result because 4 out of 6 MAPE values are the lowest. The improvement of MAPE accuracy in the use of wavelet functions only happen

The rest of the paper is organized as follows: section 2 briefly reports the wind speed/power forecasting in Vir- tual Power Players context; section 3 summarizes the energy

The meteorological parameters like wind speed, temperature, pressure, and air density are considered as input parameters collected from KL University area and

For a real case study with 66 wind power plants, all the different sparse structures of the LASSO-VAR model shows a better performance than the Persistence, AR and VAR models, and

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Application of hybrid RBF neural network ensemble model based on wavelet support vector machine regression in rainfall time series

a short-term wind power combined forecasting model based on error forecast correction 115.. can obtain