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The increasing shares of renewable energy sources at low voltage distribution nodes are the cause of increased operational uncertainty. This uncertainty must also be taken into account during operational planning for the short term period, i.e. up to five days ahead. Therefore the system operators must take into account how low-voltage load as well as generation change at each node in the near future...
Modeling of non-linearity and uncertainty associated with rainfall-runoff process has received a lot of attention in the past years. Recently artificial intelligence techniques are used for hydrological time series modelling. Earlier studies showed this approach is effective, still there are concerns about how these techniques perform efficiently to predict the run-off with high standard of accuracy...
Stock market forecasting has attracted a lot of research interests in previous literature. Traditionally, the autoregressive moving average (ARMA) model has been one of the most widely used linear models in time series forecasting. However, the ARMA model cannot easily capture the nonlinear patterns. And recent studies have shown that artificial neural networks (ANN) method achieved better performance...
Using the projection theory and modern time series analysis method, based on the autoregressive moving average (ARMA) innovation model and white noise estimators, the reduced-order Wiener state estimators for descriptor system with MA colored observation noise and multi-observation lags are presented. They can handle the prediction, filtering and smoothing in a unified framework. They avoid the solutions...
In order to cope with nonlinear time series, a variable structure radial basis function (RBF) networks model, in which the numbers of basis functions and input order vary over time, is proposed in this paper. Then sequential Monte Carlo (SMC) method is used for time series on-line prediction and corresponding algorithm is developed. At last, the data of weekly price of the shipbuilding steel product...
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