Sentence examples similar to multi step ahead from inspiring English sources

Currently, various techniques have been developed for multi-step ahead wind speed forecasting.

The LF-DFNN networks are used as advanced forecast models, providing multi-step ahead wind speed estimates from 15 min to 3 h ahead.

The results of this application demonstrate that the proposed methodology works well for the multi-step ahead wind speed and power forecasting.

The model bank is generated by linearizing the first principles model across a carefully designed trajectory based on accuracy of multi-step ahead predictions.

The efficiency of the proposed approach is tested on a real-world wind farm problem, where multi-step ahead wind speed estimates from 15 min to 3 h are sought.

In this paper, the experimental results in different wind farms and different seasons prove that the regression model considering the characteristics of multi-step ahead wind speed forecasting, task correlation and heteroscedasticity, will produce more accurate forecasts than the other models as for two to six-ahead wind speed forecasting.

In this paper, a two-level self-organizing map (SOM) clustering technique was used to identify spatially homogeneous clusters of precipitation satellite data, and to choose the most operative and effective data for a feed-forward neural network (FFNN) to model rainfall runoff process on a daily and multi-step ahead time scale.

Alongside with this study, Papacharalampous et al. (2017b) investigate the error evolution in multi-step ahead forecasting when adopting this specific set of methods.

Papacharalampous et al. (2017a) compare 11 stochastic and nine machine learning univariate time series forecasting methods in multi-step ahead forecasting of geophysical processes and (empirically) prove that stochastic and machine learning methods can perform equally well.

In this work, a novel multi-step ahead forecasting method based on multi-kernel learning is developed.

Thus, a correlation aware multi-step ahead wind speed forecasting technique with heteroscedastic multi-kernel learning is designed.