Sentence examples for forecasting I from inspiring English sources

In addition, growing interest in net demand forecasting, i.e., demand less generation, is another important motivation to combine PSPF and PLF into one review paper and assess compatibility.

Accurate very short-term forecasting (i.e., up to 12-hours-ahead) of the two resources could improve operational efficiency of power systems.

Regression models are in general fast-implemented in contrast to their computationally intensive alternative in precipitation and temperature forecasting, i.e. the GCMs.

Precipitation and temperature forecasting is mostly based on deterministic models as the Global Circulation Models (GCMs), which simulate the Earth's atmosphere using numerical equations; therefore, deviating from traditional time series forecasting, i.e. univariate time series forecasting.

Several strategies for handling model variability have been proposed in ensemble forecasting: (i) a single best model (providing the best fit to available data) is chosen, (ii) models are presented individually, and (iii) models are combined into a consensus prediction via model agreement, model central tendency (e.g., mean), or probability density functions [52].

Demand forecasting (i.e. determining the amount of medicine needed for country programs) is a challenging and complicated task [ 43] and insufficient focus has been placed on improving outdated procurement practices.

The above citations make it clear that when the aim is on air pollution forecasting, any forecast model ought to be better than the pure persistence forecast (i.e. forecasting for tomorrow what occurred today).

Moreover, to further improve the accuracy of short-term rainfall forecasts (i.e., 1- to 6-h rainfall forecasts), real-time radar observation data are included.

Fig. 6 (a) ROC curves for all probabilistic forecasts (i.e., 365 forecasts) produced by the three models in the trials of 2006 2007, 2008, and 2009.

According to this correction, the total number of earthquakes forecasted, i.e., the summation of λ0 and λn over the whole area, corresponds to Y.

Skilful forecasts (i.e. model efficiency index > 0) were achieved in 70% of the CARs for spring wheat and canola, and 43% for barley (low values observed in CAR with small harvested area).