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Phase 3: SDR forecast interval generation.
Grey probable forecasts were subsequently generated from the SDR forecast interval and cross-checked with MDR forecast interval expectations.
The intention of carrying out the MDR analysis was to determine a region for which SDR forecast interval could intersect its forecast interval.
Once the parameters are obtained, the seismicity for the next forecast interval was simulated many times based on the ETAS model.
To produce forecasting calculations the DBM requires to take into account the triggering effect of seismicity occurred both before and during the forecast interval.
It has been found from the study made on the data that the accuracy of the forecast depends on the type of the model and the selection of forecast interval.
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b number of SDR forecast intervals were obtained or generated (as the case may be) by locating the FLV forecast state ( s_{b}^{f} ) using ( k_{b}^{f} ).
At the end of the SDR fuzzification and future pattern determination analysis, ( r_{f}^{*L} (i) ) were reversed and used in generating GFMAPR forecast intervals.
The maximum load-carrying rates of all branches are obtained with wind power scenarios generated within the forecast intervals by Monte Carlo simulation.
It is therefore possible to calculate forecast intervals for future observations, assuming that the baseline model holds in the future.
Forecasts obtained by using this model showed a notable improvement between day 275 and day 375, with observed data falling within 95percentt forecast intervals, and adequate goodness of fit (χ = 3.20, df = 2; p = 0.20).
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