Neural Stochastic Process Model Applied to Inflows Series

摘要

The generic model of stochastic process based on neural networks, called NeuralrnStochastic Process (NSP), was applied to the treatment of series of monthly inflows.rnThese series correspond to Affluent Natural Energy (ANE), which is the aggregationrnof the inflows to the plants, comprising a reservoir equivalent of a subsystem of NationalrnInterconnected System (NIS). The series of ANE presents temporal correlationrnand spatial correlation. The NSP model in its original version can capture the temporalrncorrelation, however, does not incorporate the spatial correlation of the series. Thisrnpaper presents a variant of the NSP model aimed at the incorporation of spatial correlationrnof the series of ANE. The results indicated that the model is able to capture thernbehavior of the time series of all NIS subsystems, providing different scenarios for thernnext 5 years that embody the same temporal and spatial correlation of the historicalrndata.