Neural Stochastic Process model applied to inflows series

The generic model of stochastic process based on neural networks, called Neural Stochastic Process (NSP), was applied to the treatment of series of monthly inflows. These series correspond to Affluent Natural Energy (ANE), which is the aggregation of the inflows to the plants, comprising a reservoir equivalent of a subsystem of National Interconnected System (NIS). The series of ANE presents temporal correlation and spatial correlation. The NSP model in its original version can capture the temporal correlation, however, does not incorporate the spatial correlation of the series. This paper presents a variant of the NSP model aimed at the incorporation of spatial correlation of the series of ANE. The results indicated that the model is able to capture the behavior of the time series of all NIS subsystems, providing different scenarios for the next 5 years that embody the same temporal and spatial correlation of the historical data.