NeuroInflow: the new model to forecast average monthly inflow

摘要

In utilities using a mixture of hydroelectric and nonhydroelectric power, the economics of the hydroelectric plants depend upon the reservoir height and the inflow into the reservoir for several months into the future. Accurate forecasts of reservoir inflow allow the utility to feed proper amounts of fuel to individual plants, and to economically allocate the load between various nonhydroelectric plants. For this reasons, several companies in the Brazilian Electrical Sector use the linear time-series models such as PARMA (Periodic Auto regressive Moving Average) models developed by Box-Jenkins. This paper provides for river flow prediction a numerical comparison between non-linear sigmoidal regression Blocks networks (NSRBN), called NeuroInflow and PARMA models. The model was implemented to forecast monthly average inflow with a long-term prediction horizon (one to twelve months ahead). It was tested on 37 hydroelectric plants located in different river basins in Brazil. The results obtained in the evaluation of the performance of NeuroInflow were better than the results obtained with PARMA models.