Evaluation of Monthly Inflow Forecasting Models for the Planning and Management of the Brazilian Hydropower System

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Abstract
The Brazilian interconnected power system consists of 153 medium and large hydropower plants, and is complemented by a large set of small hydropower, thermal and wind power plants. Hydropower is the predominant source of energy supply and on average represents 85% of total energy consumption in the country during the last ten years. Inflow forecasts are required for the planning and operation of the power system. The current practice is to utilize a set of streamflow forecasting models to predict reservoir inflows, which are used as inputs to the optimization models. The purpose of the study is to evaluate the accuracy of the forecasting models that are being used by the electric system national operator (ONS). We use statistical analysis by comparing the forecasts with historical observations at various reservoir sites and different seasons. Short-term forecasts are important for real-time operation, mainly for flood control and unit commitment. For monthly planning and operation, the hydro system optimization model uses deterministic reservoir inflow forecasts for the first month and different scenarios to represent future possible inflows after the first month until the end of the planning horizon. The optimization model is solved by stochastic programming with recourse and is updated at the beginning of each month. This paper evaluates the accuracy of monthly inflow forecasts for all the medium and large hydropower plants in Brazil. We intend to answer the following questions. How is the goodness of fit among different periods and river basins? How can the forecasting models be improved?

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