Assessing the Optimistic Bias in the Natural Inflow Forecasts: A Call for Model Monitoring in Brazil

Hydroelectricity accounted for roughly 61.4% of Brazil's total generation in 2024 and addressed most of the intermittency of wind and solar generation. Thus, inflow forecasting plays a critical role in the operation, planning, and market in this country, as well as in any other hydro-dependent power system. These forecasts influence generation schedules, reservoir management, and market pricing, shaping the dynamics of the entire electricity sector. The objective of this paper is to measure and present empirical evidence of a systematic optimistic bias in the official inflow forecast methodology, which is based on the PAR(p)-A model. Additionally, we discuss possible sources of this bias and recommend ways to mitigate it. By analyzing 14 years of historical data from the Brazilian system through rolling-window multistep (out-of-sample) forecasts, results indicate that the official forecast model exhibits statistically significant biases of 6%, 14%, 20%, and 24% for 1-, 6-, 12-, and 24-step-ahead forecasts in the Southeast subsystem, and 19%, 57%, 81%, and 109% in the Northeast subsystem. These findings uncover the limitations of current inflow forecasting methodologies used in Brazil and call for new governance and monitoring policies.