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

• Main Authors: Brigatto, Arthur, Street, Alexandre, Fernandes, Cristiano, Valladao, Davi, Bodin, Guilherme, Garcia, Joaquim Dias
• Format: Journal Article
• Language: English
• Published: 17-10-2024
• Subjects: Computer Science - Systems and Control; Statistics - Applications
• DOI: 10.48550/arxiv.2410.13763

Hydroelectricity accounted for roughly 66% of the total generation in Brazil
in 2023 and addressed most of the intermittency of wind and solar generation.
Thus, one of the most important steps in the operation planning of this country
is the forecast of the natural inflow energy (NIE) time series, an
approximation of the energetic value of the water inflows. To manage water
resources over time, the Brazilian system operator performs long-term forecasts
for the NIE to assess the water values through long-term hydrothermal planning
models, which are then used to define the short-term merit order in day-ahead
scheduling. Therefore, monitoring optimistic bias in NIE forecasts is crucial
to prevent an optimistic view of future system conditions and subsequent
riskier storage policies. In this article, we investigate and showcase strong
evidence of an optimistic bias in the official NIE forecasts, with predicted
values consistently exceeding the observations over the past 12 years in the
two main subsystems (Southeast and Northeast). Rolling window out-of-sample
tests conducted with real data demonstrate that the official forecast model
exhibits a statistically significant bias of 6%, 13%, 18%, and 23% for 1, 6,
12, and 24 steps ahead in the Southeast subsystem, and 19%, 57%, 80%, and 108%
in the Northeast.