arXiv subjects

Cristiano Fernandes

Publications and source records attributed to Cristiano Fernandes.

3 recordsLinked to original sources

arXiv2025

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 1.28, 3.83, 5.39, and 6.73 average GW for 1-, 6-, 12-, and 24-step-ahead forecasts in the Southeast subsystem, and 0.54, 1.66, 2.32, and 3.17 average GW 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.

eess.SY↗

arXiv2021

A Multi-Quantile Regression Time Series Model with Interquantile Lipschitz Regularization for Wind Power Probabilistic Forecasting

Modern decision-making processes require uncertainty-aware models, especially those relying on non-symmetric costs and risk-averse profiles. The objective of this work is to propose a dynamic model for the conditional non-parametric distribution function (CDF) to generate probabilistic forecasts for a renewable generation time series. To do that, we propose an adaptive non-parametric time-series model driven by a regularized multiple-quantile-regression (MQR) framework. In our approach, all regression models are jointly estimated through a single linear optimization problem that finds the global-optimal parameters in polynomial time. An innovative feature of our work is the consideration of a Lipschitz regularization of the first derivative of coefficients in the quantile space, which imposes coefficient smoothness. The proposed regularization induces a coupling effect among quantiles creating a single non-parametric CDF model with improved out-of-sample performance. A case study with realistic wind-power generation data from the Brazilian system shows: 1) the regularization model is capable to improve the performance of MQR probabilistic forecasts, and 2) our MQR model outperforms five relevant benchmarks: two based on the MQR framework, and three based on parametric models, namely, SARIMA, and GAS with Beta and Weibull CDF.

stat.AP↗

arXiv2020

ScoreDrivenModels.jl: a Julia Package for Generalized Autoregressive Score Models

Score-driven models, also known as generalized autoregressive score models, represent a class of observation-driven time series models. They possess powerful properties, such as the ability to model different conditional distributions and to consider time-varying parameters within a flexible framework. In this paper, we present ScoreDrivenModels.jl, an open-source Julia package for modeling, forecasting, and simulating time series using the framework of score-driven models. The package is flexible with respect to model definition, allowing the user to specify the lag structure and which parameters are time-varying or constant. It is also possible to consider several distributions, including Beta, Exponential, Gamma, Lognormal, Normal, Poisson, Student's t, and Weibull. The provided interface is flexible, allowing interested users to implement any desired distribution and parametrization.

stat.CO↗