Detalhes do Documento

This paper presents a novel time-adaptive quantile-copula estimator for kernel density forecast and a discussion of how to select the adequate kernels for modeling the different variables of the problem. Results are presented for different case-studies and compared with splines quantile regression (QR). The datasets used are from NREL's Eastern Wind Integration and Transmission Study, and from a real wind farm located in the Midwest region of the United States. . The new probabilistic forecasting model is elegant and simple and yet displays advantages over the traditional QR approach. Especially notable is the quality of the results achieved with the time-adaptive version, namely when evaluated in terms of forecast calibration, which is a characteristic that is advantageous for both system operators and wind power producers.