Journal Article

PAR(p)-vine copula based model for stochastic streamflow scenario generation

Pereira, Guilherme, Veiga, Álvaro

2018

Overview

Synthetic streamflow data is vital for the energy sector, as it feeds stochastic optimisation models that determine operational policies. Considered scenarios should differ from each other, but be the same from a statistical point of view, i.e., the scenarios must preserve features of the original time series such as the mean, variance, and temporal dependence structures. Traditionally, linear models are applied for this task. Recently, the advent of copulas has led to the emergence of an alternative that overcomes the drawbacks of linear models. In this context, we propose a methodology based on vine copulas for the stochastic simulation of periodic streamflow scenarios. Copula-based models that focus on single-site inflow simulation only consider lag-one time dependence. Therefore, we suggest an approach that incorporates lags that are greater than one. Furthermore, the proposed model deals with the strong periodicity that is commonly present in monthly streamflow time series. The resulting model is a non-linear periodic autoregressive model. Our results indicate that this model successfully simulates scenarios, preserving features that are observed in historical data.

• Publisher: Springer Nature B.V
• Subject: Autoregressive models / Autoregressive processes / Computer simulation / Energy policy / Hydrologic data / Inflow / Periodicity / Scenario generation / Stream discharge / Stream flow / Time series / Water inflow