%0 Conference Paper %A Mario Souto %A Alexandre Moreira %A Alvaro Veiga %A Alexandre Street %A Joaquim Dias Garcia %A Camila Epprecht %B 2014 Power Systems Computation Conference (PSCC)2014 Power Systems Computation Conference %C Wrocław, Poland %D 2014 %G eng %I IEEE %R 10.1109/PSCC.2014.7038460 %T A high-dimensional VARX model to simulate monthly renewable energy supply %U http://ieeexplore.ieee.org/document/7038460/ %X

This paper proposes a novel framework for forecasting and simulating renewable energy on a long-term horizon. In this regard, it is presented a monthly multivariate stochastic model for wind and hydro inflow as well as an efficient estimation method for high-dimensional data. Firstly, in order to model the inherent uncertainty of renewable energy supplies, this work proposes a high-dimensional VARX with periodic variance. Secondly, an estimation procedure is suggested based on the maximum likelihood criterion with endogenous variable selection. Due to the presence of multicollinearity and high-dimensionality, the estimation procedure proposed in this work has two main features: (i) a fixed-point algorithm to pursue the maximum likelihood estimators under periodic heteroskedasticity (ii) obtain a sparse solution by means of ℓ 1 -regularization. Simulations and forecasting results for a real case study involving fifty Brazilian renewable power plants are presented.