@inproceedings{35599,
  author = {Mario Souto and Alexandre Moreira and Alvaro Veiga and Alexandre Street and Joaquim Dias Garcia and Camila Epprecht},
  title = {A high-dimensional VARX model to simulate monthly renewable energy supply},
  abstract = {<p><span>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&nbsp;</span><span>1</span><span>&nbsp;-regularization. Simulations and forecasting results for a real case study involving fifty Brazilian renewable power plants are presented.</span></p>},
  year = {2014},
  journal = {2014 Power Systems Computation Conference (PSCC)2014 Power Systems Computation Conference},
  publisher = {IEEE},
  address = {Wrocław, Poland},
  url = {http://ieeexplore.ieee.org/document/7038460/},
  doi = {10.1109/PSCC.2014.7038460},
  language = {eng},
}