LASSO vector autoregression structures for very short-term wind power forecasting

L. Cavalcante, Ricardo J. Bessa, Marisa Reis, Jethro Browell

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The deployment of smart grids and renewable energy dispatch centers motivates the development of forecasting techniques that take advantage of near real-time measurements collected from geographically distributed sensors. This paper describes a forecasting methodology that explores a set of different sparse structures for the vector autoregression (VAR) model using the Least Absolute Shrinkage and Selection Operator (LASSO) framework. The alternating direction method of multipliers is applied to fit the different VAR-LASSO variants and create a scalable forecasting method supported by parallel computing and fast convergence, which can be used by system operators and renewable power plant operators. A test case with 66 wind power plants is used to show the improvement in forecasting skill from exploring distributed sparse structures. The proposed solution outperformed the conventional autoregressive and vector autoregressive models, as well as a sparse-VAR model from the state of the art.LASSO Vector Autoregression Structures for Very Short-term Wind Power Forecasting
Original languageEnglish
Number of pages24
JournalWind Energy
Publication statusAccepted/In press - 23 Aug 2016


  • wind power
  • vector autoregression
  • scalability
  • sparse
  • renewable energy
  • parallel computing


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