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

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

Research output: Contribution to journalArticlepeer-review

88 Citations (Scopus)
571 Downloads (Pure)

Abstract

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
Pages (from-to)657-675
Number of pages19
JournalWind Energy
Volume20
Issue number4
Early online date15 Mar 2017
DOIs
Publication statusPublished - 30 Apr 2017

Keywords

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

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