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The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by researchers from the Department of Computer & Information Sciences involved in mathematically structured programming, similarity and metric search, computer security, software systems, combinatronics and digital health.

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

Cavalcante, L. and Bessa, Ricardo J. and Reis, Marisa and Browell, Jethro (2016) LASSO vector autoregression structures for very short-term wind power forecasting. Wind Energy. ISSN 1095-4244 (In Press)

[img] Text (Cavalcante-etal-WE2016-LASSO-vector-autoregression-structures-for-very-short-term-wind-power-forecasting)
Cavalcante_etal_WE2016_LASSO_vector_autoregression_structures_for_very_short_term_wind_power_forecasting.pdf - Accepted Author Manuscript
Restricted to Repository staff only until 29 August 2017.

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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