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

Cavalcante, L., Bessa, R. J., Reis, M. and Browell, J. (2017) LASSO vector autoregression structures for very short-term wind power forecasting. Wind Energy, 20(4), pp. 657-675. (doi: 10.1002/we.2029)

Abstract

The deployment of smart grids and renewable energy dispatch centers motivates the development of forecasting techniquesthat take advantage of near real-time measurements collected from geographically distributed sensors. This paper describesa forecasting methodology that explores a set of different sparse structures for the vector autoregression (VAR) model usingthe least absolute shrinkage and selection operator (LASSO) framework. The alternating direction method of multipliers isapplied to fit the different LASSO-VAR variants and create a scalable forecasting method supported by parallel computingand fast convergence, which can be used by system operators and renewable power plant operators. A test case with 66wind power plants is used to show the improvement in forecasting skill from exploring distributed sparse structures. Theproposed solution outperformed the conventional autoregressive and vector autoregressive models, as well as a sparse VARmodel from the state of the art.

• Item Type: Articles
• Additional Information: This work was made in the framework of the SusCity project (contract no. ‘MITP-TB/CS/0026/2013’) financed by nationalfunds through Fundação para a Ciência e a Tecnologia (FCT), Portugal. Jethro Dowell is supported by the University ofStrathclyde’s EPSRC Doctoral Prize, grant number EP/M508159/1.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Browell, Dr Jethro
• Authors: Cavalcante, L., Bessa, R. J., Reis, M., and Browell, J.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Wind Energy
• Publisher: Wiley
• ISSN: 1095-4244
• ISSN (Online): 1099-1824
• Published Online: 19 September 2016