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

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Cavalcante, L. and Bessa, Ricardo J. and Reis, Marisa and Browell, Jethro (2017) LASSO vector autoregression structures for very short-term wind power forecasting. Wind Energy, 20 (4). pp. 657-675. ISSN 1095-4244 (https://doi.org/10.1002/we.2029)

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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

ORCID iDs

Cavalcante, L., Bessa, Ricardo J., Reis, Marisa and Browell, Jethro ORCID logoORCID: https://orcid.org/0000-0002-5960-666X;
  • Item type:Article
    ID code:57829
    Dates:
    Date
    Event
    30 April 2017
    Published
    15 March 2017
    Published Online
    23 August 2016
    Accepted
    8 March 2016
    Submitted
    Notes:Copyright © 2016 John Wiley & Sons, Ltd. Cavalcante, L., Bessa, R. J., Reis, M., and Browell, J. (2017) LASSO vector autoregression structures for very short-term wind power forecasting. Wind Energ., 20: 657–675. doi: 10.1002/we.2029 This is the peer reviewed version of the following article: Cavalcante, L., Bessa, R. J., Reis, M., & Browell, J. (2016). LASSO vector autoregression structures for very short-term wind power forecasting. Wind Energy, which has been published in final form at http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-1824. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:16 Sep 2016 13:59
    Last modified:12 Dec 2024 17:29
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/57829
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