### Abstract

Original language | English |
---|---|

Number of pages | 24 |

Journal | Wind Energy |

Publication status | Accepted/In press - 23 Aug 2016 |

### Fingerprint

### Keywords

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

### Cite this

*Wind Energy*.

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*Wind Energy*.

**LASSO vector autoregression structures for very short-term wind power forecasting.** / Cavalcante, L.; Bessa, Ricardo J.; Reis, Marisa; Browell, Jethro.

Research output: Contribution to journal › Article

TY - JOUR

T1 - LASSO vector autoregression structures for very short-term wind power forecasting

AU - Cavalcante, L.

AU - Bessa, Ricardo J.

AU - Reis, Marisa

AU - Browell, Jethro

N1 - 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.

PY - 2016/8/23

Y1 - 2016/8/23

N2 - 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

AB - 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

KW - wind power

KW - vector autoregression

KW - scalability

KW - sparse

KW - renewable energy

KW - parallel computing

UR - http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-1824

M3 - Article

JO - Wind Energy

JF - Wind Energy

SN - 1095-4244

ER -