Open AccessLASSO vector autoregression structures for very short‐term wind power forecasting
Author(s) -
Cavalcante Laura,
Bessa Ricardo J.,
Reis Marisa,
Browell Jethro
Publication year - 2017
Publication title -
wind energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.743
H-Index - 92
eISSN - 1099-1824
pISSN - 1095-4244
DOI - 10.1002/we.2029
Subject(s) - lasso (programming language) , autoregressive model , wind power forecasting , vector autoregression , wind power , renewable energy , computer science , convergence (economics) , scalability , term (time) , mathematical optimization , autoregressive integrated moving average , time series , econometrics , power (physics) , electric power system , machine learning , engineering , mathematics , economics , physics , quantum mechanics , database , world wide web , economic growth , electrical engineering
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 LASSO‐VAR 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. Copyright © 2016 John Wiley & Sons, Ltd.