Projects per year
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
Original language | English |
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Pages (from-to) | 657-675 |
Number of pages | 19 |
Journal | Wind Energy |
Volume | 20 |
Issue number | 4 |
Early online date | 15 Mar 2017 |
DOIs | |
Publication status | Published - 30 Apr 2017 |
Keywords
- wind power
- vector autoregression
- scalability
- sparse
- renewable energy
- parallel computing
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Dive into the research topics of 'LASSO vector autoregression structures for very short-term wind power forecasting'. Together they form a unique fingerprint.Projects
- 1 Finished
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Doctoral Training Partnership (DTP - University of Strathclyde)
McFarlane, A. (Principal Investigator)
EPSRC (Engineering and Physical Sciences Research Council)
1/10/15 → 30/09/19
Project: Research - Studentship
Prizes
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Glasgow Research Partnership in Engineering: Postdoctoral Exchange
Browell, J. (Recipient), 2015
Prize: To be assigned