Ahead of Print article · Journal article
Correlation-constrained and sparsity-controlled vector autoregressive model for spatio-temporal wind power forecasting
China Agricultural University1
Department of Electrical Engineering, Technical University of Denmark2
Center for Electric Power and Energy, Centers, Technical University of Denmark3
Energy Analytics and Markets, Center for Electric Power and Energy, Centers, Technical University of Denmark4
China Electric Power Research Institute5
CITIES - Centre for IT-Intelligent Energy Systems, Centers, Technical University of Denmark6
The ever-increasing number of wind farms has brought both challenges and opportunities in the development of wind power forecasting techniques to take advantage of interdependenciesbetweentensorhundredsofspatiallydistributedwind farms, e.g., over a region. In this paper, a Sparsity-Controlled Vector Autoregressive (SC-VAR) model is introduced to obtain sparse model structures in a spatio-temporal wind power forecasting framework by reformulating the original VAR model into a constrained Mixed Integer Non-Linear Programming (MINLP) problem.
It allows controlling the sparsity of the coefficient matrices in direct manner. However this original SC-VAR is difficult to implement due to its complicated constraints and the lack of guidelines for setting its parameters. To reduce the complexity of this MINLP and to make it possible to incorporate prior expert knowledge to benefit model building and forecasting, the original SC-VAR is modified and a Correlation-Constrained SC-VAR (CCSC-VAR) is proposed based on spatial correlation information about wind farms.
Our approach is evaluated based on a case study of very-short-term forecasting for 25 wind farms in Denmark. Comparison is performed with a set of traditional local methods and spatio-temporal methods. The results obtained show the proposed CCSC-VAR has better overall performance than both the original SC-VAR and other benchmark methods, taking into account all evaluation indicators, including sparsitycontrol ability, sparsity, accuracy and efficiency
Language: | English |
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Publisher: | IEEE |
Year: | 2018 |
Pages: | 5029-5040 |
ISSN: | 15580679 and 08858950 |
Types: | Ahead of Print article and Journal article |
DOI: | 10.1109/TPWRS.2018.2794450 |
ORCIDs: | 0000-0001-6361-6839 , 0000-0001-9142-9467 , 0000-0002-3490-6543 and Pinson, Pierre |