Description
Dataset (CSV format) used in paper [1]. 5 minute resolution wind power data from 22 wind farms in south-east Australia from 2012-2013 made publicly available by the Australian Electricity Market Operator (AEMO). Processed by Stefanos Delikaraoglou, Pierre Pinson and Jethro Dowell (please acknowledge).
[1] J. Dowell, P. Pinson, "Very-short-term Probabilistic Wind Power Forecasts by Sparse Vector Autoregression," IEEE Transactions on Smart Grid, accepted, 2015, (pre-print), DOI: 10.1109/TSG.2015.2424078
[1] J. Dowell, P. Pinson, "Very-short-term Probabilistic Wind Power Forecasts by Sparse Vector Autoregression," IEEE Transactions on Smart Grid, accepted, 2015, (pre-print), DOI: 10.1109/TSG.2015.2424078
| Date made available | 2015 |
|---|---|
| Publisher | University of Strathclyde |
| Temporal coverage | 1 Jan 2012 - 31 Dec 2013 |
| Date of data production | 1 Feb 2014 |
| Geographical coverage | South-east Australia |
Research output
- 1 Article
-
Very-short-term probabilistic wind power forecasts by sparse vector autoregression
Dowell, J. & Pinson, P., 31 Mar 2016, In: IEEE Transactions on Smart Grid. 7, 2, p. 763-770 8 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile297 Citations (Scopus)305 Downloads (Pure)
Projects
- 1 Finished
-
Doctoral training centre in wind energy systems | Browell, Jethro
Browell, J. (Research Co-investigator)
EPSRC (Engineering and Physical Sciences Research Council)
1/10/11 → 24/09/15
Project: Research Studentship - Internally Allocated
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- DataSetCite