aemo

<p>Wind power data from 22 wind farms in south eastern Australia provided by
the Australian Electricity Market Operator (AEMO) and pre-processed by Jethro 
Browell and Stefanos Delikaraoglou. The data set contains data from 2013
in 5 minute resolution and is a subset of the data set provided 
at http://dx.doi.org/10.15129/9e1d9b96-baa7-4f05-93bd-99c5ae50b141.
The columns are the wind farms sorted from west to east.
Meta data on the capacity and location of the wind farms can be accessed 
with <code>attr(aemo, "meta")</code>.</p>

datasets

Functions for recursive online fitting of time-adaptive lasso vector auto regression. A recursive coordinate descent algorithm is used to estimate sparse vector auto regressive models and exponential forgetting is applied to allow model changes. Details can be found in Jakob W. Messner and Pierre Pinson (2018). "Online adaptive LASSO estimation in Vector Auto Regressive models for wind power forecasting in high dimension". International Journal of Forecasting, in press. <doi:10.1016/j.ijforecast.2018.02.001>.

Jakob Messner

onlineVAR

Online Fitting of Time-Adaptive Lasso Vector Auto Regression

aemo function

<p>A data frame with 22 columns for each wind farm and 105120 rows.</p>

aemo: Wind power data from 22 wind farms in south eastern Australia

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