Content
The topic for the wind forecasting track is focused on mimicking the operation 48-hour ahead prediction of hourly power generation at 7 wind farms, based on historical measurements and additional wind forecast information (48-hour ahead predictions of wind speed and direction at the sites). The data is available from the 1st hour of 2009/7/1 to the 12th hour of 2012/6/28.
This is based on GEF2012 - Wind Forecasting.
You are free to use the tool you prefer to estimate models and produce forecasts.
Timeline
The competition will take place by the end of the course lectures and you will have 3 weeks to complete the requirements.
The full dataset will be made available by the Professor.
The last lecture of the course will be dedicated to the presentations of the results.
Requirements
This is a team competition.
You are required to produce a notebook to present your whole project, from methodologies used to the results obtained, carefully explaining your approaches.
In particular, you have to report:
- list of forecasting methods used
- accuracy results on test set for each time series and each method
using RMSE
- best accuracy results on test set for each time series using
RMSE
- average accuracy result on test set (Average RMSE)
- total computation time required to make the computations with system
information
- total time spent on developing the project
The evaluation part is aimed at mimicking real operational conditions. For that, a number of 48-hour periods with missing power observations were defined. All these power observations are to be predicted. These periods are defined as follows. The first period with missing observations is from 2011/1/1 at 01:00 until 2011/1/3 at 00:00. The second period with missing observations is from 2011/1/4 at 13:00 until 2011/1/6 at 12:00.
Note that to be consistent, only the meteorological forecasts for that period that would actually be available in practice are given. These two periods then repeat every 7 days until the end of the dataset. In between periods with missing data, power observations are available for updating the models.
Finally, you are required to submit a “groupname_submission.csv” at least two days before the presentation.
You need to score an RMSE <= 0.9 on the test data to pass this assignment.
Data
The period between 2009/7/1 and 2010/12/31 is a model identification and training period, while the remainder of the dataset, from 2011/1/1 to 2012/6/28, is there for evaluation. The training period will be used for designing and estimating models permitting predicting wind power generation at lead times from 1 to 48 hours ahead, based on past power observations and/or available meteorological wind forecasts for that period.
The file “train.csv” contains the training data: - the first column
(“date”) is a timestamp giving the date and time of the hourly wind
power measurements in the following columns. For instance “2009070812”
is for the 8th of July 2009 at 12:00
- the following 7 columns (“wp1” to “wp7”) gather the normalized wind
power measurements for the 7 wind farms. They are normalized so as to
take values between 0 and 1 in order for the wind farms not to be
recognizable.
In parallel, files with explanatory variables (wind forecasts) are
also provided for those who may want to use them. For example, the file
“windforecasts_wf1.csv” contains the wind forecasts for the wind farm 1.
In these files:
- the first column (“date”) is a timestamp giving the date and time at
which the forecasts are issued. For instance “2009070812” is for the 8th
of July 2009 at 12:00
- the second column (“hors”) is for the lead time of the forecast. For
instance if “date” = 2009070812 and “hors” = 1, the forecast is for the
8th of July 2009 at 13:00
- the following 4 columns (“u”, “v”, “ws” and “wd”) are the forecasts
themselves, the first two being the zonal and meridional wind
components, while the following two are corresponding wind speed and
direction.
Finally, the file “template.csv” provide example forecast results from a forecast method. This file gives a template for the submission of results that should be strictly followed.