Title: Probabilistic Wind Power Forecasting Model - Case Study Datasets

Xydas E, Qadrdan M, Marmaras C, et al. (2016). Probabilistic Wind Power Forecasting Model - Case Study Datasets. Cardiff University. http://doi.org/10.17035/d.2016.0012070323

Access Rights: Data is provided under a Creative Commons Attribution (CC BY 4.0) licence

Access Method: Click to email a request for this data to opendata@cardiff.ac.uk

Dataset Details

Publisher: Cardiff University

Date (year) of data becoming publicly available: 2016

Data format: .xlsx, .mat, .docx

Software Required: Microsoft Office (Excel), Matlab

Estimated total storage size of dataset: Less than 1 gigabyte

Number of Files In Dataset: 2

DOI : 10.17035/d.2016.0012070323

DOI URL: http://doi.org/10.17035/d.2016.0012070323


Two datasets are provided.

The file named
InputData.xlsx’ is related to the wind power data used to train the model. The
data consisted of half-hourly aggregated wind power values from wind farms
across the Great Britain for the period of 01/03/2014–30/9/2014. Column
headings are included to explain the variables.

The file named “Outputs.mat’
is related to model outputs. This file can be opened only if you have installed
MATLAB at your system.

Once opened, a file of type
“struct” includes all the forecast for different cases. This file is divided
into fields, each of them representing a different scenario. So, the fields are
divided based on the:

Number of Generated Forecasts;

Number of Magnitude Classes used for the training process;

The update frequency of the model (forecasting process)

For example the file “Outputs.Num_Of_Forecasts1000.MagnitudeClasses10.UpdateFreq16
shows the generated 1000 weekly forecasts, when using 10 Magnitude classes for
the training and updating the model every 16 half hours.

Column headings are provided to help understand its column. The actual
data are provided in order to compare the generated forecasts with them and
evaluate the performance of the model.

Research results based upon these data are published at

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Last updated on 2021-22-06 at 15:36