Title: Probabilistic Wind Power Forecasting Model - Case Study Datasets
Citation
Xydas E, Qadrdan M, Marmaras C, et al. (2016). Probabilistic Wind Power Forecasting Model - Case Study Datasets. Cardiff University. https://doi.org/10.17035/d.2016.0012070323
Access Rights: Creative Commons Attribution 4.0 International
Access Method: Click to email a request for this data to opendata@cardiff.ac.uk
Cardiff University Dataset Creators
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 The file named “Outputs.mat’ Once opened, a file of type 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” Column headings are provided to help understand its column. The actual Research results based upon these data are published at
Description
“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.
is related to model outputs. This file can be opened only if you have installed
MATLAB at your system.
“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:
shows the generated 1000 weekly forecasts, when using 10 Magnitude classes for
the training and updating the model every 16 half hours.
data are provided in order to compare the generated forecasts with them and
evaluate the performance of the model.
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