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Flexible Network Data Share

Flexible Networks for a Low Carbon Future (or Flexible Networks for short) was SP Energy Networks first major Low Carbon Network innovation trial project, generating real learning from real networks.

We are pleased to be able to make available for further research and analysis the source data from the network monitoring carried out for this project.

The Flexible Networks project data consists of network monitored data at three trial sites – St Andrews, Whitchurch and Ruabon.  Before making a request for the detailed network data, we recommend that you make yourself familiar with the context of the network data, which can be obtained from the various project study reports.  The volume of raw data is vast (>2TB), so it is worth becoming familiar with the scope of the data and its applicability to your needs before embarking on any major data download.

If you would like to use this data, all that we ask is that any work based on this data, including any published papers, should acknowledge the use of the Flexible Networks project data.  We are also keen to hear about your findings, so please come back to us once you have your results.

To access the dataset, please fill out the simple form below and we will get back to you.

 

Flexible Networks for a Low Carbon Future (or Flexible Networks for short) was SP Energy Networks first major Low Carbon Network innovation trial project, generating real learning from real networks.  We are pleased to be able to make available for further research and analysis the source data from the network monitoring carried out for this project.

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This data is provided free of charge under a royalty-free non-exclusive license on the understanding that the Flexible Networks project is attributed in the publication of any results.

Disclaimer

Whilst every effort has been made to ensure the varasity of the data, no warranty is given or should be implied as to its overall quality.  This is essentially raw data.  We have carried out some post processing to correct known data errors, but this is real-world network-monitored data, so there will undoubtedly be errors in it that we have not detected.  For example, there will be gaps in the datasets where data has been lost for some reason. Therefore, it is recommended that the user performs their own quality checks and error correction on the data before carrying out any detailed analysis.