This file was generated on 2020-11-06 by Marc Cañigueral


--------------------
GENERAL INFORMATION
--------------------


1. Title of Dataset: Buildings energy demand

2. Author Information (repeatable):

Name: Marc Cañigueral Maurici		
Institution: Universitat de Girona
Email: marc.canigueral@udg.edu
ORCID: https://orcid.org/0000-0001-9724-5829

Name: Ferran Torrent-Fontbona		
Institution: Universitat de Girona
Email: ferran.torrent@udg.edu
ORCID: https://orcid.org/0000-0002-3359-3979

Name: Joaquim Meléndez Frigola	
Institution: Universitat de Girona
Email: joaquim.melendez@udg.edu
ORCID: https://orcid.org/0000-0001-8891-6854

------------
DESCRIPTION
------------

1. Abstract for the dataset 
The dataset consists on two files:
- demand: active power demand from 10 different households, in Watts units and hourly resolution.
- generation: solar generation considering the current installed photovoltaic power in each household, in Watts units and hourly resolution. The total peak power installed is 30.5 kWp.


2. Date of data collection:
2018-07-01

3. Geographic location of data collection:
Catalunya, Spain

4. Funding sources that supported the collection of the data (repeatable):
European Commission, Horizon 2020 grant.

Funding: European Commission (EC)
Project code: 773715

--------------------
ACCESS INFORMATION
-------------------- 


1. License Creative Commons /restrictions placed on the data:

[Mandatory | The recommended license is CC-0. See the recommended CC licenses in (pending license group. At the moment you can consult http://www2.udg.edu/projectesbiblioteca/BibliotecaiRecerca/Propietatintel%C2%B7lectual/CreativeCommons/tabid/21008/language/ca-ES/Default. aspx)]
CCBY

2. Dataset Digital Object Identifier (Handle/DOI):
[Mandatory | included by the Library]
10.34810/data19

3. Publications related to the dataset
Marc Cañigueral, Ferrant Torrent and Joaquim Meléndez. Impact of batteries in the hosting capacity of a grid with photovoltaic generation.

DOI/hdl/URL: http://hdl.handle.net/10256/16682

4. Links to other publicly accessible locations of the data (repeatable):
[Recommended if Applicable |Ex. other data sets of the same project: websites, Zenodo, Figshare, etc.)]

DOI/URL:

5. Limitations to reuse:
[Mandatory if Applicable | in case of not being open data]


--------------------------
VERSIONING AND PROVENANCE
-------------------------- 

1. Last modification date:
2018-09-19

2. Links/relationships to other versions of this dataset:
None


---------------------------
METHODOLOGICAL INFORMATION
---------------------------


1. Description of methods used for collection/generation of data: 
The demand dataset was provided by the DSO (Distribution System Operator). No collection/generation work done by the researchers.
The solar generation profile was download from PVGIS portal, considering the location and the installed peak power of each household.


2. Methods for processing the data: 
The study multiplies the data profiles (demand and generation) from the Dataset by different factors according to the scenario. Not other preprocessing than scaling the data.


3. Software specific information needed to interpret the data:
The files are in CSV, no special software required to interpret the data.


---------------
FILE OVERVIEW
---------------


1. List of all files included in the DataSet
Filename: demand.csv        
Short description: Active power demand from 10 different households, in Watts units and hourly resolution.

Filename: generation.csv
Short description: Solar generation considering the current installed PV power in each household, in Watts units and hourly resolution. The total peak power installed is 30.5 kWp.
     

2. Relationship between files:
Energy consumption profiles from file 'demand.csv' correspond to the same households than the energy production profiles from file 'generation.csv'.
For example, 'C1' column in 'demand.csv' and 'G1' column in 'generation.csv' correspond to consumption and generation profiles from Household 1, respectively.


3. File formats:
CSV


-------------------

OTHER INFORMATION YOU CONSIDER RELEVANT:
-------------------
In the study the authors talk about two scenarios. The Scenario 1 considers the current demand profiles, from file 'demand.csv'. The Scenario 2, considers a future high electrification of households final demand, so the demand profiles in file 'demand.csv' are scaled by a factor of 14.
In both scenarios, all generation profiles in 'generation.csv' are scaled by a factor depending on the total installed photovoltaic power, considering than the original generation profiles correspond to a total peak power of 30.5 kWp.