BUSCADOR RECOLECTA

Data related to electrical consumption and employment indicators of 2 buildings of the University of Girona. Data correspond to the buildins PI and PII of Escola Politècnica Superior from September 1, 2011 to October 14, 2014, Dades relacionades amb els consums elèctrics i els indicadors d’ocupació de 2 edificis de la Universitat de Girona. Les dades corresponen als edificis PI i PII de l'Escola Politècnica Superior des de l'1 de setembre de 2011 fins el 14 octubre de 2014

Data related to the electrical consumption of 7 non-residential buildings of the University of Girona. Data correspond to the electrical consumption of the buildings PI, PII, PII, PIV of the Escola Politècnica Superior and the faculties of Sciences, Law, and Economics from September 2011 to October 2014, Dades relacionades amb els consums elèctrics de 7 edificis no-residencials de la Universitat de Girona. Les dades corresponen als consums elèctrics del edificis PI, PII, PIII, PIV de l'Escola Politècnica Superior i de les facultats de Ciències, de Dret, i d'Econòmiques des de setembre de 2011 fins octubre de 2014

Dades associades a l'article publicat: Massana i Raurich, Joaquim ; Pous i Sabadí, Carles ; Burgas Nadal, Llorenç ; Meléndez i Frigola, Joaquim ; Colomer Llinàs, Joan. (2017). Identifying services for short-term load forecasting using data driven models in a Smart City platform. Sustainable Cities and Society, vol. 28, p.108-117. Disponible a https://doi.org/10.1016/j.scs.2016.09.001, Software: 1 Fitxer Zip; 7 Fitxers xlsx, Data related to the electrical consumption of 7 non-residential buildings of the University of Girona. Data correspond to the electrical consumption of the buildings PI, PII, PII, PIV of the Escola Politècnica Superior and the faculties of Sciences, Law, and Economics from September 2011 to October 2014, Dades relacionades amb els consums elèctrics de 7 edificis no-residencials de la Universitat de Girona. Les dades corresponen als consums elèctrics del edificis PI, PII, PIII, PIV de l'Escola Politècnica Superior i de les facultats de Ciències, de Dret, i d'Econòmiques des de setembre de 2011 fins octubre de 2014, This research project has been partially funded through BR-UdGScholarship of the University of Girona granted to Joaquim MassanaRaurich. Work developed with the support of the research groupSITES awarded with distinction by the Generalitat de Catalunya(SGR 2014–2016), the MESC project funded by the Spanish MINECO (Ref. DPI2013-47450-C2-1-R) and the European Union’s Horizon2020 Research and Innovation Programme under grant agreementNo 680708

Dades associades a l'article publicat: Massana i Raurich, J., Pous i Sabadí, C., Burgas Nadal, Ll., Meléndez i Frigola, J., Colomer Llinàs, J. (2015). Short-term load forecasting for non-residential buildings contrasting artificial occupancy attributes. Energy and Buildings, vol. 130, p. 519-531. Disponible a https://doi.org/10.1016/j.enbuild.2016.08.081, Software: 1 Fitxer Zip; 2 Fitxers xlsx, Data related to electrical consumption and employment indicators of 2 buildings of the University of Girona, Dades relacionades amb els consums elèctrics i els indicadors d’ocupació de 2 edificis de la Universitat de Girona, This research project has been partially funded through BR-UdG Scholarship ofthe University of Girona granted to Joaquim Massana Raurich. Work developed with the support of the research group SITES awarded with distinction by the Generalitat de Catalunya (SGR 2014-2016) and the MESC project funded by the Spanish MINECO (Ref. DPI2013-47450-C2-1-R), European Union’s Horizon2020 Research and Innovation Programme under grant agreementNo 680708

Dades primàries associades a una comunicació a congrés presentada a CIRED 2019. 25th International Conference on Electricity Distribution: Madrid, 3-6 June 2019, The files are in CSV, no special software required to interpret the data, The dataset includes two files: one called 'demand', with the active power demand from 10 different households, in Watts units and hourly resolution, and another called 'generation', with the 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. 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. 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. 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. 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. The dataset contains the data collected from 0:00 am to 11:00 pm on July 1, 2018