Databases

Links to useful databases

• KEEL (Knowledge Extraction Evolutionary Learning): datasets for supervised classification. More info in this paper

• PGLib-OPF (Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms): also for testing Unit Commitment. Replaces the previous NESTA library and is maintained by an official IEEE PES Task Force. More in this paper.

• Data portals of European Transmission System Operators (TSOs): RTE (France), REE (Spain), Energinet (Denmark)

• Data portal of European Network of Transmission System Operators for Electricity (ENTSO-e)

• Benchmark data for unit commitment problem from Argonne National Laboratory (link)

• Unit commitment data by Diego Tejada (link)

• Rensmart: data for wind and solar production in different locations of UK (link)

• Ninja database of renewables (link)

• Kaggle hourly weather data (link)

• Low Carbon London (UK Power Networks): smart metering project to conduct behaviour trials. Over five thousands households in the London area, consumption data, time-of-use tariff data, and survey (link)

• Pecan Street: experiment conducted in Austin, it contains minute-level electricity consumption data and individually monitored appliance circuits (link)

• Ausgrid Substation: 300 residential customers in an Australian distribution network, smart meter data combined with rooftop PV generation (link)

• Customer Behaviour Trials (Commission for Energy Regulation): smart metering project to conduct customer trials in Ireland. It tooks place during 2009 and 2010 with over 5000 Irish homes and business participating (ISSDA)

• UMass Smart: consumption data of 400 homes at the one-minute granularity of a whole day (link )

• Building Data Genome: data from the Building Data Genome Project containing 507 whole-building electrical meters, the majority from university buildings (link)

• European One-Node-Per-Country Dataset (link)

• Transmission network datasets (link)

• Unit Commitment Test Systems (link)

• Wind power scenarios by Waqquas Bukhhsh (link)

• Wind forecast scenarios by DTU Wind Power (link)