This is a table overview for open-source data useful for energy research. Here's what you need to know:
The columns "Thermal" to "Price/Cost" help you to filter for the data you need.
Apart from that, we provide relevant metadata (licence, geographical and time scope and resolution, etc.), corresponding publications and the citation in bibtex format in the subsequent columns.
For some datasets, we additionally provide exploratory data analysis (EDA) in jupyter notebooks (see the last table column). You can find the GitLab repository here.
As we value interoperability of research data, we started annotating our resources using the Open Energy Metadata in JSON format. These files will soon be published alongside with the EDA notebooks in our GitLab repository.
This collection is part of the Energy Data Lab project, an initiave to accelerate energy research via providing open-source benchmark data sets and (pre-trained) models. Find out more here!
Disclaimer: While efforts have been made to ensure accuracy, we do not claim that all information stated herein is correct or up-to-date. Users are advised to independently verify any details before relying on them and contact us in case of inaccuracies.
For questions, comments, or data input, please contact a.k.schneider@tum.de. We'd highly appreciate your feedback - do not hesitate to reach out and help us advance energy research!
| Name | Source | Thermal | Thermal.heating | Thermal.cooling | Electricity | Electricity.demand | Electricity.supply | Gas | Weather | Price/Cost | Licence | Description | Paper/Documentation | Download | Geographical Scope | Geographical Resolution | Time Span Start (Year) | Time Span End (Year) | Time Span Comment | Time Resolution | Time Resolution Comment | Size | Methodology | EDA Notebook Available | Citation |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AEMO | AEMO Nemweb Data Archive | x | x | specific | SCATA data from 75 Australian wind farms | documentation | link | Australia | wind farm level | 2015 | 2024 | 5min | measured | yes | @data{aeomo2024, author= {AEMO (Australian Energy Market Operator)}, title={MARKET DATA NEMWEB}, year={2024}}} | ||||||||||
| Battery System with Subunits | Repository KITOpen | x | CC Attribution 4.0 International | The Battery Aging dataset is a comprehensive collection of time-series data capturing various aspects related to the performance and degradation of batteries. This dataset aims to provide insights into the aging process of batteries, offering valuable information for researchers, engineers, and practitioners involved in battery technology and energy storage systems. | documentation | link | Germany | 2018 | 21.06.2023 | several | ms, sec, 5min | measured | yes | @data{steinbuss2023, author = {Steinbuß, G. and Rzepka, B. and Bischof, S. and Blank, T. and Böhm, K.}, title = {Frequent Observations from a Battery System with Subunits}, institution = {Karlsruhe Institute of Technology}, year = {2023}, doi = {10.35097/1174} } | |||||||||||
| DEDDIAG | Nature | x | x | MIT | German household electricity demand dataset, features recordings from 15 homes over 3.5 years, capturing 50 appliances at a 1 Hz frequency. It provides raw 1 Hz power readings of load-shifting relevant appliances over recording periods ranging from 21 to 1351 days. Appliances for load-shifting, including dishwashers, fridges and washing machines, are covered. The dataset includes manual annotations for 14 appliances, precise event timestamps. The dataset is structured in plain text TSV files, organized by house directories. Each house directory contains a house.tsv file with demographic data on residents. Appliance metadata is stored in items.tsv, categorized into types such as Refrigerator, Washing Machine, etc. | paper | link | Germany | Individual home | 2016 | 2019 | 21 days up to 1351 days depending on household | seconds | 1 sec | measured | yes | @article{Wenninger2021, author = "Marc Wenninger and Jochen Schmidt and Andreas Maier", title = "{DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany}", year = "2021", month = "1", url = "https://figshare.com/articles/dataset/DEDDIAG_a_domestic_electricity_demand_dataset_of_individual_appliances_in_Germany/13615073", doi = "10.6084/m9.figshare.13615073.v1" } | ||||||||
| Distribution Grid Simulation | Frauenhofer, University of Kassel, RWTH Aachen, TU Dortmund | x | x | Open Database License | The objective of the research project SimBench is to develop a benchmark dataset to support re- search in grid planning and operation. The SimBench grids differ from other grids in the following key aspects: Consideration of a wide range of use cases during the development of the dataset; Provision of grid data for low voltage (LV), medium voltage (MV), high voltage (HV), EHV as well as design of the dataset for a suitable interconnection of a grid among different voltage levels for cross-level simulations; Grid data in three scenarios, i.e.variants, to provide today’s grid states without voltage and current violations and future grid states with violations (grid extension measures are ne- glected); Ensuring high reproducibility and comparability by providing clearly assigned load and gen- eration time series; Validation of the suitability of the datasets by means of literature research, comparisons to real grid data, expert advice and simulation calculations | documentation | Link | Germany | 11.01.2015 | 30.04.2019 | minutes | 1min, 15min | Within SimBench, a general methodology to compile benchmark grids has been developed , which makes it possible to compile benchmark grids of different voltage levels or scopes. The developed methodology consists of the following six steps: | yes | @data{simbench2019, author= {SimBench}, title={SimBench Codes grid data }, year={2019}}} | ||||||||||
| The focus of the dataset generation and validation is Germany. Nevertheless, the SimBench dataset can also be applied internationally if simulations are performed for comparable grids. It is published under the ODbL license. | |||||||||||||||||||||||||
| ECMWF | University of North Carolina at Charlotte | x | CC BY 4.0 | European Centre for Medium-range Weather Forecast (ECMWF) operational forecast and analysis data used in this study were downloaded from the ECMWF Meteorological Archival and Retrieval System (MARS) on May 1, 2021. Access to the ECMWF archived data was provided by ECMWF's Data Services. The dataset contains the following variables: Longitude, Latitude, Time, Fractional Direction of Wind, Surface Pressure, Total Column Water Vapor, Eastward Wind Component at 10 Meters Above the Surface, Northward Wind Component at 10 Meters Above the Surface, Air Temperature at 2 Meters Above the Surface, Dew Point Temperature at 2 Meters Above the Surface, Surface Solar Radiation Downwards, Surface Thermal Radiation Downwards, Low Cloud Cover, Total Column Ozone, TOA (Top of Atmosphere) Incident Solar Radiation, Total Precipitation, Forecast Albedo. | paper | link | most of Europe and North America | 63° N, °W, 21° S, and 36° E | 2017 | 2020 | hourly | forecast | yes | @data{ecmwf2021, author= {European Centre for Medium-range Weather Forecast (ECMWF)},year={2021}, month={May}, title={ECMWF DATA}} | |||||||||||
| ECMWF Solar | University of North Carolina at Charlotte, University of California at San Diego | x | CC BY 4.0 | Ensemble numerical weather prediction (NWP) is the backbone of the state-of-the-art solar forecasting for horizons ranging between a few hours and a few days. Dynamical ensemble forecasts are generated by perturbing the initial condition, and thereby obtaining a set of equally likely trajectories of the future weather. Generating dynamical ensemble forecasts demands extensive knowledge of atmospheric science and significant computational resources. Hence, the task is often performed by international and national weather centers and space agencies. Solar forecasters, on the other hand, are primarily interested in post-processing those ensemble forecasts disseminated by weather service providers, as to arrive at forecasts of solar power output. | paper | link | most of Europe and North America | 63° N, °W, 21° S, and 36° E | 2017 | 2020 | hourly | forecast | yes | @data{ecmwf2021, author= {European Centre for Medium-range Weather Forecast (ECMWF)},year={2021}, month={May}, title={ECMWF DATA}} | |||||||||||
| ENERTALK | Nature | x | x | CC Attribution 4.0 International | 15 Hz Electricity Consumption Data from 22 Houses in Korea. Provides detailed aggregate and per-appliance measurements sampled at 15 Hz from 22 houses. The dataset is organized in zip files (enertalk-dataset-{house_number}) containing a directory for each house. Each house directory holds subdirectories with Parquet files for daily aggregate and appliance-level data. Subdirectories are named with the date convention (e.g., “20170131” for January 31, 2017). Parquet files within these subdirectories follow the naming pattern “_.parquet.gzip” (e.g., “01_fridge.parquet.gzip”). Each Parquet file includes three columns: “timestamp,” “active_power,” and “reactive_power.” | paper | link | South Korea | individual houses | 2016 | 2017 | 29 days up to 122 days per household | several | 15 Hz, 1sec | measured | yes | @dataset{shin_lee_han_yim_rhee_lee_2019, title={The ENERTALK Dataset, 15 Hz Electricity Consumption Data from 22 Houses in Korea}, url={https://springernature.figshare.com/collections/The_ENERTALK_Dataset_15_Hz_Electricity_Consumption_Data_from_22_Houses_in_Korea/4502780/1}, DOI={10.6084/m9.figshare.c.4502780.v1}, abstractNote={The ENERTALK Dataset, 15 Hz Electricity Consumption Data from 22 Houses in Korea This dataset provides aggregate and per-appliance measurements sampled at 15 Hz from 22 houses. Zip files enertalk-dataset-{house_number} contain a directory for each houses. Each directory holds a set of subdirectories that contain Parquet files for the daily aggregate and appliance-level data. The naming convention for these subdirectories is “<dd>” (e.g. “20161124” for November 24, 2016). The Parquet files are named “\_.parquet.gzip” (e.g. “01\_fridge.parquet.gzip”). In these names, the two-digit integer is uniquely associated with a distinct measuring device in a house. Each Parquet file consists of three columns: “timestamp,” “active\_power,” and “reactive\_power.” The “timestamp” column contains Unix timestamps in milliseconds, such that 1000 corresponds to one second. The “active\_power” column represents active power in watts and the “reactive\_power” column represents reactive power in VAR (volt-ampere reactive) units.</dd>}, publisher={figshare}, author={Shin, Changho and Lee, Eunjung and Han, Jeongyun and Yim, Jaeryun and Rhee, Wonjong and Lee, Hyoseop}, year={2019}, month={Sep} } | ||||||||
| GGV Load | Stadtwerke Groß-Gerau Versorgungs GmbH | x | x | x | x | This dataset contains the quantities from the seasonal series can also be found as daily and monthly quantities. The data are presented absolute and relative in two tables one above the other. Holidays, public holidays and special days are also entered. The change from winter to summer time is reflected by a gap of one hour in the time series. | - | link | Germany | individual households | 2013 | 2021 | several | 15min, daily, monthly | measured | pending | @data{stadtwerke_großgerau_2020, author= {Stadtwerke Groß-Gerau Versorgungs GmbH},year={2020}, month={September}, title={Netzbilanzierung Lastprofile}} | ||||||||
| Individual Household Load | EDF R&D | x | x | x | x | CC BY 4.0 | Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. Different electrical quantities and some sub-metering values are available. This archive contains 2075259 measurements gathered in a house located in Sceaux (7km of Paris, France) between December 2006 and November 2010 (47 months). Notes: 1.(global_active_power*1000/60 - sub_metering_1 - sub_metering_2 - sub_metering_3) represents the active energy consumed every minute (in watt hour) in the household by electrical equipment not measured in sub-meterings 1, 2 and 3. 2.The dataset contains some missing values in the measurements (nearly 1,25% of the rows). All calendar timestamps are present in the dataset but for some timestamps, the measurement values are missing: a missing value is represented by the absence of value between two consecutive semi-colon attribute separators. For instance, the dataset shows missing values on April 28, 2007. | documentation | link | Sceaux (7km of Paris, France) | Individual household | 2006 | 2010 | December 2006 until November 2010 | minutes | 1 min | 2,075,259 datapoints | measured | yes | @dataset{individual_household_electric_power_consumption_235, author = {Hebrail,Georges and Berard,Alice}, title = {{Individual Household Electric Power Consumption}}, year = {2012}, howpublished = {UCI Machine Learning Repository}, note = {{DOI}: https://doi.org/10.24432/C58K54} } | |||||
| Kelmarsh Wind Farm | Zenodo | x | x | x | CC BY 4.0 | This dataset contains: A kmz file for Kelmarsh wind farm in the UK (for opening in e.g. Google Earth); Static data including turbine coordinates and turbine details (rated power, rotor diameter, hub height, etc.); 10-minute SCADA and events data from the 6 Senvion MM92's at Kelmarsh wind farm, grouped by year from 2016 to end-2022, which was extracted from Cubico's secondary SCADA system (Greenbyte). Note not all signals are available for the entire period; Data mappings from primary SCADA to csv signal names; Site substation/PMU meter data where available for the same period; Site fiscal/grid meter data where available for the same period. | documentation | link | United Kingdom | individual windturbines | 2016 | 2022 | every year in a different file | 10 min | measured | yes | @dataset{plumley_2023_8252025, author = {Plumley, Charlie}, title = {Kelmarsh wind farm data}, month = aug, year = 2023, publisher = {Zenodo}, version = {0.1.0}, doi = {10.5281/zenodo.8252025}, url = {https://doi.org/10.5281/zenodo.8252025} } | ||||||||
| REDD | Massachusetts Institute of Technology | x | x | In this paper we present the Reference Energy Disaggregation Data Set (REDD), a freely available data set containing detailed power usage information from several homes, which is aimed at furthering research on energy disaggregation (the task of determining the component appliance contributions from an aggregated electricity signal). | paper | Data website not reachable | US | Individual homes | total of 119 days of data across all homes | several | 15 kHz, 1 sec, 2 sec | measured | no | not available atm... | |||||||||||
| UK-DALE | Nature | x | x | CC Attribution 4.0 International | Provides Domestic Appliance-Level Electricity recordings at a sample rate of 16 kHz for whole-house and 1/6 Hz for individual appliances. This dataset is recorded from five houses, with the longest duration of 655 days for one house. Detailed metadata files, following the NILM Metadata schema, describe appliance specifications, mains wiring, measurements from each meter, room assignments, and more. In YAML text file format, the metadata enhances understanding and utilization of the dataset. | paper | not available atm, reach out to us if you want to access the dataset | UK | Individual homes | several | 15 kHz, 1 sec, 6 sec | measured | no | not available atm... | |||||||||||
| American Meteorological Society 2013-2014 Solar Energy Prediction Contest | Kaggle | x | Contestants will predict the total daily incoming solar energy at 98 Oklahoma Mesonet sites, which will serve as "solar farms" for the contest. Input numerical weather prediction data for the contest comes from the NOAA/ESRL Global Ensemble Forecast System (GEFS) Reforecast Version 2. Data include all 11 ensemble members and the forecast timesteps 12, 15, 18, 21, and 24. The data pertains to weather conditions relevant for solar energy production. | documentation | link | United States | Grid | 1994 | 2009 | hours | 3 hours | measured | no | @misc{noauthor_ams_nodate, title = {{AMS} 2013-2014 {Solar} {Energy} {Prediction} {Contest}}, url = {https://kaggle.com/competitions/ams-2014-solar-energy-prediction-contest}, abstract = {Forecast daily solar energy with an ensemble of weather models}, language = {en}, urldate = {2024-04-10}, file = {Snapshot:C\:\\Users\\lamin\\Zotero\\storage\\3DC5Y2TU\\data.html:text/html}, } | |||||||||||
| AMPds2 | Nature | x | x | x | x | CC Attribution 4.0 International | The Almanac of Minutely Power dataset Version 2 (AMPds2) is designed to aid computational sustainability researchers, power and energy engineers, utility companies, and eco-feedback researchers in testing or prototyping using real house data. AMPds2 captures electricity, water, and natural gas consumption over a 2-year period, providing 11 measurement characteristics for electricity. This dataset offers 730 days of captured data for each meter, along with two years of hourly weather data, and two years of utility billing data for cost analysis. | paper | link | Canada | single residential home | 2012 | 2014 | April 2012 until March 2014 | minutes | 1 min | 21 different sets of 1,051,000 datapoints | measured | no | @data{DVN/FIE0S4_2016, author = {Makonin, Stephen}, publisher = {Harvard Dataverse}, title = {{AMPds2: The Almanac of Minutely Power dataset (Version 2)}}, UNF = {UNF:6:0uqZaBkSWdyv27JqTHFWPg==}, year = {2016}, version = {V3}, doi = {10.7910/DVN/FIE0S4}, url = {https://doi.org/10.7910/DVN/FIE0S4} } | |||||
| Australia 339 Wind Farms | Monash University | x | x | CC Attribution 4.0 International | This dataset contains very long minutely time series representing the wind power production of 339 | paper | link | Australia | Wind farm | 2019 | 2020 | August 2019 until Juli 2020 | minutes | measured | no | @dataset{godahewa_2021_4654858, author = {Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoff and Abolghasemi, Mahdi and Hyndman, Rob and Montero-Manso, Pablo}, title = {Wind Farms Dataset (without Missing Values)}, month = mar, year = 2021, publisher = {Zenodo}, version = 2, doi = {10.5281/zenodo.4654858}, url = {https://doi.org/10.5281/zenodo.4654858} } | |||||||||
| wind farms in Australia. , the data has been gathered by us periodically over a period of | |||||||||||||||||||||||||
| one year from 01/08/2019 to 31/07/2020. The collected periodical data are aggregated to make all the | |||||||||||||||||||||||||
| series span over one year. | |||||||||||||||||||||||||
| The collected data contain missing values where some series contain missing data for more than | |||||||||||||||||||||||||
| seven consecutive days. Our repository contains both the original version of the collected dataset and | |||||||||||||||||||||||||
| a version where the missing values have been replaced by zeros. | |||||||||||||||||||||||||
| Australian Solar Power | Monash University | x | x | CC Attribution 4.0 International | This dataset contains a single very long daily time series representing the solar power production in MW recorded per every 4 seconds starting from 01/08/2019. It was downloaded from the Australian Energy Market Operator (AEMO) online platform. The length of this time series is 7397222. | documentation | link | Australia | Solar PV power plant | 2019 | 2020 | starts August 2019 | seconds | 4 seconds | 7397222 | measured | no | @dataset{godahewa_2021_4654858, author = {Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoff and Abolghasemi, Mahdi and Hyndman, Rob and Montero-Manso, Pablo}, title = {Wind Farms Dataset (without Missing Values)}, month = mar, year = 2021, publisher = {Zenodo}, version = 2, doi = {10.5281/zenodo.4654858}, url = {https://doi.org/10.5281/zenodo.4654858} } | |||||||
| Australian Wind Farms | Monash University | x | x | CC Attribution 4.0 International | This dataset contains a single very long daily time series representing the wind power production in MW recorded per every 4 seconds starting from 01/08/2019. It was downloaded from the Australian Energy Market Operator (AEMO) online platform. The length of this time series is 7397147. | documentation | link | Australia | Wind farm | 2019 | 2020 | starts August 2019 | seconds | 4 seconds | 7397147 | measured | no | @dataset{godahewa_2021_4656032, author = {Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoff and Abolghasemi, Mahdi and Hyndman, Rob and Montero-Manso, Pablo}, title = {Wind Power Dataset (4 Seconds Observations)}, month = apr, year = 2021, publisher = {Zenodo}, version = 2, doi = {10.5281/zenodo.4656032}, url = {https://doi.org/10.5281/zenodo.4656032} } | |||||||
| Dataset of distribution transformers for predictive maintenance | Universidad del Cauca | x | x | x | CC Attribution 4.0 International | Dataset contains 16.000 electric power distribution transformers from Cauca Department (Colombia) and aims to support predictive maintenance. They are distributed in rural and urban areas of 42 municipalities. The information covers 2019 and 2020 years, has 6 categorical variables and 5 continuous variables. First ones correspond to: location, self-protected, removable connector, criticality according to ceraunic level, client and installation type. Second ones are transformer power, burn rate, users number, unsupplied electricity and secondary lines length. | paper DOI: 10.1016/j.dib.2021.107454 | link | Colombia | Individual | 2019 | 2020 | - | yearly | 16000 | measured | no | @misc{DiegoABravoM.2021, author = {{Diego A Bravo M}}, date = {2021}, title = {Dataset of Distribution Transformers at Cauca Department (Colombia)}, publisher = {Mendeley}, doi = {10.17632/YZYJ46XPMY.4} } | |||||||
| Dataset on electrical single-family house and heat pump load profiles in Germany | Schlemminger et al. | x | x | x | x | x | x | CC Attribution 4.0 International | Residential electricity household and heat pump load profiles, measured in 38 single-family houses in Northern Germany. | paper | link | Germany | Northern Germany, Indivicual households | 2018 | 2020 | May 2028 until December 2020 | several | 10 s, 1 min, 15 min and 60 min | measured | no | @dataset{schlemminger_2021_5642902, author = {Schlemminger, Marlon and Ohrdes, Tobias and Schneider, Elisabeth and Knoop, Michael}, title = {WPuQ}, month = nov, year = 2021, publisher = {Zenodo}, version = {2.0}, doi = {10.5281/zenodo.5642902}, url = {https://doi.org/10.5281/zenodo.5642902} } | ||||
| Disaggregated appliance/aggregated house power | UK Domestic Appliance Level Electricity | x | x | CC Attribution 4.0 International | Appliance-by-appliance and whole-home power demand for 5 UK homes recorded approximately once every 6s. For 3 of the homes, whole-home data was also recorded at 1s and 16kHz. Data is stored in individual directories for each house. Each appliance has a separate time series file channel_nn.dat and most have a channel_nn_button_press.dat file indicating switching events. | documentation | link | UK | London, Individual household | 2012 | 2015 | start in 09.11.2012 until 05.01.2015 | seconds | 1 sec, 6 sec, 16 kHz | >10.000 | measured | no | @misc{Kelly.2015, author = {Kelly, Jack}, date = {2015}, title = {UK Domestic Appliance Level Electricity (UK-DALE) - Disaggregated (6s) appliance power and aggregated (1s) whole house power}, publisher = {{UK Energy Research Centre Energy Data Centre (UKERC EDC)}}, doi = {10.5286/UKERC.EDC.000001} } | |||||||
| DTU 7k BUS network | Technical University Denmark | x | x | The DTU-7k Bus Active Distribution Network is a multi-voltage level distribution grid model spanning acrossthree voltage levels, namely 60 kV-10 kV-0.4 kV. The network is representative of a distribution network witha large share of weather-dependent generation connected atthe lower voltage levels such as 10 kV and 400 V. The DTU-ADN is developed considering the correlations between weather-dependent generation on the load demand. | documentation | link | Denmark | Grid | 2014 | 2015 | 10th December 2014 until 10th September 2015 | hours | varied but standard is 1hour | measured | no | @misc{baviskar_hansen_das_koivisto_2021, author={Baviskar, Aeishwarya and Hansen, Anca Daniela and Das, title={DTU 7k-Bus Active Distribution Network}, Publisher={Technical University of Denmark}, Kaushik and Koivisto, Matti Juhani}, date={2021}, DOI={10.11583/DTU.c.5389910.v1}, url={https://data.dtu.dk/collections/DTU_7k-Bus_Active_Distribution_Network/5389910/1}, } | |||||||||
| ECD-UY | Nature | x | x | CC Attribution 4.0 International | The Electricity Consumption Data set of Uruguay (ECD-UY) offers detailed records of residential electricity consumption, primarily in Montevideo. The dataset is structured into three subsets, covering total household consumption (derived from smart meters of 110,953 customers nationwide, with a 15-minute sample interval, covering the period from January 1, 2019, to November 3, 2020), electric water heater consumption (comprises data from 268 households with a 1-minute sample interval, recorded from July 2, 2019, to October 26, 2020), and by-appliance electricity consumption (includes total aggregated consumption records of nine households and disaggregated consumption data for specific appliances in each household, sampled at 1-minute intervals, spanning from August 27, 2019, to September 16, 2019). | paper | link | Uruguay | individual households | 2019 | 2020 | 3 different timespans, total: January 2019 until October 2020 | minutes | 1min, 15min | measured | no | @misc{chavat_nesmachnow_graneri_alvez_2022, author={Chavat, Juan Pablo and Nesmachnow, Sergio and Graneri, Jorge and Alvez, Gustavo}, title={ECD-UY: Detailed household electricity consumption dataset of Uruguay}, date={2022}, DOI={10.6084/m9.figshare.c.5428608.v1}, publisher={figshare}, url={https://springernature.figshare.com/collections/ECD-UY_Detailed_household_electricity_consumption_dataset_of_Uruguay/5428608/1}, } | ||||||||
| EIA Cleaned Hourly Electricity Demand Data | U.S. Energy Information Administration | x | x | x | CC Attribution 4.0 International | Cleaned hourly electricity demand data for electric balancing authorities within the contiguous US. Non-missing, continuous, and physically plausible demand data to facilitate analysis. Raw data based on the U.S. Energy Information Administration. | paper | link | US | Country, region, balancing authority | 2015 | 2019 | mid 2015 - mid 2019 | hourly | - | 43000 | measured | no | @dataset{ruggles_2020_3690240, author = {Ruggles, Tyler H. and Farnham, David J.}, title = {EIA Cleaned Hourly Electricity Demand Data}, month = feb, year = 2020, publisher = {Zenodo}, version = {v1.1}, doi = {10.5281/zenodo.3690240}, url = {https://doi.org/10.5281/zenodo.3690240} } | ||||||
| Electric Vehicle Charging Station Energy Consumption | City of Boulder (CO) Open Data Hub | x | x | x | CC0 1.0 | Shows the energy use, length of charging time, gasoline savings and greenhouse gas emission reductions from all city-owned electric vehicle (EV) charging stations. Data are broken out by charging station name/location, transaction date, and transaction start time; 1 row indicates 1 EV charging station transaction. | documentation | link | Boulder, Colorado, US | Charging station | 2018 | 2023 | seems to update still with 3months delay | - | - | 148137 | measured | no | @dataset{FGDC-STD-001-1998, title = {Electric Vehicle Charging Station Data}, month = sep, year = 2023, publisher = {Bouldercolorado}, url = {https://services.arcgis.com/ePKBjXrBZ2vEEgWd/arcgis/rest/services/Electric_Vehicle_Charging_Station_Data/FeatureServer} } | ||||||
| Electricity Load 2011 - 2014 | UCI Machine Learning Repository | x | x | CC Attribution 4.0 International | This data set contains electricity consumption, in kW, of 370 points/clients, in 15 min intervals. All time labels report to Portuguese hour. Converted data set at https://github.com/laiguokun/multivariate-time-series-data also, as well as converted to weekly time intervals at https://zenodo.org/record/4656141 . | documentation | link | Portugal | Individual household | 2011 | 2014 | minutes | 15 min | 370 | measured | no | @misc{misc_electricityloaddiagrams20112014_321, author = {Trindade,Artur}, title = {{ElectricityLoadDiagrams20112014}}, year = {2015}, howpublished = {UCI Machine Learning Repository}, note = {{DOI}: https://doi.org/10.24432/C58C86} } | ||||||||
| End Use Load Profiles for the U.S. Building Stock | National Renewable Energy Laboratory | x | x | x | x | x | CC Attribution 4.0 International | Calibrated and validated 15-minute resolution load profiles for all major residential and commercial building types and end uses, across all climate regions in the United States. These EULPs were created by calibrating the ResStock and ComStock physics-based building stock models using many different measured datasets | documentation | link | US | Building | - | - | Data is from after 2010, but widely distributed | quarter hourly | - | 900000 | calculated | no | @div{oedi_4520, title = {End-Use Load Profiles for the U.S. Building Stock}, author = {Wilson, Eric, Parker, Andrew, Fontanini, Anthony, Present, Elaina, Reyna, Janet, Adhikari, Rajendra, Bianchi, Carlo, CaraDonna, Christopher, Dahlhausen, Matthew, Kim, Janghyun, LeBar, Amy, Liu, Lixi, Praprost, Marlena, White, Philip, Zhang, Liang, DeWitt, Peter, Merket, Noel, Speake, Andrew, Hong, Tianzhen, Li, Han, Mims Frick, Natalie, Wang, Zhe, Blair, Aileen, Horsey, Henry, Roberts, David, Trenbath, Kim, Adekanye, Oluwatobi, Bonnema, Eric, El Kontar, Rawad, Gonzalez, Jonathan, Horowitz, Scott, Jones, Dalton, Muehleisen, Ralph, Platthotam, Siby, Reynolds, Matthew, Robertson, Joseph, Sayers, Kevin, and Li, Qu.}, date = {2021}, doi = {10.25984/1876417}, url = {https://data.openei.org/submissions/4520}, place = {United States}} | ||||
| Energy Demand RTE with weather and inhabitant data | Individual | x | x | - | Calculated forecast system of the French (metropolitan) energy consumption, both daily and half-hourly, using data from RTE no later than 2016, the energy network manager in France, GEOFLA for inhabitant data, and weather. These datasets are also included within the GitHub project. Individual that created this has a job at EDF Energy, but did this independently. | documentation | link | France | Country | daily | also half hourly | calculated | no | ||||||||||||
| Eurostat energy balances | Eurostat | x | x | x | x | None if source indicated | Presents the simplified energy balance sheets, key indicators and time series of key elements of energy balances and statistics. Allows to see the relative importance of the different fuels in their contribution to the economy. | documentation | link | EU (extended) | Country | 1990 | 2019 | - | yearly | - | 37 | measured | no | @book{.2020, abstract = {The current publication presents the simplified energy balance sheets for 2018, key indicators and time series of key elements of energy balances and statistics for recent years as well as energy flow charts for year 2018. Preliminary 2019 data are shown for the supply side of energy statistics. Energy data are available for all Member States of the European Union as well as United Kingdom, Iceland, Norway, Montenegro, North Macedonia, Albania, Serbia, Turkey, Bosnia and Herzegovina, Kosovo (UNSCR1244/99), Moldova, Ukraine, and Georgia.}, year = {2020}, title = {Energy balance sheets: 2020 edition}, keywords = {balance sheet;energy policy;EU statistics}, address = {Luxembourg}, edition = {2020 edition}, publisher = {{Publications Office of the European Union}}, isbn = {978-92-76-20629-3}, series = {Energy balance sheets, 2020 data} } | |||||
| Fraunhofer Irradiance Dataset | Fraunhofer | x | CC Attribution 4.0 International | The PV-Live dataset comprises data from a network of 40 solar irradiance measurement stations across the German state of Baden-Württemberg. | documentation | link | Germany | Baden-Württemberg, inidivdual measurement station | 2020 | 2023 | September 2019 until January 2023 | minutes | one dataset per month | measured | no | @dataset{dittmann_2023_8224200, author = {Dittmann, Anna and Dinger, Florian and Herzberg, Wiebke and Holland, Nicolas and Karalus, Steffen and Braun, Christian and Zähringer, Ralph and Heydenreich, Wolfgang and Lorenz, Elke}, title = {{PV-Live dataset - Measurements of global horizontal and tilted solar irradiance}}, month = aug, year = 2023, publisher = {Zenodo}, doi = {10.5281/zenodo.8224200}, url = {https://doi.org/10.5281/zenodo.8224200} } | |||||||||
| Frequency Reserves | Regelleistung.net | x | x | x | x | For the purpose of maintaining the balance between supply and demand, TSOs procure control reserve. A need for control reserve arises as soon as the current feed-in differs from current consumption. Deviations are expressed as changes in frequency. Data for FCR, aFRR and mFRR are provided. | documentation | link | Germany | German transmission system operators | 2011 | present day | Different data is available starting form a different time, Datacenter starts 12.07.2018, Network transparency starts 2014, Historical tender data starts mid 2011 | minutes | 15min | measured | no | ||||||||
| GEFCom2014 Load Forecasting Data | GEFCom2014-L | x | x | x | The load forecasting track of GEFCom2014 was about probabilistic load forecasting. We asked the contestants to provide one-month ahead hourly probabilistic forecasts on a rolling basis for 15 rounds. In the first round, we provided 69 months of hourly load data and 117 months of hourly temperature data. Incremental load and temperature data was provided in each of the future rounds. | documentation | link | ISO New England | Individual utility | 2004 | 2014 | hourly | measured | no | @dataset{hong_2016, author = {Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman}, title = {Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond}, year = 2016, publisher = {International Journal of Forecasting,}, url = {https://www.dropbox.com/s/pqenrr2mcvl0hk9/GEFCom2014.zip?e=2&file_subpath=%2FGEFCom2014+Data&dl=0} } | ||||||||||
| Household data | Open Power System Data | x | x | x | x | x | x | CC Attribution 4.0 International | Detailed small businesses and residential households load and solar generation. Relevant for household- or low-voltage-level power system modeling. Includes solar power generation and electricity consumption (load) in a resolution up to single device consumption. | documentation | link | DE | Individual household | 2014 | 2019 | December of 2014 until May of 2019 | several | 1 min, 15 min, 60 min, data not complete | 154000 | measured | no | @dataset{noauthor_open_nodate, title = {Open {Power} {System} {Data} - {Household} {Data}}, url = {https://data.open-power-system-data.org/household_data/latest/}, publisher = {Open Power System Data}, } | |||
| IDEAL Household Energy Dataset | Edinburgh DataShare | x | x | x | x | x | x | x | CC Attribution 4.0 International | Comprises data from 255 UK homes. Alongside electric and gas data from each home the corpus contains individual room temperature and humidity readings and temperature readings from the boiler. For 39 of the 255 homes more detailed data is available, including individual electrical appliance use data, and data on individual radiators. | paper DOI: 10.1038/s41597-021-00921-y | link | UK | Individual home | 2016 | 2018 | 55-673 days; up to June 2018 | seconds | 1 sec, 12 sec | measured | no | @dataset{goddard_ideal_2021, title = {{IDEAL} {Household} {Energy} {Dataset}}, copyright = {The IDEAL Household Energy Corpus is released under the Creative Commons Attribution 4.0 license agreement (also called CC BY 4.0). https://creativecommons.org/licenses/by/4.0/legalcode}, url = {https://datashare.ed.ac.uk/handle/10283/3647}, doi = {10.7488/ds/2836}, language = {eng}, publisher = {University of Edinburgh. School of Informatics}, author = {Goddard, Nigel and Kilgour, Jonathan and Pullinger, Martin and Arvind, D. K. and Lovell, Heather and Moore, Johanna and Shipworth, David and Sutton, Charles and Webb, Jan and Berliner, Niklas and Brewitt, Cillian and Dzikovska, Myroslava and Farrow, Edmund and Farrow, Elaine and Mann, Janek and Morgan, Evan and Webb, Lynda and Zhong, Mingjun}, month = apr, year = {2021}, note = {Accepted: 2020-05-28T13:10:55Z}, } | |||
| Living Lab | Energy Systems Catapult | x | x | x | x | x | x | specific | Contains the sensor data gathered for homes fitted with gas boilers during the course of the Winter trials | documentation | link | Newcastle, Manchester, South Wales, West Midlands | Individual home | 2018 | 2019 | - | minutes | irregular | 100 | measured | no | ||||
| London Smart Meter | Kaggle | x | x | x | CC Attribution 4.0 International | Refactorised version of London smart meter data (original linked also), with added weather dataset. | documentation | link | UK | Individual household | 2011 | 2014 | minutes | measured half hourly, also made into daily | 5567 | measured | no | ||||||||
| Open Power System Data Time Series dataset | Open Power System Data | x | x | x | x | x | MIT | Contains different kinds of timeseries data relevant for power system modelling, namely electricity prices, electricity consumption (load) as well as wind and solar power generation and capacities. | documentation | link | EU, 32 countries | Country, control area, bidding zone | 2015 | 2020 | - | several | 15min (6 countries, DE very detailed), 30min (3 countries, GB very detailed) , 1h (32 countries) | 200 | measured | no | @dataset{muehlenpfordt_open_2016, title = {Open {Power} {System} {Data} {Time} {Series} dataset}, url = {https://data.open-power-system-data.org/time_series}, doi = {10.25832/TIME_SERIES}, publisher = {Open Power System Data}, author = {Muehlenpfordt, Jonathan}, year = {2016}, } | ||||
| openTUMflex data | TUM Chair of Energy Economy and Application Technology | x | x | x | x | x | x | x | x | x | GNU General Public License v3.0 | An open-source python-based flexibility model to quantify and price the flexibility of household devices. Creates scenarios to calculate the flexibility potential and flexibility prices based on price, weather, generation and load forecasts of household devices. Contains dataset including PV, battery storage systems (BSS), electric vehicles (EV), heat pumps (HP), combined heat and power (CHP) units. | documentation | link | EU | no | @dataset{zade_opentumflex_2020, title = {{OpenTUMFlex} {Data}}, shorttitle = {tum-ewk/{OpenTUMFlex}}, url = {https://zenodo.org/records/4251512}, doi = {10.5281/zenodo.4251512}, abstract = {Flexibility model using Python}, publisher = {Zenodo}, author = {Zadé, Michel and You, Zhengjie and Nalini, Babu Kumaran}, month = nov, year = {2020}, doi = {10.5281/zenodo.4251512} } | ||||||||
| Regionalised heat demand and power-to-heat capacities in Germany | Heitkoetter et al. | x | x | x | x | CC Attribution 4.0 International | Residential heat demand calculated for each administrative district in Germany for 2011. Determination of heat demand share covered by electric heating technologies. 729 building categories defined from a special evaluation of census data. Five classes of heating types and installed heating capacity considered. Added assumption for installed heat capacity in 2030. | paper DOI: 10.1016/j.apenergy.2019.114161 | link | DE | Administrative district level (NUTS-3): 150000-800000 inhabitants | 2011 | 2011 | - | quarter hourly | - | 200000 | calculated and derived | no | @dataset{heitkoetter_regionalised_2019, title = {Regionalised {Heat} {Demand} and {Power}-{To}-{Heat} {Capacities} in {Germany} - {An} {Open} {Data} {Set} for {Assessing} {Renewable} {Energy} {Integration}}, shorttitle = {Regionalised {Heat} {Demand} and {Power}-{To}-{Heat} {Capacities} in {Germany}}, url = {https://zenodo.org/records/3404147}, doi = {10.5281/zenodo.3404147}, language = {eng}, publisher = {Zenodo}, author = {Heitkoetter, Wilko}, month = sep, year = {2019}, keywords = {census special evaluation, heat demand, installed heating capacity classes, open data, open source, power-to-heat capacities, regionalisation} } | |||||
| Renewables.ninja PV and Wind Profiles | Open Power System Data | x | x | x | CC Attribution-NonCommercial 4.0 International | Photovoltaic and wind capacity factors from Renewables.ninja, generated using the MERRA-2 reanalysis. Renewable.ninja can generate solar, wind, heating, cooling and weather data from everywhere around the world using the data form 2019. | documentation | link | Europe, 36 Countries | Country | 1980 | 2019 | - | hourly | - | 351000 | calculated | no | @dataset{pfenninger_renewablesninja_2020, title = {Renewables.ninja {PV} and {Wind} {Profiles}}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0360544216311744}, doi = {10.1016/j.energy.2016.08.060}, language = {en}, publisher = {Open Power System Data}, author = {Pfenninger, Stefan and Staffell, Iain}, month = sep, year = {2020}, file = {Full Text:/home/lukas/Zotero/storage/CKQLK83Y/Pfenninger and Staffell - 2016 - Long-term patterns of European PV output using 30 .pdf:application/pdf}, } | ||||||
| Residential & commercial building energy demand profiles under different retrofit scenarios (CESAR model) | Murray et al. | x | x | x | x | x | - | Hourly space and water heating, cooling and electricity demand profiles for a selected set of archetype buildings in Switzerland, under 2 envelope retrofit scenarios; includes single-family residential, Multi-family residential, Office, School, Shop, Restaurant and Hospital; based on results from simulations with the CESAR model. | paper DOI: 10.1016/j.enbuild.2019.109569 | link | CH | Building | - | - | no specific year mentioned, but assumed between 2015 and 2019 | hourly | - | 8,760 datapoints for 93 buildings | calculated | no | @dataset{noauthor_residential_2020, title = {Residential \& commercial building energy demand profiles under different retrofit scenarios ({CESAR} model)}, url = {https://data.sccer-jasm.ch/demand-hourly-profile-retrofits-cesar}, publisher = {JASM}, month = sep, year = {2020} } | ||||
| Retrieval data on network transparency | Regelleistung.net | x | In addition to the data points for the NRV balance and the NRV balance traffic light, the AEP data points relating to the uniform balancing energy price (reBAP) are also published in the data on control energy section. The AEP estimator is also located in this section. | documentation | link | EU (extended) | Country | 2013 | present day | minutes | 15min | mix | no | @dataset{noauthor_retrieval_nodate, title = {Retrieval data on network transparency}, url = {https://www.netztransparenz.de/de-de/Regelenergie/NRV-und-RZ-Saldo/NRV-Saldo}, publisher = {NETZTRANSPARENZ.DE}, } | |||||||||||
| Smart Meter Data IEEE-CIS Competition | IEEE | x | x | x | x | x | CC Attribution 4.0 International | Data files given in competition that involved predicingt the monthly electricity consumption for 3248 households in a coming year. Given data includes weather, electricity consumption (smart meter data), and information on households. | documentation | link | UK | Individual household | 2017 | 2017 | one year | minutes | 30min | 3248 | measured | no | @dataset{triguero_ieee-cis_2020, title = {{IEEE}-{CIS} {Technical} {Challenge} on {Energy} {Prediction} from {Smart} {Meter} {Data}}, url = {https://ieee-dataport.org/competitions/ieee-cis-technical-challenge-energy-prediction-smart-meter-data}, doi = {10.21227/2npg-c280}, language = {en}, publisher = {IEEE DataPort}, author = {Triguero, Isaac}, month = may, year = {2020}, } | ||||
| Spatial and temporal data to study residential heat decarbonisation pathways in England and Wales | Canet et al. | x | x | x | x | x | CC Attribution 4.0 International | Python-generated annual heat demand data for England and Wales in 2018 at Lower Layer Super Output Area (LSOA) level, before and after energy efficiency measures. Normalised half-hourly profiles for heat production and energy consumption of different heating technologies in 2013 looking at average temperature in GB. Costs for energy efficiency measures by local authority. | paper DOI: 10.1038/s41597-022-01356-9 | link | England, Wales | Lower Layer Super Output Area (LSOA): 400-1200 households, 1000-3000 persons | 2013 / 2018 | 2013 / 2018 | full year | several | 2013: half hour, 2018: yearly | 17521 half hour datapoints 34753 areas | calculated | no | @dataset{canet_spatio-temporal_2021, title = {Spatio-temporal heat demand for {LSOAs} in {England} and {Wales}}, url = {https://ukerc.rl.ac.uk/cgi-bin/dataDiscover.pl?GoButton=detail&dataid=94510b6f-f8d5-4257-a0d9-e1c0ca7f929e}, doi = {10.5286/UKERC.EDC.000944}, language = {en}, publisher = {UKERC Energy Data Centre}, author = {Canet, Alexandre}, year = {2021}, } | ||||
| TIGGE | THORPEX | x | x | x | CC Attribution 4.0 International | Ensemble forecast data from 13 global numerical weather perdiction (NWP) centres. Gathered by The Observing System Research and Predictability Experiment (THORPEX) from 10 centres, prost-processed, homogenized and standardized. | paper DOI: 10.1175/2010BAMS2853.1 | link | World | Individual centre | 2006 | 2023 | - | other | 6 hours | - | forecast | no | @dataset{santoalla_tigge_2024, title = {{TIGGE}}, url = {https://confluence.ecmwf.int/display/TIGGE/TIGGE+archive}, publisher = {ECMWF}, author = {Santoalla, Daniel V. and Mladek, Richard}, month = feb, year = {2024}, } | ||||||
| US Solar Power - Minutes | Monash University | x | x | CC Attribution 4.0 International | The solar dataset contains approximately 6000 simulated time series representing 5-minute solar power and hourly day-ahead forecasts of photovoltaic (PV) power plants in United States in 2006. The uploaded dataset contains the aggregated version of a subset of the original dataset used by Lai et al. (2017). It contains 137 time series representing the solar power production recorded per every 10 minutes in Alabama state in 2006. | documentation | link | United States | State, Solar PV power plant | 2006 | 2006 | full year | minutes | 10 minute steps | 52,000 datapoints for 6,000 plants | calculated | no | @dataset{godahewa_solar_2020, title = {Solar {Dataset} (10 {Minutes} {Observations})}, url = {https://zenodo.org/records/4656144}, doi = {10.5281/zenodo.4656144}, language = {eng}, publisher = {Zenodo}, author = {Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoff and Hyndman, Rob and Montero-Manso, Pablo}, month = jun, year = {2020}, keywords = {forecasting, solar}, } | |||||||
| US Solar Power - Weekly | Monash University | x | x | CC Attribution 4.0 International | The solar dataset contains approximately 6000 simulated time series representing 5-minute solar power and hourly day-ahead forecasts of photovoltaic (PV) power plants in United States in 2006. The uploaded dataset contains the aggregated version of a subset of the original dataset. It contains 137 time series representing the weekly solar power production in Alabama state in 2006. | documentation | link | United States | State, Solar PV power plant | 2006 | 2006 | full year | weekly | 52 datapoints for 6,000 plants | calculated | no | @dataset{godahewa_solar_2020, title = {Solar {Weekly} {Dataset}}, url = {https://zenodo.org/records/4656151}, doi = {10.5281/zenodo.4656151}, language = {eng}, publisher = {Zenodo}, author = {Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoff and Hyndman, Rob and Montero-Manso, Pablo}, month = jun, year = {2020}, keywords = {forecasting, solar, weekly series} } | ||||||||
| WeatherBench: A benchmark dataset for data-driven weather forecasting | Rasp et al. | x | x | x | CC Attribution 4.0 International | Benchmark dataset for data-driven medium-range weather forecasting. Data derived and processed from the ERA5 archive of the ECMWS. Machine-learning friendly: evaluation metrics, baseline scores, sample models provided. | documentation | link | World | 0.25° latitude-longitude grid | 1979 | 2018 | - | hourly | - | - | estimated | no | @dataset{rasp_weatherbench_2020, title = {{WeatherBench}: {A} benchmark dataset for data-driven weather forecasting}, url = {https://mediatum.ub.tum.de/1524895}, urldate = {2024-04-12}, author = {Rasp, Stephan and Dueben, Peter D. and Sher, Sebastian and Weyn, Jonathan A. and Mouatadid, Soukayna and Thuerey, Nils}, month = dec, year = {2020}, } | ||||||
| When2Heat Heating Profiles | Open Power System Data | x | x | x | CC Attribution 4.0 International | Time series of heat demand for space and water heating, and coefficient of performance (COP) of heat pumps. Calculated for 28 European countries. | paper DOI: 10.1038/s41597-019-0199-y | link | EU | Country | 2008 | 2022 | - | hourly | - | 114000 | calculated | no | @dataset{ruhnau_when2heat_2023, title = {{When2Heat} {Heating} {Profiles}}, author = {Ruhnau, Oliver and Hirth, Lion and Praktiknjo, Aaron and Muessel, Jarush}, month = jul, year = {2023}, } | ||||||
| Wind Power Production Italy | Sotavento | x | x | x | - | Publicly available historical (and simulated) data consisting of wind speed, direction and resulting power production. | documentation | link | Italy | Wind farm | 2020 | 2024 | Query result is limited to 140 rows and dates after 01/01/2020. | several | 10 minutes, hourly, daily | measured | no | @dataset{noauthor_historical_nodate, title = {Historical Wind Power Production Italy}, url = {https://www.sotaventogalicia.com/en/technical-area/real-time-data/historical/}, langid = {american}, } | |||||||
| Wind Turbine in Turkey | Kaggle | x | - | In Wind Turbines, Scada Systems measure and save data's like date/time, wind speed, wind direction, generated power etc. for 10 minutes intervals. This file was taken from a wind turbine's scada system that is working and generating power in Turkey. | documentation | link | Turkey | Wind turbine | 2018 | 2018 | full year | minutes | 10 minutes | 50,530 datapoints | measured | no | @dataset{erisen_wind_2019, title = {Wind {Turbine} in {Turkey}}, url = {https://www.kaggle.com/datasets/berkerisen/wind-turbine-scada-dataset}, language = {en}, author = {Erisen, Berk}, year = {2019} } | ||||||||
| Penmanshiel Wind Farm | Zenodo | x | x | x | CC BY 4.0 | Wind farm dataset containing 10-minute SCADA and events data from 14 Senvion MM82's at Penmanshiel wind farm, grouped by year from 2016 to mid-2021 as well as static data including turbine coordinates and turbine details (rated power, rotor diameter, hub height, etc.). Additionally, there is a kmz file for the wind farm, data mappings from primary SCADA to csv signal names, site substation/PMU meter data and site fiscal/grid meter data. | documentation | link | United Kingdom | individual windturbines | 2016 | 2022 | full year | minutes | 10 minutes | 14 x 7 .csv files x 19079280 data points for SCADA Turbine data (14 Turbines, 7 years), 14 x 7 .csv files x 140657 data points for SCADA Status data | measured | yes | @dataset{plumley_2023_8253010, author = {Plumley, Charlie}, title = {Penmanshiel wind farm data}, month = aug, year = 2023, publisher = {Zenodo}, version = {0.1.0}, doi = {10.5281/zenodo.8253010}, url = {https://doi.org/10.5281/zenodo.8253010} } | ||||||
| A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods | Zenodo | x | x | CC BY 4.0 | This repository contains a comprehensive solar irradiance, imaging, and forecasting dataset. The goal with this release is to provide standardized solar and meteorological datasets to the research community for the accelerated development and benchmarking of forecasting methods. The data consist of three years (2014–2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California. In addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, Numerical Weather Prediction forecasts, and weather data. We also include sample codes of baseline models for benchmarking of more elaborated models. | documentation | link | California | state | 2014 | 2016 | start and end vary by a couple of hours but they are the first and last days of the year | minutes | 1 minute, 5 minutes | forecast | no | @dataset{Carreira_2019_2826939, author = {Carreira Pedro, H., Larson, D., & Coimbra, C.}, title = {A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods}, month = jun, year = 2019, publisher = {Zenodo}, version = {1}, doi = {10.5281/zenodo.2826939}, url = {https://doi.org/10.5281/zenodo.2826939} } | ||||||||
| Detailed operational building data for six office rooms in Denmark | Zenodo | x | x | x | x | CC BY 4.0 | The operational building data presented in this paper has been collected from six office rooms located in an office building (research and educational purposes) located on the main campus of Aalborg University in Denmark. The dataset consists of measurements of occupancy, indoor environmental quality, room-level and system-level heating, ventilation and lighting operation at a 5 min resolution. The indoor environmental quality and building system data were collected from the building management system. The occupancy level in each monitored room is established from the computer vision-based analysis of wall-mounted camera footage of each office. The number of people present in the room is estimated using the YOLOv5s image recognition algorithm. The present dataset can be used for occupancy analysis, indoor environmental quality investigations, machine learning, and model predictive control. | paper Doi: 10.1016/j.dib.2024.110326 | link | Denmark | Office rooms | 2023 | 2023 | Start on the 27th of February and ends the 31st December | minutes | 5 minutes | measured | no | @dataset{Melgaard_2024_10673763, author = {Melgaard, S. P., Johra, H., Nyborg, V. Ø., Marszal-Pomianowska, A., Jensen, R. L., Kantas, C., Larsen, O. K., Hu, Y., Frandsen, K. M., Larsen, T. S., Svidt, K., Andersen, K. H., Leiria, D., Schaffer, M., Frandsen, M., Veit, M., Ussing, L. F., Lindhard, S. M., Pomianowski, M. Z., … Heiselberg, P. K.}, title = {A Danish high-resolution dataset for six office rooms with occupancy, indoor environment , heating, ventilation, lighting and room control monitoring}, month = feb, year = 2024, publisher = {Zenodo}, version = {3}, doi = {10.5281/zenodo.10673763}, url = {https://doi.org/10.5281/zenodo.10673763} } | ||||||
| Industrial VAE Load | Zenodo | x | x | x | x | CC Attribution 4.0 International | 5,359 company sites in Germany from the industrial as well as the trade and commerce sector, provided by the VEA Federal Union of Energy Consumers, represent a wide range of divers businesses, e.g. the processing industry, maintenance and repair of motor vehicles as well as health care and welfare. Each data set contains information from the system operator, namely the voltage level of the connected grid as well as energy and power prices and peak load times of the specific grid operator. Furthermore, for each data set the load profile is given in 15-minute resolution for an entire billing period. If local energy generation plants, such as combined heat and power plants or photovoltaics, are installed, their generation is included in the load profiles as they directly effect the purchased energy. | link | link | Germany | Company sites | 2016 | 2016 | full year | quarter hourly | 15 minutes | 35136 values | measured | no | @dataset{tiemann_2024_13910298, |