Find research datasets worth reusing

opencc-zeroOct 2024View details →

Weekly plots of Great Britain's half-hourly electrical system weather dependent generation, net imports and overall demand from 2008-11-10

Plots that show the electrical system transition of Great Britain, they were created to form the individual frames for a video of the transition.

opencc-by-4.0Sep 2021View details →

Electricity demand data and solar generation data from Plymouth. UK

This dataset was used in the Western Power Distribution Presumed Open Data competition in 2021. The data is provided under the Western Power Distribution Open Data Licence. There are five files: pv_train_set4.csv - contains solar panel data - an irradiance, power output and solar panel temperature for each half-hour from 3rd November 2017 through 2nd July 2020. weather_train_set4.csv - contains hourly reanalysis temperature and solar radiation at 6 weather stations near the solar panels (near Plymouth, UK). demand_train_set4.csv - contains half-hourly electricity demand data from a substation near Plymouth, UK, running from 3rd November 2017 through 2nd July 2020. pv_test_set4.csv - contains an extra week of data to pv_train_set4.csv, running from 3rd July 2020 through 9th July 2020. demand_test_set4.csv - contains an extra week of data to demand_train_set4.csv, running from 3rd July 2020 through 9th July 2020.

opencc-by-4.0Nov 2022View details →

University campus buildings electrical and thermal demand and generation.

UVTgv has agreed to share data for both generation and consumption profile of their buildings. The load data is provided as one .csv per building and year with 8760 rows each one representing one hourly consumption of the building. The load datasets comprise 2019 and 2020 load data for electricity and heat demand, for the following buildings: ABR, C, ICSTM. The generation profiles provided are for 3 PV installations (generation profile), one solar thermal plant (capacity factor), and a mini-wind turbine (generation profile).

opencc-by-4.0Feb 2023View details →

Open synthetic data on travel and charging demand of battery electric cars: An agent-based simulation on three charging behavior archetypes

Background Battery electric vehicles (BEVs) are crucial for a sustainable transportation system. As more people adopt BEVs, it becomes increasingly important to accurately assess the demand for charging infrastructure. However, much of the current research on charging infrastructure relies on outdated assumptions, such as the assumption that all BEV owners have access to home chargers and the "Liquid-fuel" mental model. To address this issue, we simulate the travel and charging demand on three charging behavior archetypes. We use a large synthetic population of Sweden, including detailed individual characteristics, such as dwelling types (detached house vs. apartment) and activity plans (for an average weekday). This data repository aims to provide the BEV simulation's input, assumptions, and output so that other studies can use them to study sizing and location design of charging infrastructure, grid impact, etc. A journal paper published in Transportation Research Part D: Transport and Environment details the method to create the data (particularly Section 2.2 BEV simulation). <a href="https://doi.org/10.1016/j.trd.2023.103645">https://doi.org/10.1016/j.trd.2023.103645</a> Methodology This data product is centered on the 1.7 million inhabitants of the Västra Götaland (VG) region, which includes the second largest city in Sweden, Gothenburg. We specifically simulated 284,000 car agents who live in VG, representing 35% of all car users and 18% of the total population in the region. They spend their simulation day (representing an average weekday) in a variety of locations throughout Sweden. This open data repository contains the core model inputs and outputs. The numbers in parentheses correspond to the data sets. We use individual agents' activity plans (1) and travel trajectories from MATSim simulation for the BEV simulation (2), in which we consider overnight charger access (3), car fleet composition referencing the current private car fleet in Sweden (4), and Swedish road network with slope information (5) with realistic BEV charging & discharging dynamics. For the BEV simulation, we tested ten scenarios of charging behavior archetypes and fast charging powers (6). The output includes the time history of travel trajectories and charging of the simulated BEVs across the different scenarios (7). Data description The current data product covers seven data files. (1) Agents' experienced activity plans File name: 1_activity_plans.csv <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> act_id </td> <td> Activity index of each agent </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> deso </td> <td> Zone code of Demographic statistical areas (DeSO)1 </td> <td> String </td> <td> - </td> </tr> <tr> <td> POINT_X </td> <td> Coordinate X of activity location (SWEREF99TM) </td> <td> Float </td> <td> meter </td> </tr> <tr> <td> POINT_Y </td> <td> Coordinate Y of activity location (SWEREF99TM) </td> <td> Float </td> <td> meter </td> </tr> <tr> <td> act_purpose </td> <td> Activity purpose (work, home, other) </td> <td> String </td> <td> - </td> </tr> <tr> <td> mode </td> <td> Transport mode to reach the activity location (car) </td> <td> String </td> <td> - </td> </tr> <tr> <td> dep_time </td> <td> Departure time in decimal hour (0-23.99) </td> <td> Float </td> <td> hour </td> </tr> <tr> <td> trav_time </td> <td> Travel time to reach the activity location </td> <td> String </td> <td> hour:minute:second </td> </tr> <tr> <td> trav_time_min </td> <td> Travel time in decimal minute </td> <td> Float </td> <td> minute </td> </tr> <tr> <td> speed </td> <td> Travel speed to reach the activity location </td> <td> Float </td> <td> km/h </td> </tr> <tr> <td> distance </td> <td> Travel distance between the origin and the destination </td> <td> Float </td> <td> km </td> </tr> <tr> <td> act_start </td> <td> Start time of activity in minute (0-1439) </td> <td> Integer </td> <td> minute </td> </tr> <tr> <td> act_time </td> <td> Activity duration in decimal minute </td> <td> Float </td> <td> minute </td> </tr> <tr> <td> act_end </td> <td> End time of activity in decimal hour (0-23.99) </td> <td> Float </td> <td> hour </td> </tr> <tr> <td> score </td> <td> Utility score of the simulation day given by MATSim </td> <td> Float </td> <td> - </td> </tr> </tbody> </table> 1 <a href="https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/">https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/</a>   (2) Travel trajectories File name: 2_input_zip Produced by MATSim simulation, the zip folder contains ten files (events_batch_X.csv.gz, X=1, 2, …, 10) of input events for the BEV simulation. They are the moving trajectories of the car agents in their simulation days. <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> time </td> <td> Time in second in a simulation day (0-86399) </td> <td> Integer </td> <td> Second </td> </tr> <tr> <td> type </td> <td> Event type defined by MATSim simulation2 </td> <td> String </td> <td> - </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> link </td> <td> Nearest road link consistent with (5) </td> <td> String </td> <td> - </td> </tr> <tr> <td> vehicle </td> <td> Vehicle ID identical to person </td> <td> Integer </td> <td> - </td> </tr> </tbody> </table> 2 One typical episode of MATSim simulation events: Activity ends (actend) -> Agent’s vehicle enters traffic (vehicle enters traffic) -> Agent’s vehicle moves from previous road segment to its next connected one (left link) -> Agent’s vehicle leaves traffic for activity (vehicle leaves traffic) -> Activity starts (actstart)   (3) Overnight charger access File name: 3_home_charger_access.csv <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> home_charger </td> <td> Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes) </td> <td> Integer </td> <td> - </td> </tr> </tbody> </table>   (4) Car fleet composition File name: 4_car_fleet.csv <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> income_class </td> <td> Income group (0=None, 1=below 180K, 2=180K-300K, 3=300K-420K, 4=above 420K) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> car </td> <td> Car model class (B=40 kWh, C=60 kWh, D=100 kWh) </td> <td> String </td> <td> - </td> </tr> </tbody> </table>   (5) Road network with slope information File name: 5_road_network_with_slope.shp (5 files in total) <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> length </td> <td> The length of road link </td> <td> Float </td> <td> meter </td> </tr> <tr> <td> freespeed </td> <td> Free speed </td> <td> Float </td> <td> km/h </td> </tr> <tr> <td> capacity </td> <td> Number of vehicles </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> permlanes </td> <td> Number of lanes </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> oneway </td> <td> Whether the segment is one-way (0=no, 1=yes) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> modes </td> <td> Transport mode (car) </td> <td> String </td> <td> - </td> </tr> <tr> <td> link_id </td> <td> Link ID </td> <td> String </td> <td> - </td> </tr> <tr> <td> from_node </td> <td> Start node of the link </td> <td> String </td> <td> - </td> </tr> <tr> <td> to_node </td> <td> End node of the link </td> <td> String </td> <td> - </td> </tr> <tr> <td> count </td> <td> Aggregated traffic (number of cars travelled per day) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> slope </td> <td> Slope in percent from -6% to 6% </td> <td> Float </td> <td> - </td> </tr> <tr> <td> geometry </td> <td> LINESTRING (SWEREF99TM) </td> <td> geometry </td> <td> meter </td> </tr> </tbody> </table>   (6) Simulation scenarios specifying the parameter sets File name: 6_scenarios.txt <table> <tbody> <tr> <td> Parameter set (paraset) </td> <td> Strategy 1 </td> <td> Strategy 2 </td> <td> Strategy 3 </td> <td> Fast charging power (kW) </td> <td> Minimum parking time for charging (min) </td> <td> Intermediate charging power (kW) </td> </tr> <tr> <td> 0 </td> <td> 0.2 </td> <td> 0.2 </td> <td> 0.9 </td> <td> 150 </td> <td> 5 </td> <td> 22 </td> </tr> <tr> <td> 1 </td> <td> 0.2 </td> <td> 0.2 </td> <td> 0.9 </td> <td> 50 </td> <td> 5 </td> <td> 22 </td> </tr> <tr> <td> 2 </td> <td> 0.3 </td> <td> 0.3 </td> <td> 0.9 </td> <td> 150 </td> <td> 5 </td> <td> 22 </td> </tr> <tr> <td> 3 </td> <td> 0.3 </td> <td> 0.3 </td> <td> 0.9 </td> <td> 50 </td> <td> 5 </td> <td> 22 </td> </tr> </tbody> </table>   (7) Time history of travel trajectories and charging of the simulated BEVs File name: 7_output.zip Produced by the BEV simulation, the zip folder contains four files (parasetX.csv.gz, X=1, 2, 3, 4) corresponding to the four parameter sets specified in (6). They are the moving trajectories of the car agents with simulated energy and charging time history in their simulation days. <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> home_charger </td> <td> Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> car </td> <td> Car model class (B=40 kWh, C=60 kWh, D=100 kWh) </td> <td> String </td> <td> - </td> </tr> <tr> <td> seq </td> <td> Sequence ID of time history by agent </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> time </td> <td> Time (0-86399) </td> <td> Integer </td> <td> Second </td> </tr> <tr> <td> purpose </td> <td> Valid for activities (home, work, school, other) </td> <td> String </td> <td> - </td> </tr> <tr> <td> type </td> <td> Event type defined by MATSim simulation </td> <td> String </td> <td> - </td> </tr> <tr> <td> link </td> <td> Link ID (link_id in File 5) </td> <td> String </td> <td> - </td> </tr> <tr> <td> distance_driven </td> <td> Cumulative driven distance in the simulation day </td> <td> Float </td> <td> km </td> </tr> <tr> <td> energy_1 </td> <td> Energy consumed while driving (-) or charging (+) (Strategy 1) </td> <td> Float </td> <td> kWh </td> </tr> <tr> <td> energy_2 </td> <td> Energy consumed while driving (-) or charging (+) (Strategy 2) </td> <td> Float </td> <td> kWh </td> </tr> <tr> <td> energy_3 </td> <td> Energy consumed while driving (-) or charging (+) (Strategy 3) </td> <td> Float </td> <td> kWh </td> </tr> <tr> <td> charger_1 </td> <td> Power rating of the charger (Strategy 1) </td> <td> Float </td> <td> kW </td> </tr> <tr> <td> charger_2 </td> <td> Power rating of the charger (Strategy 2) </td> <td> Float </td> <td> kW </td> </tr> <tr> <td> charger_3 </td> <td> Power rating of the charger (Strategy 3) </td> <td> Float </td> <td> kW </td> </tr> <tr> <td> soc_1 </td> <td> State of charge (0-1, Strategy 1) </td> <td> Float </td> <td> - </td> </tr> <tr> <td> soc_2 </td> <td> State of charge (0-1, Strategy 2) </td> <td> Float </td> <td> - </td> </tr> <tr> <td> soc_3 </td> <td> State of charge (0-1, Strategy 3) </td> <td> Float </td> <td> - </td> </tr> </tbody> </table>

opencc-by-4.0Sep 2022View details →

Interannual Electricity Demand Calculator

Interannual Electricity Demand Calculator Large parts of this code were originally developed by <a href="https://github.com/L-vdM">Lieke van der Most</a> (University of Groningen) in the EU renewable energy modelling framework and release under MIT license. The original version of the code can be found <a href="https://github.com/L-vdM/EU-renewable-energy-modelling-framework">here</a> and is referenced below as [1]. This model has been validated against historical electricity demand data reported on the <a href="https://transparency.entsoe.eu/">ENTSO-E transparancy platform</a>. We have made the following adjustments to the original version: <ul> <li>generate hourly instead of daily electricity consumption profiles</li> <li>use <code>snakemake</code> for workflow management</li> <li>trim repository to demand-related code and data</li> <li>adjust code to accept cutouts from <code>atlite</code> for weather data</li> </ul> Purpose Variations in weather conditions affect electricity demand patterns. This workflow generates country-level electricity consumption time series based on weather data using analysis by <a href="https://github.com/L-vdM">Lieke van der Most</a> correlating historical electricity demand to temperature. This workflow first calculates a daily electricity demand based on the regression model developed in [1]. Subsequently, cumulative daily electricity demands are disaggregated using a hourly profile sampled from a random historical day (that is the same weekday) from the <a href="https://data.open-power-system-data.org/time_series/">Open Power System Database</a>. The resulting <code>output/demand_hourly.csv</code> file is compatible with the open-source electricity system model <a href="https://github.com/PyPSA/pypsa-eur">PyPSA-Eur</a>. Holidays are treated like weekend days. Data on national holidays across Europe are obtained using another repository by <a href="https://github.com/aleks-g">Aleksander Grochowicz</a> and others that similarly computes artificial electricity demand time series: <a href="https://github.com/aleks-g/multidecade-data/blob/v1.0/load%20data/create_artificial_demand.ipynb">github.com/aleks-g/multidecade-data</a>. The holidays are stored at <code>input_files/noworkday.csv</code>. Installation and Usage Clone the Repository Download the <a href="https://github.com/martacki/demand_calculator">demand_calculator</a> repository using <code>git</code>. <pre><code>/some/other/path % cd /some/path/without/spaces /some/path/without/spaces % git clone https://github.com/martacki/demand_calculator.git</code></pre> Install Dependencies with conda/mamba Use <a href="https://docs.conda.io/en/latest/miniconda.html"><code>conda</code></a> or <a href="https://github.com/QuantStack/mamba"><code>mamba</code></a> to install the required packages listed in <a href="https://github.com/martacki/demand_calculator/blob/master/environment.yaml">environment.yaml</a>. The environment can be installed and activated using <pre><code>.../demand_calculator % conda env create -f environment.yaml .../demand_calculator % conda activate demand</code></pre> Retrieve Input Data The only required additional input files are ERA5 cutouts which can be recycled from the <a href="../record/6382570#.Yx4KN2xByV4">PyPSA-Eur weather data deposit on Zenodo</a>. Place the file <code>europe-2013-era5.nc</code> in the following location (and rename!): <pre><code>./input_files/cutouts/europe-era5-2013.nc</code></pre> Cutouts for other weather years than 2013 can be built using the <code>build_cutout</code> rule from the <a href="https://github.com/PyPSA/pypsa-eur">PyPSA-Eur</a> repository. Run the Workflow This repository uses <code>snakemake</code> for workflow management. To run the complete workflow, execute: <pre><code>.../demand_calculator % snakemake -jall all</code></pre> After successfully running the workflow, the output files will be located in <code>output/energy_demand</code> named <code>demand_hourly_{yr}.csv</code>. The years to compute can be modified directly in the <code>Snakefile</code>. License The file <a href="../api/files/07fb550a-dc1f-4c45-8202-755484c04d67/demand_hourly.csv">demand_hourly.csv</a> is released under CC-BY-4.0 license. The file <a href="../api/files/07fb550a-dc1f-4c45-8202-755484c04d67/src.zip">src.zip </a>is released under MIT license. Changelog 2024-03-15: Extended date range from 1941 to 2023.

opencc-by-4.0Apr 2024View details →

Electricity demand Rome

<div> <div>The data represent the electricity consumption coming from a backbone of the energy supply network in the city of Rome. The time series is sampled every 10 minutes.</div> <div> </div> <div>The datatset has been originally presented in <a href="https://doi.org/10.1109/ACCESS.2015.2485943">Bianchi, Filippo Maria, et al. "Short-term electric load forecasting using echo state networks and PCA decomposition." Ieee Access 3 (2015)</a>.</div> </div>

opencc-by-4.0Oct 2019View details →

EIA_Cleaned_Hourly_Electricity_Demand_Data: 2015 - 2024

<div> Cleaned hourly electricity demand data for electric balancing authorities within the contiguous US.  Raw data is based on the U.S. Energy Information Administration's collected data <a href="http://www.eia.gov/opendata/qb.php?category=2122628">here</a>. Cleaned hourly data spans July 2, 2015 - Dec 31, 2024 (9.5 continuous year).   Please consider citing: Ruggles, T.H., Farnham, D.J., Tong, D. et al. Developing reliable hourly electricity demand data through screening and imputation. Sci Data 7, 155 (2020). <a href="https://doi.org/10.1038/s41597-020-0483-x">https://doi.org/10.1038/s41597-020-0483-x</a> </div>   Since the prior releases of this data, the EIA has limited the range of historical data available via their API. This release focuses on cleaning calendar years 2020 through 2024 and including the many balancing area subregions. Users can combine prior data releases with this latest release to have data extending from from 2 July 2015 through 31 Dec 2024.

opencc-by-nc-4.0Jan 2022View details →

2021 electricity and heat demand data for a city district (Belgium)

Dataset of electricity and heat demand in a city district in Belgium. For the time period of the data, the district was still under construction and no full inhabitation of the buildings was present. Electricity data include electricity demand for: <ul> <li>Individual households</li> <li>EV charging stations</li> <li>Decentralised waste water treatment</li> <li>Heat pump</li> <li>District heating pumps</li> <li>Vacuum network pumps</li> <li>Miscellaneous</li> </ul> 'Total' in the electricity dataset (ElectricPower) refers to the sum of the separate time series. 'Total measured' is a measurement of the total electricity use (in W). Data is averaged out over 15 minutes and expressed in kiloWatt before June 6, in Watt after that date. The electricity demand for the individual households (ElectricPowerPrivateUnits) is expressed in Watt for the complete period. The heat demand data (HeatDemand) describes the heat demand of the complete district, i.e. all private living units as well as common areas, office buildings, sports hall... Like electricity demand data, heat demand data is averaged out over 15 minutes and expressed in kiloWatt before June 6, in Watt after that date.   <div> </div>

opencc-by-4.0Nov 2018View details →

New Alternatives to the Flexibility of Electric Demand

Over the past decades, the role of electric consumers has been increasingly active in terms of their connection with the profile of electricity demand they have. This change in approach has also been supported by the integration of renewable generation sources, which add a certain level of uncertainty to the system.

opencc-by-4.0Aug 2021View details →

PUDL US Hourly Electricity Demand by State

Hourly Electricity Demand by State This archive contains the output of the <a href="https://catalyst.coop/pudl">Public Utility Data Liberation (PUDL) Project</a> state electricity demand allocation analysis, as of the v0.4.0 release of the PUDL Python package. <a href="https://github.com/catalyst-cooperative/pudl/blob/v0.4.0/src/pudl/analysis/state_demand.py">Here is the script</a> that produced this output. It was run using the Docker container and processed data that are included in <a href="https://doi.org/10.5281/zenodo.5214231">PUDL Data Release v2.0.0</a>. The analysis uses hourly electricity demand reported at the balancing authority and utility level in the FERC 714 (<a href="https://doi.org/10.5281/zenodo.4127101">data archive</a>), and service territories for utilities and balancing authorities inferred from the counties served by each utility, and the utilities that make up each balancing authority in the EIA 861 (<a href="https://doi.org/10.5281/zenodo.4127029">data archive</a>), to estimate the total hourly electricity demand for each US state. We used the total electricity sales by state reported in the EIA 861 as a scaling factor to ensure that the magnitude of electricity sales is roughly correct, and obtains the shape of the demand curve from the hourly planning area demand reported in the FERC 714. The scaling is necessary partly due to imperfections in the historical utility and balancing authority service territory maps which we have been able to reconstruct from the data reported in the EIA 861 Service Territories and Balancing Authority tables. The compilation of historical service territories based on the EIA 861 data is somewhat manual and could be improved, but overall the results seem reasonable. Additional predictive spatial variables will be required to obtain more granular electricity demand estimates (e.g. at the county level). FERC 714 Respondents The file <code>ferc714_respondents.csv</code> links FERC Form 714 respondents to what we believe to be their corresponding EIA utilities or balancing authorities. <ul> <li><code>eia_code</code>: An integer ID reported in the FERC Form 714 corresponding to the respondent's EIA ID. In some cases this is a Utility ID, and in others it is a Balancing Authority ID, but which is not specified and so we have had to infer the type of entity which is responding. Note that in many cases the same company acts as both a utility and a balancing authority, and the integer ID associated with the company is often the same in both roles, but it does not need to be.</li> <li><code>respondent_type</code>: Either <code>balancing_authority</code> or <code>utility</code> depending on which type of entity we believe was responding to the FERC 714.</li> <li><code>respondent_id_ferc714</code>: The integer ID of the responding entity within the FERC 714.</li> <li><code>respondent_name_ferc714</code>: The name provided by the respondent in the FERC 714.</li> <li><code>balancing_authority_id_eia</code>: If the respondent was identified as a balancing authority, the EIA ID for that balancing authority, taken from the EIA Form 861.</li> <li><code>balancing_authority_code_eia</code>: If the respondent was identified as a balancing authority, the EIA short code used to identify the balancing authority, taken from the EIA Form 861.</li> <li><code>balancing_authority_name_eia</code>: If the respondent was identified as a balancing authority, the name of the balancing authority, taken from the EIA Form 861.</li> <li><code>utility_id_eia</code>: If the respondent was identified as a utility, the EIA utility ID, taken from the EIA Form 861.</li> <li><code>utility_name_eia</code>: If the respondent was identified as a utility, the name of the utility, taken from the EIA 861.</li> </ul> FERC 714 Respondent Service Territories The file <code>ferc714_service_territories.csv</code> describes the historical service territories for FERC 714 respondents for the years 2006-2019. For each respondent and year, their service territory is composed of a collection of counties, identified by their 5-digit FIPS codes. The file contains the following columns, with each row associating a single county with a FERC 714 respondent in a particular year: <ul> <li><code>respondent_id_ferc714</code>: The FERC Form 714 respondent ID, which is also found in <code>ferc714_respondents.csv</code></li> <li><code>report_date</code>: The first day of the year for which the service territory is being described.</li> <li><code>state</code>: Two letter abbreviation for the state containing the county, for human readability.</li> <li><code>county</code>: The name of the county, for human readability.</li> <li><code>state_id_fips</code>: The 2-digit <a href="https://en.wikipedia.org/wiki/Federal_Information_Processing_Standard_state_code">FIPS state code</a>.</li> <li><code>county_id_fips</code>: The 5-digit <a href="https://en.wikipedia.org/wiki/FIPS_county_code">FIPS county code</a> for use with other geospatial data resources, like the <a href="https://www.census.gov/geographies/mapping-files/2010/geo/tiger-data.html">US Census DP1 geodatabase</a>.</li> </ul> State Hourly Electricity Demand Estimates The file <code>demand.csv</code> contains hourly electricity demand estimates for each US state from 2006-2019. It contains the following columns: <ul> <li><code>state_id_fips</code>: The 2-digit FIPS state code.</li> <li><code>utc_datetime</code>: UTC time at hourly resolution.</li> <li><code>demand_mwh</code>: Electricity demand for that state and hour in MWh. This is an allocation of the electricity demand reported directly in the FERC Form 714.</li> <li><code>scaled_demand_mwh</code>: Estimated total electricity demand for that state and hour, in MWh. This is the reported FERC Form 714 hourly demand scaled up or down linearly such that the total annual electricity demand matches the total annual electricity sales reported at the state level in the EIA Form 861.</li> </ul> A collection of plots are also included, comparing the original and scaled demand time series for each state. Acknowledgements This analysis was funded largely by <a href="https://gridlab.org/">GridLab</a>, and done in collaboration with researchers at the Lawrence Berkeley National Laboratory, including <a href="https://eta.lbl.gov/people/umed-paliwal-0">Umed Paliwal</a> and <a href="https://eta.lbl.gov/people/nikit-abhyankar">Nikit Abhyankar</a>. <ul> <li><a href="https://scholar.google.com/citations?user=q_rBGYgAAAAJ&hl=en">Ethan Welty</a> wrote the final code and most of the algorithms.</li> <li><a href="https://www.linkedin.com/in/yash-kumar/">Yash Kumar</a> did initial data explorations and geospatial analyses.</li> </ul> The data screening methods were originally designed to identify unrealistic data in the electricity demand timeseries reported to EIA on Form 930, and have been applied here to data form the FERC Form 714. They are adapted from code published and modified by: <ul> <li><a href="mailto:truggles@carnegiescience.edu">Tyler Ruggles</a></li> <li><a href="mailto:greg@carbonimpact.co">Greg Schivley</a></li> </ul> And described at: <ul> <li><a href="https://doi.org/10.1038/s41597-020-0483-x">Developing reliable hourly electricity demand data through screening and imputation</a></li> <li><a href="https://doi.org/10.5281/zenodo.3737085">EIA Cleaned Hourly Electricity Demand Code</a> (Zenodo)</li> <li><a href="https://github.com/truggles/EIA_Cleaned_Hourly_Electricity_Demand_Code">EIA Cleaned Hourly Electricity Demand Code</a> (GitHub)</li> </ul> The imputation methods were designed for multivariate time series forecasting. They are adapted from code published by: <ul> <li>Xinyu Chen <a href="mailto:chenxy346@gmail.com">chenxy346@gmail.com</a></li> </ul> And described at: <ul> <li><a href="https://arxiv.org/abs/2006.10436">Low-Rank Autoregressive Tensor Completion for Multivariate Time Series Forecasting</a></li> <li><a href="https://arxiv.org/abs/2008.03194">Scalable Low-Rank Tensor Learning for Spatiotemporal Traffic Data Imputation</a></li> <li><a href="https://github.com/xinychen/tensor-learning">Tensor Learning (张量学习)</a></li> </ul> About PUDL & Catalyst Cooperative For additional information about this data and PUDL, see the following resources: <ul> <li><a href="https://github.com/catalyst-cooperative/pudl">The PUDL Repository on GitHub</a></li> <li><a href="https://readthedocs.org/projects/catalystcoop-pudl/">The PUDL Documentation</a></li> <li><a href="https://zenodo.org/communities/catalyst-cooperative/">Other Catalyst Cooperative data archives on Zenodo</a></li> </ul>

opencc-by-4.0Apr 2023View details →

2022 electricity and heat demand data for a city district (Belgium)

Dataset of electricity and heat demand in 2022 in a city district in Belgium (similar data for 2021 is available on Zenodo as well, https://doi.org/10.5281/zenodo.5155659). For the time period of the data, the district was still under construction and no full inhabitation of the buildings was present. Electricity data include electricity demand for: <ul> <li>Individual households</li> <li>EV charging stations</li> <li>Decentralised waste water treatment</li> <li>Heat pump</li> <li>District heating pumps</li> <li>Vacuum network pumps</li> <li>Miscellaneous</li> </ul> 'Total' in the electricity dataset (ElectricPower) refers to the sum of the separate time series. 'Total measured' is a measurement of the total electricity use (in W). Data is averaged out over 15 minutes and expressed in Watt.  The electricity demand for the individual households (ElectricPowerPrivateUnits) is expressed in Watt for the complete period. Column names have the form x.y in which x is a random number assigned to an apartment and y refers to the electricity consumption during the day (1) or at night (2). The heat demand data (HeatDemand) describes the heat demand of the complete district, i.e. all private living units as well as common areas, office buildings, sports hall... Like electricity demand data, heat demand data is averaged out over 15 minutes and expressed in Watt. <div> </div>

opencc-by-4.0Apr 2023View details →

Dataset article "Demand-Response Control of Electric Storage Water Heaters Based on Dynamic Electricity Pricing and Comfort Optimization"

"README-SupplementaryMaterial.txt" explains the information gathered in each csv files, and including the DHW consumption profiles generated, the hourly electricity pricing for 2022 (Spain), and the experimental data utilized for the validation of the model.

opencc-by-4.0May 2023View details →

SECURES-Met - A European wide meteorological data set suitable for electricity modelling (supply and demand) for historical climate and climate change projections

For the modelling of electricity production and demand, meteorological conditions are becoming more relevant due to the increasing contribution from renewable electricity production. But the requirements on meteorological data sets for electricity modelling are quite high. One challenge is the high temporal resolution, since a typical time step for modelling electricity production and demand is one hour. On the other side the European electricity market is highly connected, so that a pure country based modelling does not make sense and at least the whole European Union area has to be considered. Additionally, the spatial resolution of the data set must be able to represent the thermal conditions, which requires high spatial resolution at least in mountainous regions. All these requirements lead to huge data amounts for historic observations and even more for climate change projections for the whole 21st century. Thus, we have developed an aggregated European wide data set that has a temporal resolution of one hour, covers the whole EU area, has a reasonable size but is considering the high spatial variability. This meteorological data set for Europe for the historical period and climate change projections fulfills all relevant criteria for energy modelling. It has a hourly temporal resolution, considers local effects up to a spatial resolution of 1 km and has a suitable size, as all variables are aggregated to NUTS regions. Additionally meteorological information from wind speed and river run-off is directly converted into power productions, using state of the art methods and the current information on the location of power plants. Within the research project SECURES (https://www.secures.at/) this data set has been widely used for energy modelling.   The SECURES-Met dataset provides variables visible in the table. <table> <tbody><tr> <th>Variable</th> <th>Short name</th> <th>Unit</th> <th>Aggregation methods</th> <th>Temporal resolution</th> </tr> </tbody><tbody> <tr> <th>Temperature (2m)</th> <td>T2M</td> <td> °C °C </td> <td> spatial mean population weighted mean (recommended) </td> <td>hourly</td> </tr> <tr> <th>Radiation</th> <td> GLO (mean global radiation) BNI (direct normal irradiation) </td> <td> Wm-2 Wm-2 </td> <td> spatial mean population weighted mean (recommended) </td> <td>hourly</td> </tr> <tr> <th>Potential Wind Power </th> <td>WP</td> <td>1</td> <td>normalized with potentially available area</td> <td>hourly</td> </tr> <tr> <th>Hydro Power Potential</th> <td> HYD-RES (reservoir) HYD-ROR (run-of-river) </td> <td> MW 1 </td> <td> summed power production summed power production normalized with average daily production </td> <td>daily</td> </tr> </tbody> </table>                           SECURES-Met is available in a tabular csv format for the historical period (1981-2020, Hydro only until 2010) created from ERA5 and ERA5-Land and two future emission scenarios (RCP 4.5 and RCP 8.5, both 1951-2100, wind power starting from 1981, hydro power from 1971) created from one CMIP5 EUROCORDEX model (GCM:  ICHEC-EC-EARTH, RCM: KNMI-RACMO22E, ensemble run: r12i1p1) on the spatial aggregation level <ul> <li>NUTS0 (country-wide),</li> <li>NUTS2 (province-wide),</li> <li>NUTS3 (Austria only),</li> <li>and EEZ (Exclusive Economic Zones, offshore only).</li> </ul> The data is divided into the historical (Historical.zip) and the two emission scenarios (Future_RCP45.zip and Future_RCP85.zip), a README file, which describes, how the files are organized,  and a folder (Meta.zip), which has information and shape files of the different NUTS levels. As population weighted temperature and radiation represent values in geographical areas more relevant for solar power, it is highly relevant to use population weighted files. Spatial mean should be used for reference only. The project SECURES, in which this dataset was produced, was funded by the Climate and Energy Fund (Klima- und Energiefonds) under project number KR19AC0K17532.

openodc-odblJun 2022View details →

Supplementary data: "Open modeling of electricity and heat demand curves for all residential buildings in Germany"

This repository contains supplementary data for the paper <a href="https://doi.org/10.1186/s42162-022-00201-y"> "Open modeling of electricity and heat demand curves for all residential buildings in Germany"</a>. See README.md / README.pdf for further details. Citing Please cite as: Büttner, C., Amme, J., Endres, J. et al. Open modeling of electricity and heat demand curves for all residential buildings in Germany. Energy Inform 5 (Suppl 1), 21 (2022). Funding The authors thank the Federal Ministry for Economic Affairs and Climate Action for funding the research project eGon (funding code: 03EI1002).

opencc-by-4.0Oct 2022View details →

ENTSO-E Electricity Demand - 01/2016 - 09/2022

Electricity demand for ten European countries downloaded from the ENTSO-E Transparency Platform the 1st October 2022. The data covers the period 01/01/2016 - 31/09/2022. Saved in Parquet format.

opencc-by-4.0Jul 2024View details →

Data Files for "Subsidies for Close Substitutes: Aggregate Demand for Residential Solar Electricity"

Data files for  "Subsidies for Close Substitutes: Aggregate Demand for Residential Solar Electricity" [https://doi.org/10.1016/j.euroecorev.2024.104848]. Findings of the paper can be replicated using these data files, along with code at https://github.com/xabajian/AP_Solar/. Please contact Alexander Abajian <xander.abajian@gmail.com> with any questions regarding the enclosed files.

opencc-by-nc-4.0Aug 2018View details →

Raw data belonging to paper "Calculating Retail Prices from Demand Response Target Schedules to Operate Domestic Electric Water Heaters"

The Zip file contains the raw data used for drawing conclusions in the paper "Calculating Retail Prices from Demand Response Target Schedules to Operate Domestic Electric Water Heaters" accepted for publication in Energy Informatics 2018. The raw data is the parameters of a sample of 50 domestic electric water heaters (DEWHs) used for evaluating the algorithm in the paper. It is explained in the readme.txt.