Find research datasets worth reusing

opencc-by-4.0Aug 2021View details →

Mobility demand mesh-grid datasets

<ul> <li>15m_flat_bike_count.h5: Table that contains the number of bike rides that started on every bike station in Chicago, per time interval. Time resolution is 15min, covering 2013-2020 (280512 timestamps).</li> <li>15m_flat_bike_norm_abs.h5: Same as 15m_flat_bike_count.h5, but normalized using the maximum number of rides recorded in that period, i.e.: 84. The normalization is calculated as x' = (x - min(X)) / (max(X) - min(X)).</li> <li>15m_flat_taxi_count.h5: Table that contains the number of taxi trip counts that started on every taxi zone in Chicago, per time interval. Time resolution is 15min, covering 2013-2020 (280512 timestamps).</li> <li>15m_flat_taxi_norm_abs.h5: Same as 15m_flat_taxi_count.h5, but normalized using the maximum number of trips recorded in that period, i.e.: 418. The normalization is calculated as x' = (x - min(X)) / (max(X) - min(X)).</li> <li>15m_map_90_60_bike_norm_abs.h5: Mobility mesh-grid for bikes, calculated from 15m_flat_bike_norm_abs.h5 counting the total number of rides per element of the grid and time interval. The final shape of the dataset is 280512 x 90 x 60.</li> <li>15m_map_90_60_taxi_norm_abs.h5: Mobility mesh-grid for taxis, calculated from 15m_flat_taxi_norm_abs.h5 using linear interpolation. The final shape of the dataset is 280512 x 90 x 60.</li> <li>station-locations-bike.csv: Geolocations of the bicycle racks of  Chicago.</li> <li>zone-centroids-taxi.csv: Geolocations of the centroids of the taxi zones of Chicago.</li> <li>grid-locations-bike.csv: Locations of the elements of a 90x60 grid overlaying Chicago that contain bicycle racks.</li> <li>holidays.csv: Holidays in Chicago for the period 2013-2020.</li> </ul>

opencc-by-4.0Aug 2021View details →

GENeSYS-MOD Germany: Technology, demand, and renewable data

This dataset contains renewable potentials, timeseries, technology data, and additional data tables for the current implementation of GENeSYS-MOD Germany.

A computational intelligence approach to predict energy demand using Random Forest in a Cloudera cluster

Society’s energy consumption has shot up in recent years, making the prediction of its demand a current challenge to ensure an efficient and responsible use. Artificial intelligence techniques have proven to be potential tools in handling tedious tasks and making sense of large-scale data to make better business decisions in different areas of knowledge. In this article, the use of random forests algorithms in a Big Data environment is proposed for households energy demand forecasting. The predictions are based on the use of information from different sources, confirming a fundamental role of socioeconomic data in consumer’s behaviours. On the other hand, the use of Big Data architectures is proposed to perform horizontal and vertical scaling of the solution to be used in real environments. Finally, a tool for high-resolution predictions with great efficiency is introduced, which enables energy management in a very accurate way. Raw data is incuded in data.csv. This file contains half hourly home electricity consumption registers for 4404 households with fix tariffs (not subject to dynamic time of use) for a period between November 2011 and February 2014. Original information was acquired from the Low Carbon London project led by UK Power Networks (https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households) RFResults.zip contains the energy predictions for each ACORN group using the generated Random Forest algorithm. For this purpose, the first 613 days of a total of 818 observations of each group were considered for training and the last 205 days for testing. Meteorological data was adquired from the darksky app (https://darksky.net). These data are included in the weather_hourly_darksky.csv uk_bank_holidays. xlsx contains the dated of UK bank holidays for the studied period, used as additional variable related to occupancy

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.

Sensitivities to temperature and evaporative demand in wheat relatives

Data used in the paper "Sensitivities to temperature and evaporative demand in wheat relatives" accepted by Journal of Experimental Botany. There are three tables: <ul> <li>Metadata: genotype information</li> <li>Definition: Definition, symbol and unit of the variables used</li> <li>Genotype variables: set of variables used for analyses, figures and tables in the article. </li> </ul>

Climate change impacts on energy demand

Climate change impacts on energy demand by energy carrier (electricity, natural gas, and petroleum) and sector (agriculture, industry, residential, and commercial).

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-4.0Oct 2022View details →

openENTRANCE - Case Study 1 - Residential Demand Response - Data and Scripts

Data files and Python and R scripts are provided for Case Study 1 of the openENTRANCE project. The data covers 10 residential devices on the NUTS2 level for the EU27 + UK +TR + NO + CH from 2020-2050. The devices included are full battery electric vehicles (EV), storage heater (SH), water heater with storage capabilitites (WH), air conditiong (AC), heat circulation pump (CP), air-to-air heat pump (HP), refrigeration (includes refrigerators (RF) and freezers (FR)), dish washer (DW), washing machine (WM), and tumble drier (TD). The data for the study uses represenative hours to describe load expectations and constraints for each residential device - hourly granularity from 2020 to 2050 for a representative day for each month (i.e. 24 hours for an average day in each month). The aggregated final results are in Full_potential.V9.csv and acheivable_NUTS2_summary.csv. The file metaData.Full_Potential.csv is provided to guide users on the nomenclature in Full_potential.V9.csv and the disaggregated data sets.The disaggregated loads can be found in d_ACV8.csv, d_CPV6.csv, d_DWV6.csv, d_EVV7.csv, d_FRV5.csv, d_HPV4.csv, d_RFV5.csv, d_SHV7.csv, d_TDV6.csv, d_WHV7.csv, d_WMV6.csv while the disaggregated maximum capacities p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv.  Full_potential.V9.csv shows the NUTS2 level unadjusted loads for the residential devices using representative hours from 2020-2050. The loads provided here have not been adjusted with the direct load participation rates (see paper for more details). More details on the dataset can be found in the metaData.Full_Potential.csv file. The acheivable_NUTS2_summary.csv shows the NUTS2 level acheivable direct load control potentials for the average hour in the respective year (years - 2020, 2022,2030,2040, 2050). These summaries have allready adjusted the disaggregated loads with direct load participation rates from participation_rates_country.csv. A detailed overview of the data files are provided below. Where possible, a brief description, input data, and script use to generate the data is provided. If questions arise, first refer to the publication. If something still needs clarification, send an email to ryano18@vt.edu. Description of data provided <ol> <li>Achievable_NUTS2_summary.csv <ol> <li>Description <ol> <li>Average hourly achievable direct load potentials for each NUTS2 region and device for 2020, 2022, 2030,2040, 2050</li> </ol> </li> <li>Data input <ol> <li>Full_potential.V9.csv</li> <li>participation_rates_country.csv</li> <li>P_inc_SH.csv</li> <li>P_inc_WH.csv</li> <li>P_inc_HP.csv</li> <li>P_inc_DW.csv</li> <li>P_inc_WM.csv</li> <li>P_inc_TD.csv</li> </ol> </li> <li>Script <ol> <li>NUTS2_acheivable.R</li> </ol> </li> </ol> </li> <li>COP_.1deg_11-21_V1.csv <ol> <li>Description <ol> <li>NUTS2 average coefficient of performance estimates from 2011-2021 daily temperature</li> </ol> </li> <li>Data <ol> <li>tg_ens_mean_0.1deg_reg_2011-2021_v24.0e.nc</li> <li>NUTS_RG_01M_2021_3857.shp</li> <li>nhhV2.csv</li> </ol> </li> <li>Script <ol> <li>COP_from_E-OBS.R</li> </ol> </li> </ol> </li> <li>Country dd projections.csv <ol> <li>Description <ol> <li>Assumptions for annual change in CDD and HDD</li> <li>Spinoni, J., Vogt, J. V., Barbosa, P., Dosio, A., McCormick, N., Bigano, A., & Füssel, H. M. (2018). Changes of heating and cooling degree‐days in Europe from 1981 to 2100. International Journal of Climatology, 38, e191-e208.</li> <li>Expectations for future HDD and CDD used the long-run averages and country level expected changes in the rcp45 scenario</li> </ol> </li> </ol> </li> <li>EV NUTS projectionsV5.csv <ol> <li>Description <ol> <li>NUTS2 level EV projections 2018-2050</li> </ol> </li> <li>Data input <ol> <li>EV projectionsV5_ave.csv <ol> <li>Country level EV projections</li> </ol> </li> <li>NUTS 2 regional share of national vehicle fleet <ol> <li>Eurostat - Vehicle Nuts.xlsx</li> </ol> </li> </ol> </li> <li>Script <ol> <li>EVprojections_NUTS_V5.py</li> </ol> </li> </ol> </li> <li>EV_NVF_EV_path.xlsx <ol> <li>Description <ol> <li>Country level – EV share of new passenger vehicle fleet</li> <li>From: Mathieu, L., & Poliscanova, J. (2020). Mission (almost) accomplished. Carmakers’ Race to Meet the, 21.</li> </ol> </li> </ol> </li> <li>EV_parameters.xlsx <ol> <li>Description <ol> <li>Parameters used to calculate future loads from EVs</li> <li>Wunit_EV – represents annual kWh per EV</li> <li>evLIFE_150kkm <ol> <li>number of years</li> <li>represents usable life if EV only lasted 150 thousand km. Hence, 150,000/average km traveled per year with respect to country (this variable is dropped and not used for estimation).</li> </ol> </li> <li>Average age/#years assuming 150k life – represents <ol> <li>Number of years</li> <li>Average between evLIFE_150kkm and average age of vehicle with respect to the country</li> </ol> </li> </ol> </li> </ol> </li> <li>full_potentialV9.csv <ol> <li>Description <ol> <li>Final data that shows hourly demand (Maximum Reduction) and (Maximum Dispatch for each device, region, and year. <ol> <li>This data has not been adjusted with participation_rates_country.csv</li> <li>Maximum dispatch is equal to max capacity – hourly demand with respect to the device, region, year, and hour.</li> </ol> </li> </ol> </li> <li>Script <ol> <li>Full_potentialV9.py</li> </ol> </li> </ol> </li> <li>gils projection assumptions.xlsx <ol> <li>Description <ol> <li>Data from: Gils, H. C. (2015). Balancing of intermittent renewable power generation by demand response and thermal energy storage.</li> <li>A linear extrapolation was used to determine values for every year and country 2020-2050. AC – Air Conditioning, SH – Storage Heater, WH – Water heater with storage capability, CP – heat circulation pump, TD – Tumble Drier, WM – Washing Machine, DW -Dish Washer, FR – Freezer, RF – Refrigerator. The results are in the files shown below. <ol> <li>nflh – full load hours <ol> <li>nflh_ac.csv</li> <li>nflh_cp.csv</li> </ol> </li> <li>wunit – annual energy consumption <ol> <li>Wunit_rf_fr.csv</li> </ol> </li> <li>Pcycle – power demand per cycle <ol> <li>Pcycle_wm.csv</li> <li>Pcycle_dw.csv</li> <li>Pcycle_td.csv</li> </ol> </li> <li>Punit – power damand for device <ol> <li>Punit_ac.csv</li> <li>Punit_cp.csv</li> </ol> </li> <li>r – country level household ownership rates of residential device <ol> <li>rfr.csv</li> <li>rrf.csv</li> <li>rwm.csv</li> <li>rtd.csv</li> <li>rdw.csv</li> <li>rac.csv</li> <li>rwh.csv</li> <li>rcp.csv</li> <li>rsh.csv</li> </ol> </li> <li>Script <ol> <li>openENTRANCE projections.py</li> </ol> </li> </ol> </li> </ol> </li> </ol> </li> <li>heat_pump_hourly_share.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand</li> <li>From ENTROS TYNDP – Charts and Figures <ol> <li><a href="https://2020.entsos-tyndp-scenarios.eu/download-data/#download">https://2020.entsos-tyndp-scenarios.eu/download-data/#download</a></li> </ol> </li> </ol> </li> </ol> </li> <li>hourlyEVshares.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand</li> <li>From My Electric Avenue Study <ol> <li><a href="https://eatechnology.com/consultancy-insights/my-electric-avenue/">https://eatechnology.com/consultancy-insights/my-electric-avenue/</a></li> </ol> </li> </ol> </li> </ol> </li> <li>HP_transitionV2.csv <ol> <li>Description <ol> <li>Used to create Qhp_thermal_MWh_projectedV2.csv</li> <li>Final_energy_15-19 <ol> <li>Average final energy demand for the residential heating sector between 2015-2019</li> </ol> </li> <li>Final_energy_15-19_nonEE <ol> <li>Average final energy demand for the residential heating sector for energy sources that are not energy efficient between 2015-2019 (see paper for sources)</li> </ol> </li> <li>Final_energy_15-19_nonEE_share <ol> <li>share of inefficient heating sources</li> </ol> </li> <li>HP_thermal_2018 <ol> <li>Thermal energy provided by residential heat pumps in 2018</li> </ol> </li> <li>HP_thermal_2019 <ol> <li>Thermal energy provided by residential heat pumps in 2019</li> </ol> </li> <li>See publication for data sources</li> </ol> </li> </ol> </li> <li>Nflh_ac.csv, nflh_cp.csv <ol> <li>See gils projection assumptions.xlsx</li> </ol> </li> <li>nhhV2 <ol> <li>Description <ol> <li>Expected number of households for NUTS2 regions for 2020-2050</li> <li>See publication for data sources</li> </ol> </li> <li>Script <ol> <li>EUROSTAT_POP2NUTSV2.R</li> </ol> </li> </ol> </li> <li>NUTS0_thermal_heat_annum.csv <ol> <li>Description <ol> <li>Country level residential annual thermal heat requirements in kWh</li> <li>Used to determine maximum dispatch in openENTRANCE final V14.py</li> <li>Mantzos, L., Wiesenthal, T., Matei, N. A., Tchung-Ming, S., Rozsai, M., Russ, P., & Ramirez, A. S. (2017). JRC-IDEES: Integrated Database of the European Energy Sector: Methodological Note (No. JRC108244). Joint Research Centre (Seville site).</li> </ol> </li> </ol> </li> <li>p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv <ol> <li>Description <ol> <li>Maximum capacity – load for a device can never exceed maximum capacity</li> </ol> </li> <li>Data <ol> <li>gils projection assumptions.xlsx</li> </ol> </li> <li>Script <ol> <li>openENTRANCE final V14.py</li> </ol> </li> </ol> </li> <li>P_inc_DW.csv, P_inc_HP.csv, P_inc_SH.csv, P_inc_TD.csv, P_inc_WH.csv, P_inc_WM.csv, SAMPLE_PINC.csv <ol> <li>Description <ol> <li>Unadjusted average hourly potential for increase by NUTS2 region for 2018-2050</li> </ol> </li> <li>Data <ol> <li>d_ACV8.csv, d_CPV6.csv, d_DWV6.csv, d_EVV7.csv, d_FRV5.csv, d_HPV4.csv, d_RFV5.csv, d_SHV7.csv, d_TDV6.csv, d_WHV7.csv, d_WMV6.csv <ol> <li>Theoretical maximum reduction / load of the respective device</li> </ol> </li> <li>p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv <ol> <li>Maximum capacity</li> </ol> </li> </ol> </li> <li>Script <ol> <li>P_increaseV2.py</li> </ol> </li> </ol> </li> <li>Pcycle_dw.csv, Pcycle_td.csv, Pcycle_wm.csv <ol> <li>Description <ol> <li>power demand per cycle kWh</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol> </li> <li>Punit_ac.csv, Punit_cp.csv <ol> <li>Description <ol> <li>Unit capacities kWh</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol> </li> <li>Qhp_thermal_MWh_projectedV2.csv <ol> <li>Description <ol> <li>NUTS2 expectations for thermal energy demand met by heat pumps for 2022-2050</li> <li>Assumes a linear decomposition of non-renewable and non-energy efficient heating sources until 2050</li> </ol> </li> <li>Data <ol> <li>HP_transitionV2.csv</li> <li>nhhV2.csv</li> </ol> </li> <li>Script <ol> <li>HP_projection_nuts.py</li> </ol> </li> </ol> </li> <li>rac.csv, rcp.csv, rdw.csv, rfr.csv, rrf.csv, rsh.csv, rtd.csv, rwh.csv, rwm.csv <ol> <li>Description <ol> <li>Household ownership rates</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol> </li> <li>s_hdd nutsV3.csv, s_cdd nutsV3.csv, yr_hdd nutsV3.csv, yr_cdd nutsV3.csv <ol> <li>Description <ol> <li>s_hdd nutsV3.csv and s_cdd nutsV3.csv – months share of total heating and cooling degree days (yr_hdd and yr_cdd respectively)</li> <li>yr_hdd nutsV3.csv and yr_cdd nutsV3.csv – annual heating and cooling degree days respectively</li> <li>long run (2011-2021) average NUTS 2 level hdd and cdd</li> </ol> </li> </ol> </li> <li>s_wash nuts_V2.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand for washing machine, tumble drier, and dishwasher</li> </ol> </li> <li>Data <ol> <li>stamminger_V2.xlsx</li> </ol> </li> <li>Script <ol> <li>S_wash_nuts_V2.py</li> </ol> </li> </ol> </li> <li>Stamminger_2009.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand for water heater – WH, storage heater – SH, air conditioner AC, heat circulation pump – CP</li> <li>From Stamminger, R. (2009). Synergy potential of smart domestic appliances in renewable energy systems.</li> </ol> </li> </ol> </li> <li>Time_index.csv <ol> <li>Used to create the appropriate timestamp for representative hours</li> </ol> </li> <li>Wunit_rf_fr.csv <ol> <li>Annual energy consumption for refrigeration and freezers</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol>

opencc-by-4.0Dec 2019View details →

Data related to the manuscript "Bayesian Calibration and Validation of a Large-scale and Time-demanding Sediment Transport Model"

1) Riverbed_Elevation_Measurements.txt     Description: Measured riverbed geometry of available years     Columns: Node ID, Easting [m], Northig [m], Elevation 2002 [m asl], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation        2013 [m asl] ---------------------------------------------------------------------------------------------------------------------------- 2) Hydro_FT_2D_manual.txt     Description: Simulation results of the manually calibrated full model     Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl] 3.1) Hydro_FT_2D_CollocationPointBase.txt     Description: Parameter combinations of the collocation point base for each of the 20 simulations conducted with the full model to      construct the surrogate     Rows: Critical Shields parameter, Grain Roughness, Grain Size distribution 3.2) Hydro_FT_2D_CollocationResults.txt     Description: Simulation results of the 20 simulations conducted with the full model at the collocation points     Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of simulation 1 through 20, Node ID, Easting [m asl], Northig     [m asl], Elevations 2010 [m asl] of simulation 1 through 20, Node ID, Easting [m asl], Northig [m asl], Elevations 2013 [m asl] of     simulation 1 through 20 ---------------------------------------------------------------------------------------------------------------------------- 4.1) aPC_MC_N_Combinations_Weights_prior.txt     Description: ID of prior MC runs with tested parameter combinations and corresponding importance weights     Rows: ID of MC runs, Critical Shields parameter, Grain Roughness, Grain Size distribution, importance weights 4.2) aPC_MC_2005_prior.txt     Description: aPC surrogate results of prior MC runs for 2005     Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of MC run 1 through 100,000 4.3) aPC_MC_2010_prior.txt     Description: aPC surrogate results of prior MC runs for 2010     Columns: Node ID, Easting [m], Northig [m], Elevations 2010 [m asl] of MC run 1 through 100,000 4.4) aPC_MC_2013_prior.txt     Description: aPC surrogate results of prior MC runs for 2013     Columns: Node ID, Easting [m], Northig [m], Elevations 2013 [m asl] of MC run 1 through 100,000      4.5) aPC_MC_N_Combinations_Weights_posterior.txt     Description: ID of accepted (posterior) MC runs with tested parameter combinations and corresponding importance weights     Rows: ID of accepted MC runs, Critical Shields parameter, Grain Roughness, Grain Size distribution, importance weights 4.6) aPC_MC_2005_posterior.txt     Description: aPC surrogate results of posterior MC runs for 2005     Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of accepted MC run 1 through 857 4.7) aPC_MC_2010_posterior.txt     Description: aPC surrogate results of posterior MC runs for 2010     Columns: Node ID, Easting [m], Northig [m], Elevations 2010 [m asl] of accepted MC run 1 through 857 4.8) aPC_MC_2013_posterior.txt     Description: aPC surrogate results of posterior MC runs for 2013     Columns: Node ID, Easting [m], Northig [m], Elevation 2013 [m asl] of accepted MC run 1 through 857 ---------------------------------------------------------------------------------------------------------------------------- 5) aPC_MAP.txt     Description: Simulation results conducted with the stochastically calibrated aPC surrogate model using the MAP parameter      combination     Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl] 6) Hydro_FT_2D_MAP.txt     Description: Simulation results conducted with the stochastically calibrated full model using the MAP parameter combination     Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl] ---------------------------------------------------------------------------------------------------------------------------- 7) dz.txt     Description: Riverbed Evolution for all nodes in the section of interest (n=1138) obtained with differently calibrated models for all         considered time periods     Columns: Node ID, Easting [m asl], Northig [m asl], aPC_prior 2005 [m], aPC_posterior 2005 [m], aPC_MAP 2005 [m],      Hydro_FT-2D_MAP 2005 [m], Hydro_FT-2D_manual 2005 [m], aPC_prior 2010 [m], aPC_posterior 2010 [m], aPC_MAP 2010 [m],     Hydro_FT-2D_MAP 2010 [m], Hydro_FT-2D_manual 2010 [m], aPC_prior 2013 [m], aPC_posterior 2013 [m], aPC_MAP 2013 [m],     Hydro_FT-2D_MAP 2013 [m], Hydro_FT-2D_manual 2013 [m] 8) dz_CalibrationNodes.txt     Description: Riverbed Evolution for calibration nodes (n=204) obtained with differently calibrated models for all considered time     periods     Columns: Node ID, Easting [m asl], Northig [m asl], aPC_prior 2005 [m], aPC_posterior 2005 [m], aPC_MAP 2005 [m],      Hydro_FT-2D_MAP 2005 [m], Hydro_FT-2D_manual 2005 [m], aPC_prior 2010 [m], aPC_posterior 2010 [m], aPC_MAP 2010 [m],     Hydro_FT-2D_MAP 2010 [m], Hydro_FT-2D_manual 2010 [m], aPC_prior 2013 [m], aPC_posterior 2013 [m], aPC_MAP 2013 [m],     Hydro_FT-2D_MAP 2013 [m], Hydro_FT-2D_manual 2013 [m]

opencc-by-4.0Jan 2020View details →

Table S3. List of Locustella sound recordings included in bioacoustic analysis surrounding description of the Taliabu Grasshopper-Warbler. The table provides information on sound library sources and sampling localities of recordings as well as raw data on all 11 bioacoustic parameters measured (see Supplementary Materials section SM3 for more details on parameters). Recordings whose source is labeled as "private recording" were obtained by colleagues and are available upon demand from the corresponding author.

supplement to Rheindt, Frank E., Prawiradilaga, Dewi M., Ashari, Hidayat, Suparno, Gwee, Chyi Yin, Lee, Geraldine W. X., Wu, Meng Yue, Ng, Nathaniel S. R. (2020): A lost world in Wallacea: Description of a montane archipelagic avifauna. Science 367: 167-170, DOI: 10.1126/science.aax2146

opencc-by-4.0Apr 2020View details →

Covid-19 - impact on evolution of energy demand

Consumo_elect_COVID_2020 & Consumo elect_COVID_2019 Dataset with register of energy demand in Spain. Timeframe: January to March (2019 & 2020)   Casos_COVID_ESPAÑA Dataset with register of the evolution of the spread of Covid-19 in Spain. Break-down of data per region (CCAA). Timeframe: January to March 2020   Casos_COVID_mundo Dataset with register of the evolution of the spread of Covid-19 in Spain. Break-down of data per country. Timeframe: January to March 2020

opencc-by-4.0Jan 2021View details →

Adaptation and energy demand systematic mapping of the literature dataset

Those files form a database of all the informations extracted during the associated systematic review : <a href="https://doi.org/10.1088/1748-9326/abc044">When adaptation increases energy demand: a systematic map of the literature</a> ReadMe.pdf provides a detailled notice of the dataset. This dataset contains 1 SQLite file and for convenience 9 CSV files which are the tables contained in the SQLite file. CSV files are given both in Windows and Linux format in separate folders.

opencc-by-4.0Jul 2022View details →

Supply of and demand for forest ecosystem services (including various GIS forest descriptors)

This data-set shows distribution of supply of and demand for forest ecosystem services (total of 11) across Europe. Various descriptors of forests are included, including location embeddings. All data is a raster on 1 x 1 km spatial resolution. Detailed description is presented in the Metadata & Data description file. Associated code for is available at https://github.com/marcuslovricus/FES_supply_demand

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Data from: Peregrine Falcons shift mean and variance in provisioning in response to increasing brood demand

The hierarchical model of provisioning posits that parents employ a strategic, sequential use of three provisioning tactics as offspring demand increases (e.g., due to increasing brood size and age). Namely, increasing delivery rate (reducing intervals between provisioning visits), expanding provisioned diet breadth, and adopting variance-sensitive provisioning. We evaluated this model in an Arctic breeding population of Peregrine falcons (Falco peregrinus tundrius) by analyzing changes in inter-visit-intervals (IVIs) and residual variance in IVIs across 7 study years. Data was collected using motion-sensitive nest camera images and analyzed using Bayesian mixed effect models. We found strong support for a decrease in IVIs (i.e., increase in delivery rates) between provisioning visits and an increase in residual variance in IVIs with increasing nestling age, consistent with the notion that peregrines shift to variance-prone provisioning strategies with increasing nestling demand. However, support for predictions made based on the hierarchical model of tactics for coping with increased brood demand was equivocal as we did not find evidence in support of expected covariances between random effects (i.e., between IVI to an average-sized brood (intercept), change in IVI with brood demand (slope) or variance in IVI). Overall, our study provides important biological insights into how parents cope with increased brood demand.

opencc-zeroDec 2023View details →

opencc-by-4.0Sep 2023View details →

Benchmarking dataset for multiskilled workforce planning with uncertain demand

These datasets are related to the Data Article entitled: “A benchmark dataset for the retail multiskilled personnel planning under uncertain demand”, submitted to the Data Science Journal. This data article describes datasets from a home improvement retailer located in Santiago, Chile. The datasets were developed to solve a multiskilled personnel assignment problem (MPAP) under uncertain demand. Notably, these datasets were used in the published article "Multiskilled personnel assignment problem under uncertain demand: A benchmarking analysis" authored by Henao et al. (2022). Moreover, the datasets were also used in the published articles authored by Henao et al. (2016) and Henao et al. (2019) to solve MPAPs. The datasets include real and simulated data. Regarding the real dataset, it includes information about the store size, number of employees, employment-contract characteristics, mean value of weekly hours demand in each department, and cost parameters. Regarding the simulated datasets, they include information about the random parameter of weekly hours demand in each store department. The simulated data are presented in 18 text files classified by: (i) Sample type (in-sample or out-of-sample). (ii) Truncation-type method (zero-truncated or percentile-truncated). (iii) Coefficient of variation (5, 10, 20, 30, 40, 50%).

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-4.0Apr 2024View details →

AMIRIS demand response workflow input

This upload contains the input data necessary to run a workflow applying the agent-based power market model <a href="https://gitlab.com/dlr-ve/esy/amiris/amiris">AMIRIS</a> in order to study the impact of power tariffs design on demand response profitability. <h2>Usage</h2> The data has to be copied into the "./inputs/data/" folder of the <a href="https://github.com/jokochems/demand_response_analyses_workflow">demand response analyses workflow</a> and unpacked there. See the description of the workflow on the dependencies how to execute the model. Important note: You will need a version of AMIRIS that is not yet open source and contains the demand response implementation. Feel free to contact the author of this data set in order to request it. Also, you will need a solver, such as Gurobi or CPLEX for instance. <h2>Background</h2> Data has been obtained from previous <a href="https://github.com/pommes-public/pommesinvest">pommesinvest </a>model runs. It has been put together by executing a data <a href="https://github.com/pommes-public/pommesevaluation/blob/main/amiris_converter.ipynb">converter script</a> from the <a href="https://github.com/pommes-public/pommesevaluation/">pommesevaluation </a>repository that compiles the inputs into a format understood by AMIRIS, thus allowing for a soft model coupling (sequential execution) with full input harmonization.