Find research datasets worth reusing

opencc-by-4.0Apr 2022View details →

CROSSBOW HLU2-UC4-TC5 Day ahead energy Price for the demonstration period in Romania

For the evaluation of the curtailment distribution algorithm, an experiment was made with the forecast generation or RES assets in the area of Tariverde and the simulation of 30 limitations applied on random days. This dataset contains the DA energy prices in Croatia at the time the demonstration was held

opencc-by-4.0Apr 2022View details →

Argument Aspect Corpus - Nuclear Energy

The Argument Aspect Corpus–Nuclear Energy (AAC-NE) contains English-language sentences with aspect annotations describing the content of arguments on the topic of nuclear energy. It was introduced in this paper: <blockquote> Jurkschat, L., Wiedemann, G., Heinrich, M., Ruckdeschel, M., & Torge, S. (2022). Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate. In Proceedings of the 13th International Conference on Language Resources and Evaluation (LREC 2022). European Language Resources Association (ELRA). </blockquote> The AAC-NE corpus is based on a subset of all argumentative sentences contained in the UKP SAM dataset [1] for which a majority vote of three annotators could be achieved during the annotation of the main argument aspect of each sentence. The CSV files contain one of nine aspect labels per argumentative sentence split into training, dev, and test set. <table> <thead> <tr> <th>aspect</th> <th>train</th> <th>dev</th> <th>test</th> <th>Sum</th> <th>Kripp. Alpha</th> </tr> </thead> <tbody> <tr> <td>alternatives</td> <td>100</td> <td>16</td> <td>21</td> <td>137</td> <td>0.69</td> </tr> <tr> <td>costs</td> <td>98</td> <td>17</td> <td>29</td> <td>144</td> <td>0.72</td> </tr> <tr> <td>environment</td> <td>209</td> <td>27</td> <td>64</td> <td>300</td> <td>0.74</td> </tr> <tr> <td>innovation</td> <td>33</td> <td>2</td> <td>8</td> <td>43</td> <td>0.38</td> </tr> <tr> <td>reactor safety</td> <td>112</td> <td>17</td> <td>43</td> <td>172</td> <td>0.59</td> </tr> <tr> <td>reliability</td> <td>47</td> <td>5</td> <td>10</td> <td>62</td> <td>0.36</td> </tr> <tr> <td>waste</td> <td>87</td> <td>5</td> <td>26</td> <td>118</td> <td>0.80</td> </tr> <tr> <td>weapons</td> <td>52</td> <td>11</td> <td>15</td> <td>78</td> <td>0.77</td> </tr> <tr> <td>other</td> <td>120</td> <td>23</td> <td>29</td> <td>172</td> <td>0.49</td> </tr> <tr> <td>all</td> <td>858</td> <td>123</td> <td>245</td> <td>1226</td> <td>0.62</td> </tr> <tr> <td>pro</td> <td> </td> <td> </td> <td> </td> <td>706</td> <td> </td> </tr> <tr> <td>cons</td> <td> </td> <td> </td> <td> </td> <td>520</td> <td> </td> </tr> </tbody> </table> Additionally, it contains 2000 unlabeled sentences with presumably argumentative content sampled from the newspaper “The Guardian”. [1] Stab, C., Miller, T., Schiller, B., Rai, P., & Gurevych, I. Cross-topic Argument Mining from Heterogeneous Sources. In E. Riloff, D. Chiang, J. Hockenmaier, & J. Tsujii (Eds.), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 3664–3674). Association for Computational Linguistics. <a href="https://doi.org/10.18653/v1/D18-1402">https://doi.org/10.18653/v1/D18-1402 </a>

Diversity of options to eliminate fossil fuels and reach carbon-neutrality across the entire European energy system

Sector-coupled Euro-Calliope model outputs The subdirectories found here cover cost-optimal and cost relaxation (SPORES) carbon-neutrality runs for a sector-coupled, sub-national resolution European energy system model. The underlying model to produce these results, <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-coupled Euro-Calliope</a>, is an extension of the power-sector only <a href="https://github.com/calliope-project/euro-calliope">Euro-Calliope model</a>. It incorporates all energy consuming sectors and includes a more detailed representation of transmission capacities between 98 model regions in Europe. The model runs here are based on specific Sector-Coupled Euro-Calliope minor releases: <ul> <li><a href="https://github.com/calliope-project/euro-calliope-2.0/commit/74f6a9b2e157b6147e155b556f521c03ef23246a">cost-opt</a></li> <li><a href="https://github.com/calliope-project/euro-calliope-2.0/commit/519a4fb26920114e451b8247b38ed86b93b6af89">slack-*</a></li> </ul> The models were optimised using the <a href="https://github.com/calliope-project/calliope">Calliope open energy system modelling framework</a>, again based on different minor releases: <ul> <li><a href="https://github.com/calliope-project/calliope/commit/1faed85eeddbe41c29d52982a6bfb147ef9001a3">cost-opt</a></li> <li><a href="https://github.com/calliope-project/calliope/commit/19460da2e23e752995a9a02ae6dca49379565d43">slack-*</a></li> </ul> <code>slack-*</code> results are for cost relaxation runs, where <code>*</code> refers to the percentage relaxation from the optimal cost of the 2018 energy system. All results use the <a href="https://github.com/sentinel-energy/friendly_data">friendly data</a> format. Data files are structured according to standardised sector-coupled Euro-Calliope output processing provided by the <a href="https://github.com/brynpickering/friendly-calliope">friendly-calliope</a> package + additional processing to produce data relevant to nine high-level metrics (see script <a href="https://github.com/calliope-project/sector-coupled-euro-calliope/blob/main/src/analyse/result_to_friendly.py">here</a>). Both cost optimal and SPORES results related to a projected demand scenario are given in the directories ending in "demand-update". To explore the data, please refer to the <a href="https://sentinel-energy.github.io/friendly_data/">friendly data documentation</a>.

Replication data for: Energy flow analysis of an industrial ammonia refrigeration system

This dataset includes energy data acquired from a pelagic fish processing plant, including data from an industrial ammonia refrigeration system that provides cooling and freezing. In addition, production data is included. Data from the system was analysed within the KSP project PCM-STORE (308847) supported by the Research Council of Norway and industry partners. PCM-STORE aims at building knowledge on novel PCM technologies for low-temperature thermal energy storage. Collecting and analysing data is an important part of evaluating the potential for reduction of CO2 emissions and increasing energy efficiency. Many processing plants measure and log data, but it is not often published. This dataset includes specific energy demand, peak power demand, power demand for different sections of the plant, ambient temperatures, and production volumes. The data was collected in 2021. The included graphics show the refrigeration system and some resulting tables and graphs. Production follows a seasonal cycle throughout the year, with no (or very low) production in the spring (Mar-May), and peak production in the autumn (Sep-Nov). The cycle is linked to the seasonal availability of fish. Annual SEC numbers (200-247 kWh/tonnes) were found to be in line with other Norwegian pelagic plants. A strong dependency between SEC and volume throughput were also found, where months of low production resulted in high SEC values and vice versa. Knowledge about the processes indicates that a fillet production is more energy intensive compared to round production, due to more energy demand from the fillet sections, higher mass (fish and brine) in each box and higher requirement of hot water for cleaning. This dataset is related to the conference paper "Energy flow analysis of an industrial ammonia refrigeration system and potential for a cold thermal energy storage" presented at the 15th IIR Gustav Lorentzen Conference on Natural Refrigerants, Trondheim, Norway 13-15 June 2022.

Dataset of EnergyPlus models to evaluate the impact of modeling the hysteresis phenomenon of phase change materials on the building energy performance

This dataset is the research data generated to evaluate the impact of modeling the hysteresis phenomenon of phase change materials (PCM) on the building performance simulation, which includes: -  A series of EnergyPlus models representing the medium office of the Prototype Building Models developed by DOE. These are the original model without PCM (Baseline), and four models with different PCM modeling approaches (melting-curve, solidification-curve, mean-curve, hysteresis-model). - The typical meteorological year (TMY) for Frankfurt city that was used to obtain the results, which is freely provided by Climate.One.Building.Org repository (https://climate.onebuilding.org/).

Auxiliary Euro-Calliope datasets: QTDIAN storyline-specific spatial data to represent a European energy system model at several spatial resolutions

Custom output generated with the <a href="https://github.com/brynpickering/possibility-for-electricity-autarky/tree/custom-regions">custom-region possibility-for-electricity-autarky</a> workflow. This output provides similar data to <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a> (technically eligible land area for renewables and other spatially disaggregated energy system data), but with three additional land area scenarios. These scenarios are in line with three storylines from the <a href="https://zenodo.org/record/5834010">QTDIAN toolbox</a> and are based on updating the `possibility-for-electricity-autarky` workflow configuration to include the following parameters (also included in `config.yaml`):   <pre><code> scenarios: people-powered: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 0.2 # agro pv share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 0.1 share-rooftop-used: 1.0 government-directed: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0 market-driven: use-of-protected areas: true pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 1.0 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0</code></pre>   This dataset includes different spatial resolutions of land availability. For more information on the `ehighways` resolution, see <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a>. This dataset is used as an input to the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a>.

opencc-by-4.0Mar 2021View details →

Coherent and non-coherent Eddy Kinetic Energy and gridded coherent eddy statistics

This dataset includes the processed data used for the paper titled "Climatology, seasonality and trends of oceanic coherent eddies". The original data was obtained from AVISO+ SSH altimetry, Martínez-Moreno, J. et.al (2019) and Chelton, D. B., & Schlax, M. G. (2013).   Further information and scripts to reproduce the result of the manuscript can be found at: https://github.com/josuemtzmo/CEKE_climatology

opencc-by-4.0Apr 2022View details →

M2 internal tide modal energy terms from a global HYCOM simulation

This data set contains modal energy terms from a forward global HYCOM simulation (22.1) with realistic tide and atmospheric forcing as discussed in <a href="https://doi.org/10.1016/j.ocemod.2020.101656">https://doi.org/10.1016/j.ocemod.2020.101656</a> (On the interplay between horizontal resolution and wave drag and their effect on tidal baroclinic mode waves in realistic global ocean simulations, 2020, MC Buijsman, GR Stephenson, JK Ansong, BK Arbic, JAM Green, ... Ocean Modelling 152, 101656). Please cite this article when using these data.  This is a 4-km simulation with 41 layers. All data is on the native tripole grid. Data is stored as netcdf4 classic. The 2D data sets are 7055 x 9000 (lat x lon). The data set contains <ol> <li>The time-mean and depth-integrated M2 mode 1-5 energy terms: x (eastward) and y (northward) fluxes, KE, APE, conversion, flux divergence, and the intermodal energy conversion (topographic mode coupling) term. The terms are computed for a two-week time series starting on GMT 01-Sep-2016 01:00:00. For details see the paper.</li> <li>Positive seafloor depth, and latitude and longitude coordinates</li> </ol> The M2 mode-1 SSH of the same simulation can be found here: https://doi.org/10.5281/zenodo.5514226 <a href="https://sites.google.com/site/maartenbuijsman/">https://sites.google.com/site/maartenbuijsman/</a>

ASHRAE 1836-RP main list of energy efficiency measures

Energy Efficiency Measures (EEMs) play a central role throughout the building energy efficiency industry, and lists of EEMs therefore exist in a variety of resources. However, each of these use different conventions for describing and organizing measures, which presents a major challenge for aggregating information across these resources.  The ASHRAE 1836-RP main list of energy efficiency measures was assembled as part of ASHRAE Research Project 1836 in order to discover trends in how existing resources describe and organize EEMs.  Analysis of this dataset supported the overall objective of 1836-RP, which was to develop a standardized system for the categorization and characterization of EEMs. The dataset contains the complete list of 3,490 EEMs assembled and analyzed as part of 1836-RP. The EEMs were collected from 16 different source documents during the 1836-RP literature review from September 2019 through July 2020. An initial list of suggested sources was provided by the members of the 1836-RP Project Advisory Board, and additional documents were added through the authors’ literature review. A data dictionary can be found in the README.txt file.  Additional information on working with this dataset can be found in the project repository: <a href="https://github.com/retrofit-lab/ashrae-1836-rp-text-mining">https://github.com/retrofit-lab/ashrae-1836-rp-text-mining</a>

Dataset of 30 energy customers with flexibility data, and distributed generation, considering residential, small commerce, large commerce, and industrial customers

The dataset has 30 customers: ten residential, ten small commerce, five large commerce, and five industrial customers. The combination of several energy customer types allows the creation of a dataset with different types of consumption profiles, generation, and flexibility, and, therefore, different values of participation in demand response events. The residential profiles of the considered customers use the data available in the Working Group on Intelligent Data Mining and Analysis (IDMA): https://site.ieee.org/pes-iss/data-sets/ The values represent a week period using 15 minutes reading periods. All the values are expressed in kWh and the matrixes were created as [customer x time_period].   We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.

Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 2. Developing prediction models

This data set contains the data, JMP scripts, and figures of the article titled "Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 2. Developing prediction models" to be published in the journal Animal - Open Space.

Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 1. Weight changes due to fasting, bleeding, and chilling

This data set contains the data, JMP scripts, and figures of the article titled "Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 1. Weight changes due to fasting, bleeding, and chilling" to be published in the journal Animal - Open Space.

opencc-by-4.0Aug 2022View details →

Single molecule dataset for article: Multistep orthophosphate release tunes actomyosin energy transduction

<table> <tbody> <tr> <td> Dataset (single molecule movies) that is behind the results in the article's Figure 2 and Figure 3. MATLAB scripts used to analyze the dataset.  </td> </tr> </tbody> </table>

opencc-by-4.0Aug 2022View details →

Dataset of multi-objective optimization results for a new latent energy storage approach in buildings based on several phase change materials with different melting temperatures

This dataset comprises the multi-objective optimization results obtained for a new latent energy storage approach based on several phase change materials (PCMs) with different melting temperatures in buildings. The results were obtained for a small office building in eight climate-representative locations according to the ASHRAE 169-2020 climate classification and within the WMO Region VI (Europe). The dataset contains: - The EnergyPlus baseline models employed as a case study for each climate. - The Pareto fronts obtained after the multi-objective optimization in each climate. - The EnergyPlus models for the best designs achieved on the Pareto fronts in terms of annual total load reductions.

Demonstration Cases - Simulation data of energy consumption of residential building typologies

The dataset is about the energy analysis for retrofit strategies of 5 building typologies and the EDEA project located in 3 climates zones in Europe: South (Madrid), Central (Berlin) and North (Helsinki). The dataset includes: (1) Open Document Spreadsheet (.ods) file with the results of Heating Consumption (kWh/m2·year) and Cooling Consumption (kWh/m2·year) for the five buildings, in three locations and for several scenarios: - Locating external new insulation in walls and roof. - Replacing Windows. - Combination strategies: locating new insulation layers and replacing the existing windows. - Installing solar protection devices.

Custom Dataset Collected for Energy Manager

<ul> <li>Change directory to inside the dataset</li> <li>Create virtual python environment and install the contents in requirements.txt</li> <li>Run the commands in sample_commands.txt file to get an idea on how the sample_pvm_to_csv.py script works</li> </ul> Data description: Light sensor (APDS9960) - Solar irradiance; Power sensor (INA226) - solar panel open circuit voltage (OCV), solar panel closed circuit current (CCC), solar panel closed circuit shunt voltage (CCSV); DHT22 - temperature and humidity data are collected using a custom setup (check the custom_setup.pdf file), placed indoors, next to a curtain less glass window (the plane of the sensing surfaces were almost 45 degree to the window glass. The spectral distortions made by the glass on the solar irradiance observed is unknown - sorry! The sensors used are INA226, Solar panel (1.5W, 137x81x2.5 mm, 16% efficiency, V & I at peak power :5.5V & 270ma), APDS9960 and DHT22. The custom setup transmits collected data to the server. The server creates a new log file everyday with '.txt' file extension. When the server restarts and begins logging, the first file created has the time information of when the log file was created, suffixed with number '0' before the file extension. This suffix is incremented day by day, until the next interruption to the server, where it create a new log file with the current time and suffix '0', and the whole cycle continues. The log files provided here are raw and unfiltered.   A header with 'UTC' time was transmitted by the custom setup to the server whenever it restarts. It contains column information. The sampling period is 0.2s and only the time stamp for the first data is found in the header. The timestamp for the rest is calculated using the index / serial number and the sample period. The index and the data from each sensor are separated by '#'. If a sensor provides multiple data, then those data are separated by ','.

Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Energy Cost Optimization with Energy Selling

Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> an energy cost optimization considering the presence of an energy buyer is proposed to validate the scheduler’s ability to maximize profits while also minimizing energy costs. The scenario considers an added sales value corresponding to 50% of the buying. For this scenario, the genetic algorithm was executed for 2 hours, with 1 and 0 for the optimization weights total cost and machine occupancy deviation, respectively.   File Description: <ul> <li>Input_JSON_Energy_Cost_Energy_Selling_Optimization - JSON input data for the energy cost optimization with energy selling</li> <li>Output_JSON_Energy_Cost_Energy_Selling_Optimization - JSON output data for the energy cost optimization with energy selling</li> <li>Output_Statistics_Energy_Cost_Energy_Selling_Optimization - Excel output energy cost optimization with energy selling statistics</li> </ul>

Diffuse reflectance spectra of coated plates and corresponding plots transformed Kubelka-Munk function versus the energy of light (eV)

The link contains UV-DRS  results of  TiO2/Fe2O3 layered composites (from commercial nanoparticles) and corresponding bandgap energies

Dataset: Spatial Data Starter Kit for OnSSET Energy Planning in Kitui County, Kenya

This is a set of openly-available data pre-processed to facilitate county-level energy planning using the Open Source Spatial Electrification Tool (OnSSET) in Kitui County, Kenya. It provides a ready-to-use starter kit of data inputs for county-level OnSSET analysis. The work to identify these data is submitted for publication - publication details will be added here as soon as possible upon release. These data are contained in spatial data files used to create the input for OnSSET in Kitui, and a prepared CSV data input for OnSSET in Kitui (kitui_OnSSET_data). The following spatial data files are included in the dataset: <ul> <li>kitui_admin: A vector (.geojson) file containing the administrative boundaries of Kitui county. </li> <li>kitui_clusters: A vector (.geojson) file locating population clusters generated in data processing for OnSSET.</li> <li>kitui_demand: A raster (.tif) file containing merged health, agriculture, commercial, and residential demands for Kitui county in kWh.</li> <li>kitui_elevation: A raster (.tif) containing elevation information.</li> <li>kitui_GHI: A raster (.tif) file containing global horizontal irradiance data for Kitui county.</li> <li>kitui_hydro: A vector (.geojson) file containing the locations of hydropower stations in Kitui county. Note that there are none, and that this is expected.</li> <li>kitui_night_lights: A raster (.tif) file capturing the light emitted from Kitui county at night.</li> <li>kitui_power_stations: A vector (.geojson) file showing the locations of power stations in Kitui county.</li> <li>kitui_roads: A vector (.geojson) file showing the main roadways in Kitui county.</li> <li>kitui_transformers: A vector (.geojson) file showing transformer locations in Kitui county.</li> <li>kitui_transmission_lines: A vector (.geojson) file locating transmission lines in Kitui county. </li> <li>kitui_travel_hours: A raster (.tif) file showing travel time to the nearest market center in Kitui county.</li> <li>kitui_wind_100: A raster (.tif) file of wind speeds at 100 m in Kitui county.</li> </ul> This dataset has been produced through work undertaken in the Climate Compatible Growth Programme.

Dataset: energy consumption patterns in a science and technology park

This dataset contains time series of energy consumption and external temperature for a group of buildings in a science and technology park, from 2018 to 2022, that is suitable for the development of algorithms to improve energy efficiency and for the early detection of energy consumption peaks based on night-time outdoor temperatures. Time series of power, energy consumption and external temperature for group of tertiary buildings, from 2018-01-01 to 2022-09-15. Peaks in electricity consumption are a major concern for building owners, especially during summer, when external temperatures are high, and users demand air conditioning. Owners may face high costs, observe increased risk of overheating in energy intensive equipment, and may exceed the power threshold set out in the electricity supply contract. Several effective "peak-shaving" strategies can be put in place, such as a higher temperature set-point (which implies a temporary reduction of comfort levels), switching off low-priority processes, and starting the cooling process earlier than usual. This dataset can be used to develop algorithms for early detection of peaks in energy consumption, based on external temperatures measured during the night.