Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
16
datasets available to search
ShareScore release 0.7.1
Dataset results
16 results for “Energy optimisation”
Optimised household consumption profiles through a smart building energy mangement system TABEDE
<p>In the context of the TABEDE project (<a href="https://www.tabede.eu/">https://www.tabede.eu/</a>) several synthetic profiles simulating the consumption and generation of residential buildings, whose appliances were controlled by our proposed Energy Management System (i.e., the TABEDE solution), were simulated. Their construction process was characterised by the following:</p> <ul> <li>Consumption profiles were generated via a bottom-up approach capable of emulating the consumption of individual household appliances. These last ones correspond to the most used appliances in the UK, which were randomly distributed among the buildings based on their average utilisation rate and ownership observed in residential buildings in the country.</li> <li>The physics in terms of heat exchange between neighbouring buildings and the environment were considered, together with the size of the buildings and their physical characteristics. A total of 66 houses and apartments, according to 8 type or building archetypes were created.</li> <li>PV generation profiles were generated according to the meteorological condition of the simulated day.</li> </ul> <p>Together with this, the profiles feature how the TABEDE solution optimised the flexible part of the consumption (i.e., appliances that were controllable by the solution and whose consumption could be shifted in time without sacrificing user comfort) to minimize the electricity bill of the buildings.</p> <p>The information contained in the actual database features the following variables:</p> <ul> <li>TABEDE penetration: percentage of buildings owning the TABEDE solution. Buildings with TABEDE will observe their flexible consumption being optimised.</li> <li>PV penetration: percentage of buildings with a PV system installed on them.</li> <li>Simulation day: one day in summer (19/06/2019) featuring the highest solar radiation of the year, and a day in winter (19/12/2019) with the lowest.</li> <li>Batteries: whether the PV systems is installed alongside household batteries.</li> </ul> <p>Details on the formulation can be found in: <a href="https://urldefense.com/v3/__https:/www.energy-proceedings.org/an-intelligent-infrastructure-for-enabling-demand-response-ready-buildings/__;!!La4veWw!khYBEaeJY85mX5yQUrp0PwoXcg5U10dEdgZ296hONYGyBS5xg91Z8MoDUQy34a4f9Lo$">https://www.energy-proceedings.org/an-intelligent-infrastructure-for-enabling-demand-response-ready-buildings/</a></p>
Global Environmental and Weather data for PyPSA-Earth: An Open Optimisation Model of the Earth Energy System.
<p><strong>PyPSA-Earth </strong>is an open model dataset of the global power system at different network levels that cover our Earth. The African model can be built using the code provided at <a href="https://github.com/pypsa-meets-africa/pypsa-africa">https://github.com/pypsa-meets-africa/pypsa-africa</a>. Other regions follow soon under the same code base.</p> <p>Since the GitHub codebase is not suited for handling large changing files, we provide here separate <strong>data bundles and cutouts</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-meets-africa.readthedocs.io/en/latest/index.html">documentation</a></p> <p>The below-provided <strong>cutouts </strong>are spatiotemporal subsets of the Earth weather data from the <a href="https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation">ECMWF ERA5</a> reanalysis dataset and the <a href="https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002">CMSAF SARAH-2</a> solar surface radiation dataset for the <strong>year 2013</strong>. They have been prepared by and are for use with the <a href="https://github.com/PyPSA/atlite">atlite</a> tool (<a href="https://atlite.readthedocs.io/">https://atlite.readthedocs.io/</a>). They can be reproduced or extended for other weather years (approx. 40-50 years) around the world by using the <a href="https://github.com/pypsa-meets-africa/pypsa-africa/blob/main/scripts/build_cutout.py">build.cutout.py</a></p> <p><strong>ECMWF ERA5</strong></p> <ul> <li><strong>Source: </strong><a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a></li> <li><strong>Terms of Use: </strong><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf</a></li> </ul>
Battery and water heater energy elasticity performance optimisation
<p>This dataset provides actual data demonstrating the INVADE European Union initiative (https://h2020invade.eu/) from the Bulgarian pilot situated in Albena resort, Bulgaria (https://albena.bg/). It represents results from two different approaches to energy elasticity - using a 200kWh industrial sized battery with a combination of a PV, as well as using water heaters with a combination of thermal solar collectors. For both approaches, the system takes into account the energy prices as listed in the Independent Bulgarian Energy Exchange (http://www.ibex.bg/en), as well as weather forecast for the expected energy production from the solar panels.</p> <p><strong>Battery.xlsx</strong> (16 days worth of data for the battery as follows):</p> <ul> <li>Timestamp: the time stamp of the entered data point</li> <li>IBEX SpotPrice (EUR/MWh): the energy price for the current data point</li> <li>Consumption (kWh): the current energy consumption from the grid as taken from the energy meter into the facility</li> <li>ChargingPowerRegulation (kW): control signal received from the system to charge the battery</li> <li>DischargingPowerRegulation (kW): control signal received from the system to discharge the battery</li> <li>EnergyLevel (kWh): the energy level of the battery</li> <li>PV Production (kWh): the produced energy by the PV installation</li> <li>ActualSolarIrradiation (W/m^2): the current solar irradiance</li> <li>ActualTemperature (℃): the current temperature</li> </ul> <p><strong>WaterHeater.xlsx</strong> (1 month worth of data for the water heater as follows):</p> <ul> <li>Timestamp: the time stamp of the entered data point</li> <li>IBEX SpotPrice (EUR/MWh): the energy price for the current data point</li> <li>Consumption (kWh): the current energy consumption from the grid as taken from the energy meter into the facility</li> <li>EnergyLevelHeat (kWh): the current thermal energy level in the water boilers</li> <li>EnergyHeatCapacity (kWh): the current thermal energy capacity of the water boilers</li> <li>HeatProduction (kWh): the current thermal energy production by the solar thermal collectors</li> <li>ActualSolarIrradiation (W/m^2): the current solar irradiance</li> <li>ActualTemperature (℃): the current temperature</li> </ul> <p>Please, make all Creative Commons license attributions for usage of this dataset to "Albena AD (https://albena.bg/)"</p>
Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling - Supplementary Material
<p>Supplementary material for the manuscript "Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling".</p> <p>This deposit contains all data and visualization scripts needed to replicate results in the manuscript.This includes user created figures, model input files, model output files, configuration files for running the workflow, and all scripts needed to process results.</p> <p>In addition to the European Commission, we acknowledge that Trevor Barnes' contribution to this paper was funded via a Mitacs Globalink Research Award, grant number IT2569</p>
Global demand data for PyPSA-Earth: An Open Optimisation Model of the Earth Energy System.
<p><strong>PyPSA-Earth </strong>is an open model dataset of the global power system at different network levels that cover our Earth. The African model can be built using the code provided at <a href="https://github.com/pypsa-meets-africa/pypsa-africa">https://github.com/pypsa-meets-africa/pypsa-africa</a>. Other regions follow soon under the same code base.</p> <p>Since the GitHub codebase is not suited for handling large changing files, we provide here separate <strong>data bundles and cutouts</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-meets-africa.readthedocs.io/en/latest/index.html">documentation</a></p> <p>The below-provided <strong>resource file </strong>contains demand time-series generated by <a href="https://github.com/niclasmattsson/GlobalEnergyGIS/blob/b23206f8701acafdf7359f9cc952dfd4e7b819e5/src/downloaddatasets.jl">GEGIS</a> covering the world. The time series are produced for different socio-economic scenarios (SSP), weather years, and prediction years<strong>.</strong></p>
Supplementary Data: Full Results: Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system
<p>Supplementary Data</p> <p><a href="https://arxiv.org/abs/1801.05290"><strong>Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system</strong></a></p> <p>Authors: T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner</p> <p><a href="https://arxiv.org/abs/1801.05290">arXiv:1801.05290</a></p> <p>The files in this record contain the full output data from each of the scenarios considered in the above publication. They also include the post-processed input data, which might be useful if you want to rerun the scenarios with only small changes to the input data.</p> <p>The scripts to build the model, input data and result summaries can be found in a <a href="https://zenodo.org/record/1146665">companion Zenodo repository</a>. (The supplementary data was split because of the size of the full results.)</p> <p>For each scenario, there is a <a href="https://github.com/PyPSA/PyPSA">PyPSA</a> network file in <a href="https://en.wikipedia.org/wiki/Hierarchical_Data_Format">HDF5 format</a> and a CSV of shadow prices.</p> <p>To read in a network file do:</p> <pre><code class="language-python">import pypsa network = pypsa.Network("network_file_name.h5")</code></pre> <p>All data is released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0).</p>
Machine Learning-based Energy Optimisation in Smart City Internet of Things
<p>Dataset for the paper Machine Learning-based Energy Optimisation in Smart City Internet of Things accepted for publication at The First International Workshop on the Integration between Distributed Machine Learning and the Internet of Things, ACM MobiHoc 2023.</p> <p>The dataset is collected from a real-world deployment of environmental sensors in the city of Bern, Switzerland. Our proposed approach can be applied to determine the tradeoff between the accuracy of temperature measurements and reducing the energy consumption for a single sensor; hence, without loss of generality, the evaluation is conducted on a dataset from a single sensor. Overall, we acquired 3697 measurements, each long 138 seconds. To correct the measurements, we set the maximum ventilation duration of 138 seconds, during which the multivariate time series of humidity and temperature sensor values are recorded together with their corresponding timestamps. The sensor values are recorded at a fixed frequency.</p> <p>From this raw data, we created the training and test sets through data augmentation to simulate time series of different lengths. Namely, for each measurement, we generated 136 samples with the increasing length of measurement time-series, padding the residual time-series length with zeros until reaching a time-series length of 137.</p> <p>We released the source code and trained models on the following GitHub repository https://www.github.com/ricsamikwa/ml-iot-smartcitytemp</p>
Global Socio-Economic and Environmental data for PyPSA-Earth: An Open Optimisation Model of the Earth Energy System.
<p><strong>PyPSA-Earth </strong>is an open model dataset of the global power system at different network levels that cover our Earth. The African model can be built using the code provided at <a href="https://github.com/pypsa-meets-africa/pypsa-africa">https://github.com/pypsa-meets-africa/pypsa-africa</a>. Other regions follow soon under the same code base.</p> <p>Since the GitHub codebase is not suited for handling large changing files, we provide here separate <strong>data bundles and cutouts</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-meets-africa.readthedocs.io/en/latest/index.html">documentation</a></p> <p>The below-provided<strong> data files </strong>contain various open data for improving energy system modelling decisions. A thorough description with license restrictions will follow soon.</p>
Benchmark set inputs for absolute binding free energy calculations of fragment optimisations
<p>Supplementary Information: "Evaluating the use of absolute binding free energy in the fragment optimization process"</p> <p>Provided here are the various scripts, input files, and results necessary to reproduce the outcomes of the above mentioned publication. Please see the provided README.md files for further information on the contents of this dataset.</p>
Business load profiles used in "Maximising the benefits of renewable energy infrastructure in displacement settings: Optimising the operation of a solar-hybrid mini-grid for institutional and business users in Mahama Refugee Camp, Rwanda"
<p>Version used in the submission of "Maximising the benefits of renewable energy infrastructure in displacement settings: Optimising the operation of a solar-hybrid mini-grid for institutional and business users in Mahama Refugee Camp, Rwanda" by Hamish Beath, Javier Baranda Alonso, Richard Mori, Ajay Gambhir, Jenny Nelson and Philip Sandwell.</p>
Supplemental data for the report "Optimisation of lattice simulations energy efficiency"
<p>Supplemental data for the report <a href="http://doi.org/10.5281/zenodo.7057319">"Optimisation of lattice simulations energy efficiency"</a>. Also available as a <a href="https://git.dev.dirac.ed.ac.uk/portelli/tursa-energy-efficiency">git repository</a>.</p> <p>It contains:</p> <ul> <li>Full copy of benchmark run directories</li> <li>Power monitoring scripts</li> <li>Power monitoring raw measurements</li> <li>Power monitoring data analysis and results used in the report</li> </ul> <p>For a more complete description, please see the README.md file.</p>
Supplementary Data: Code, Input Data and Result Summaries: Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system
<p>Supplementary Data</p> <p><a href="https://arxiv.org/abs/1801.05290"><strong>Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system</strong></a></p> <p>Authors: T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner</p> <p><a href="https://arxiv.org/abs/1801.05290">arXiv:1801.05290</a></p> <p>The files in this record contain the scripts to build the model, input data and result summaries for the model PyPSA-Eur-Sec-30 described in the above publication.</p> <p>The full results files (which include the post-processed input data) can be found in a <a href="https://zenodo.org/record/1146649">companion Zenodo repository</a>. (The supplementary data was split because of the size of the full results.)</p> <p><strong>WARNING:</strong> A newer, improved version of this model, <a href="https://github.com/PyPSA/pypsa-eur-sec">PyPSA-Eur-Sec</a>, is under construction on GitHub.</p> <p><strong>Scripts</strong></p> <p>To use the scripts, you need the following free software Python libraries:</p> <ul> <li><a href="https://github.com/PyPSA/PyPSA">PyPSA</a> for the modelling framework</li> <li><a href="https://github.com/FRESNA/vresutils">vresutils</a> for various helper functions to build the model instance</li> <li><a href="https://github.com/FRESNA/atlite">atlite</a> to process weather data into power system data</li> <li><a href="https://snakemake.readthedocs.io/en/latest/">snakemake</a> to organise the execution of the software</li> </ul> <p>and other standard libraries from the <a href="https://pypi.python.org/pypi">Python Package Index</a> (PyPI), such as pandas, pyomo, countrycode, etc.</p> <p>snakemake requires that all code runs with Python version 3. The code setup is known to work with the following versions: PyPSA 0.12.0, pandas 0.21.1, numpy 0.14.0, scipy 0.19.1, pyomo 5.2. You may need to downgrade your libraries to these versions for the scripts to work. If you insist on using the latest versions, please be aware that you'll need to make at least the following changes:</p> <p>i) To accommodate changes in pandas versions 0.22 and higher, in scripts/prepare_network.py change "costs = costs.loc[idx[:,cost_year,:],"value"].unstack(level=2).groupby("technology").sum()" to "costs = costs.loc[idx[:,cost_year,:],"value"].unstack(level=2).groupby(level="technology").sum(min_count=1)".</p> <p>ii) In later versions of PyPSA the component groups like "pypsa.components.one_port_components" have become network-specific and are stored instead at "network.one_port_components".</p> <p>To solve the optimisation problem the scripts are coded to use the commercial solver <a href="http://www.gurobi.com/">Gurobi</a>. To solve the problems in a reasonable time, you will need <a href="http://www.gurobi.com/">Gurobi</a> or an equivalently fast solver such as <a href="https://www.ibm.com/analytics/data-science/prescriptive-analytics/cplex-optimizer">CPLEX</a>. <a href="http://www.gurobi.com/">Gurobi</a> and <a href="https://www.ibm.com/analytics/data-science/prescriptive-analytics/cplex-optimizer">CPLEX</a> both have cost-free licences for academic users.</p> <p>You will also need a computer with at least 64 GB of RAM, since pyomo and the solver are memory intensive.</p> <p>The Python scripts in this repository (in the directory scripts/) are released under the <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GNU General Public Licence Version 3.0</a> (GPL 3.0).</p> <p>The scripts build_*.py process all raw input data into a form where it can be used in the model.</p> <p>make_options.py prepares the options.yml file for each model run.</p> <p>prepare_network.py populates the PyPSA network for each model run with the input data.</p> <p>solve_network.py solves the optimisation problem with <a href="http://www.gurobi.com/">Gurobi</a> or the solver of your choice (this step takes several hours).</p> <p>make_summary.py aggregates the results into CSV files in the directory results/ (also provided in this repository).</p> <p>The scripts plot_*.py and paper_graphics*.py prepare graphical output.</p> <p>All scripts are managed with the <a href="http://snakemake.readthedocs.io/en/latest/">snakemake</a> workflow management tool.</p> <p>To run the scripts, adjust the parameters in config.yaml and cluster.yaml to your local configuration. Then simply execute</p> <pre><code>snakemake</code></pre> <p>for the rule you want to run.</p> <p>Since the jobs are computationally intensive you may want to run them on a cluster. To run the jobs on a cluster with <a href="https://slurm.schedmd.com/">Slurm</a>, then execute e.g.</p> <pre><code>./snakemake_cluster --jobs 6</code></pre> <p>The cluster is configured in cluster.yaml. You will need to create the directory for the logs, i.e. logs/cluster/, before running the script.</p> <p><strong>Data</strong></p> <p>All input data (in the directory scripts/) and results summaries (in the directory results/) are released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0), except those where explicit sources and licences are mentioned in the data folders.</p> <p>The input data include:</p> <ul> <li>Electricity sector data, which largely follows the <a href="https://doi.org/10.5281/zenodo.804337">Zenodo repository</a> for <strong><a href="https://doi.org/10.1016/j.energy.2017.06.004">The Benefits of Cooperation in a Highly Renewable European Electricity Network</a></strong>, except the current repository uses the <a href="https://data.open-power-system-data.org/time_series/2017-07-09/">Open Power System Data Time Series Data Package</a> for load data and <a href="http://renewables.ninja/">Renewables.ninja</a> for solar time series.</li> <li>Heating time series based on the degree-day approximation, constructed with the library <a href="https://github.com/FRESNA/atlite">atlite</a>.</li> <li>Hourly traffic statistics for a week from the German Federal Highway Research Institute (BASt).</li> <li>Yearly energy per country per sector from the <a href="http://www.indicators.odyssee-mure.eu/energy-efficiency-database.html">Odyssee database</a> and <a href="http://ec.europa.eu/eurostat/web/energy/data/energy-balances">Eurostat</a>.</li> <li>A cost database with literature sources.</li> </ul>
SATO EU project | Self-assessment towards optimisation of building energy
<p>Get a glimpse of SATO project, the challenges, and technologies tested for the self-assessment and real life performance of energy use in buildings.</p>
Data Bundle for PyPSA-Eur-Sec: A Sector-Coupled Open Optimisation Model of the European Energy System
<p>While small data files used in PyPSA-Eur-Sec are included directly in the git repository, larger ones are collected in this data bundle. The data bundle’s size is around 680 MB.</p> <p><strong>Licenses</strong></p> <p>Different licenses apply to the various components of this data bundle (mostly attribution).</p> <p>For details see <a href="https://pypsa-eur-sec.readthedocs.io/en/latest/installation.html#data-requirements">https://pypsa-eur-sec.readthedocs.io/en/latest/installation.html#data-requirements</a></p> <p><strong>Changelog 0.3.1</strong></p> <ul> <li>Fix IRENASTAT encoding</li> </ul> <p><strong>Changelog 0.3.0</strong></p> <ul> <li>Add <a href="https://pxweb.irena.org/pxweb/en/IRENASTAT">IRENASTAT</a> country-level power generation capacities.</li> </ul> <p><strong>Changelog 0.2.0</strong></p> <ul> <li>add hydrogen salt cavern storage potential (h2_salt_caverns_GWh_per_sqkm.geojson)</li> </ul> <p> </p>
Optimising Grid-Connected PV-Battery Systems for Energy Arbitrage and Frequency Containment Reserve
<p>Data and results of paper "Optimising Grid-Connected PV-Battery Systems for Energy Arbitrage and Frequency Containment Reserve"</p>
Optimisation of Falls Prevention After Low-energy Osteoporotic Fractures: Feasibility Study
ClinicalTrials.gov study NCT03642808. IPD Sharing: Not stated. Countries: 1. Publications: 11.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.