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21 results for “microgrid”
Rye microgrid load and generation data, and meteorological forecasts.
<p>This dataset contains timeseries for Rye Microgrid, Trondheim, Norway. The timeseries include solar and wind power generation, consumption and historical weather forecasts.</p> <p>From <a href="https://github.com/TronderEnergi/tronderenergi-ai-hackathon-2021">https://github.com/TronderEnergi/tronderenergi-ai-hackathon-2021</a>:</p> <p><em>"The Rye microgrid is a pilot within the EU research project REMOTE. It is a small microgrid placed at Langørgen, in the outskirts of Trondheim, and is a small energy system designed to supply electricity to a modern farm and three households. The REMOTE projects goal for Rye Microgrid is to run the system in islanded mode.</em></p> <p><em>The system has two sources of generation – a wind turbine and a rack of PV panels. In addition, the system has two storages – a battery with high charge and discharge response, but with limited storage and losses, and a hydrogen energy system, with lower charge and discharge rates, higher losses and storage capacity. When you want to charge the hydrogen system, electricity is used to run an electrolyser that makes hydrogen from water and stores the resulting hydrogen in a tank. The process can be reversed by producing electricity from hydrogen using a fuel cell. (...)</em></p> <p><em>Morover, when local production or discharges from storages are not sufficient to cover the demand, the microgrid can draw electricity from the grid at some costs."</em></p> <p> </p> <p>For further details, see: <a href="https://www.remote-euproject.eu/remote18/rem18-cont/uploads/2019/03/REMOTE-D2.2.pdf">https://www.remote-euproject.eu/remote18/rem18-cont/uploads/2019/03/REMOTE-D2.2.pdf</a> and <a href="https://github.com/TronderEnergi/tronderenergi-ai-hackathon-2021">https://github.com/TronderEnergi/tronderenergi-ai-hackathon-2021</a></p> <p>rye_generation_and_load.csv is a comma-separated csv-file with the following columns (all values in <em>kW </em>and time as UTC):</p> <ul> <li>Consumption: Consumption of loads in system (residential and agriculture).</li> <li>Solar: Total production from all solar PV racks.</li> <li>Wind: Power production from wind turbine.</li> </ul> <p>met_data.h5: Contains historical weather forecasts data from The Norwegian Meteorological Institute (met.no) updated every 6 hours for the given location. The file is in hdf5 format. The forecasts include the following parameters: air_pressure_at_sea_level [Pa], air_temperature_2m [K], cloud_area_fraction [pu], integral_of_surface_downwelling_shortwave_flux_in_air_wrt_time [J/m<sup>2</sup>s], wind_direction_10m [deg], wind_speed_10m [m/s]</p> <p>The structure of the file is as follows:</p> <ul> <li>lat63_41_lon10_11 (coordinates) <ul> <li>[forecasted parameter] <ul> <li>forecast <ul> <li>2020-01-01T00Z (time forecast was issued) <ul> <li>axis0 (columns, index where each represent a point in a geographical grid. For example if axis=0,1,2,3, the tables contains the forecasts for the four closes points to the microgrid.)</li> <li>axis1 (rows, timestamps)</li> <li>block0_items (equal to axis0)</li> <li>block0_values (matrix, forecast values)</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul>
Microgrid Simulation Datasets
<p>Simulation results (raw data) for a wide-range time-domain simulation environment for stand-alone microgrids. The contents are as follows:</p> <p>1. py_psim_hw_merged_50ns.parquet: Simulation data comparing results obtained with Python, PSIM, and FPGA accelerated simulation program.</p> <p>2. hw_realtime.parquet: FPGA real-time simulation data.</p> <p> </p>
Dataset for KIOS CoE Sandboxing use-case SUC5 corresponding to cyber attacks affecting the control of active distribution grids and microgrids
<p>These datasets <span> illustrate two primary scenarios (S1-S2) concerning the operation of the sandboxing use case SUC5 corresponding to cyber attacks affecting the control of active distribution grids and microgrids. These scenarios examine the functioning of an active distribution grid and microgrid system, along with the effects of certain cyber-attacks in this context. The demonstration of each scenario is detailed in selected time-series plots which were described in detail in Section </span><span>1.3 of the supporting document of SUC5 (</span><span>accompanied by an in-depth analysis of the processes and an impact assessment). A</span><span>ll data captured during the execution of each scenario was collected, including electrical measurements, reference and set-point signals. </span></p> <ul> <li><span><span><strong>SUC5/S1 datasets/<span>MITM with FDI</span> cyber-attack</strong><span><strong> in an active distribution grid (grid-connected)</strong>: This dataset is related to the operation of the fifth sandboxing use case (SUC5) of the KIOS CoE Sandboxing environment for cyber-physical analysis of EPES, which examines the operation of an active distribution grid, when the distribution grid is interconnected with the main grid. Specifically, this dataset corresponds to the first scenario (S1) of SUC5, where a MITM with FDI cyber-attack is virtually conducted within the sandboxing environment to introduce an offset deviation to the active power set-point allocated to BSS inverter controller from the secondary controller. More details about the scenario related to this dataset can be found in Section 1.3 of the supporting document. The dataset includes electrical measurements of the active power generated by the BSS inverter (connected at bus 2), and the active power set-point before and after the attack. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded from the real time simulator using the “OpWrite” block of the RT-LAB, with 1-millisecond time resolution. <br></span></span></span></li> <li><span><span><span><strong>SUC5/S2 datasets/MITM with FDI cyber-attack in a microgrid (islanding mode)</strong>: This dataset is related to the operation of the fifth sandboxing use case (SUC5) which investigates the operation of a microgrid during islanding mode. This dataset corresponds to the second scenario (S2) of SUC5, where a MITM with FDI cyber-attack is virtually conducted within the sandboxing environment to introduce an offset deviation to the frequency reference signal, exchanged between the higher-level controller (tertiary controller) and the microgrid local controller (secondary V-f controller). More details about the scenario related to this dataset can be found in Section 1.3 of this supporting document. The dataset includes electrical measurements of the microgrid frequency, the reference frequency value generated by the tertiary controller, as well as the attacked frequency reference value. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded from the real time simulator using the “OpWrite” block of the RT-LAB, with 1-millisecond time resolution. <br></span></span></span></li> </ul>
Brazil STAR Project Microgrid Monitoring Data
<p>Contains monitoring data from microgrids deployed in rural areas of the State of Amazonas, Brazil. The data collections started in January of 2018 and ended in March 2019.</p> <p>Contained in the folder CSVs.zip are data from four housing units (denoted as STAR-A, STAR-B, STAR-C and STAR-D) consisting of:<br> 1) Battery information,<br> 2) Solar photovoltaic (PV) generation information, and<br> 3) Measurements from individual appliance monitors (IAMs).</p> <p>Folders are named in the format YYYY-MM-DD-STAR-X_SYS, where<br> 1) YYYY is the year,<br> 2) MM is the month,<br> 3) DD is the day,<br> 4) STAR-X is indicator for the housing unit, i.e., STAR-A, STAR-B, STAR-C, or STAR-D.<br> 5) SYS denotes the "system" and refers to one of the following: Battery, PV, or IAM.</p> <p>The files YYYY-MM-DD-STAR-X_Battery contain the following information in their columns in the order presented:<br> 1) Timestamp<br> 2) Battery current (mA)<br> 3) Ampere-hours (mAh)<br> 4) State of Charge (SOC) (%)<br> 5) Number of charge cycles<br> 6) Number of full discharges<br> 7) Battery voltage (mV)<br> 8) Battery power (W)<br> 9) Amount of discharge energy (0.01 kWh)<br> 10) Amount of charge energy (0.01 kWh)</p> <p>The files YYYY-MM-DD-STAR-X_PV contain the following information in their columns in the order presented:<br> 1) Timestamp<br> 2) Panel voltage (mV)<br> 3) Panel power (W)<br> 4) Energy generated (0.01 kWh)<br> 5) Battery state of operation: 0(Off); 1(Low power); 2(Fault); 3(Bulk); 4(Absorption); 5(Float); 6(Inverting)</p> <p>The files YYYY-MM-DD-STAR-X_IAM contain the following information in their columns in the order presented:<br> 1) Timestamp<br> 2) Real power (W)<br> 3) Reactive power (W)<br> 4) Voltage (unknown unit)<br> 5) IAM ID</p>
Single-unit and multi-unit peer-to-peer transactions in a microgrid (uGIM dataset)
<p>uGIM is a microgrid intelligent management platform that can represent individual end-users using a multi-agent approach. This dataset has data regarding two weeks (May 13<sup>th</sup> to May 18<sup>th</sup>, 2019, and September 30<sup>th</sup> to October 6<sup>th</sup>, 2019) where auction-based peer-to-peer transactions were performed in a real microgrid.</p> <p>The data was collected by uGIM agents and auctions were executed every hour. The hour-ahead auctions are performed by the sellers in a fully distributed approach. To create this dataset two NanoPi M1 Plus (with 1.2 GHz quad-core CPUs and 1 GB of RAM running the Ubuntu 16.04.6 LTS operating system), and three Raspberry Pi Model B+ (with 1.4 GHz 64-bit quad-core CPUs and GB of RAM running the Raspberry Pi OS operating system) were used.</p> <p>uGIM related publications:<br> - Gomes, L., Vale, Z., & Corchado, J. M. (2020). Microgrid management system based on a multi-agent approach: An office building pilot. Measurement: Journal of the International Measurement Confederation, 154. <a href="http://doi.org/10.1016/j.measurement.2019.107427">https://doi.org/10.1016/j.measurement.2019.107427</a><br> - Gomes, L., Vale, Z. A., & Corchado, J. M. (2020). Multi-Agent Microgrid Management System for Single-Board Computers: A Case Study on Peer-to-Peer Energy Trading. IEEE Access, 8, 64169–64183. <a href="http://doi.org/10.1109/ACCESS.2020.2985254">https://doi.org/10.1109/ACCESS.2020.2985254</a><br> - Gomes, L. (2020). μGIM - Microgrid intelligen management system based on a multi-agent approach and the active participation of end-users [Universidad de Salamanca]. <a href="http://doi.org/10.14201/gredos.144238">https://doi.org/10.14201/gredos.144238</a><br> - Gomes, L., Spínola, J., Vale, Z., & Corchado, J. M. (2019). Agent-based architecture for demand side management using real-time resources’ priorities and a deterministic optimization algorithm. Journal of Cleaner Production, 241, 118154. <a href="http://doi.org/10.1016/j.jclepro.2019.118154">https://doi.org/10.1016/j.jclepro.2019.118154</a></p> <p> </p> <p><em>(if you used this dataset in your publications, please send us your information so we can add your publication to the list above)</em></p> <p><br>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.</p>
Data for a dairy farm microgrid solution
<p><strong>Load data </strong>(Farm_load_kW_data.txt)</p> <p>A pre-determined hourly data series of electricity consumption (one year). The consumption was considered independent from the microgrid operating state – network connected or islanded operation. Load was not controlled in order to obtain longer islanded operation capability, nor to minimize power exchange with the network in normal state. The dairy farm case was with about 180 cows and corresponding electricity consumption of approximately 261 MWh/a. The farm data series was created based on data from similar size farms. Daily consumption profile was based on diurnal consumption data of a large cowhouse in a winter day, and the variation from day to day throughout the year was approximated by creating sliding data series based on monthly electricity consumption. The dataset was then suitably scaled for the specified annual consumption.</p> <p><strong>PV generation data </strong>(PV_pu_data.txt)</p> <p>An hourly PV production data series for one year was created for a specific location (in Finland) based on MERRA-2 time series data on radiation [1] and air temperature [2]. The daily average radiation and temperature were scaled to match monthly values from PVGIS database [3,4]. PV panel generation (in per units) was calculated considering location and temperature, and selected panel tilt given by PVGIS ‘optimal inclination angle’. The PV generation data series was then scaled appropriately for the selected PV capacity in the case study.</p> <p> </p> <p><strong>References</strong></p> <ol> <li>Global Modeling and Assimilation Office (GMAO) (2015), MERRA-2 tavg1_2d_rad_Nx: 2d,1-Hourly, Time-Averaged, Single-Level, Assimilation, Radiation Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC).</li> <li>Global Modeling and Assimilation Office (GMAO) (2015), MERRA-2 tavg1_2d_flx_Nx: 2d,1-Hourly, Time- Averaged, Single-Level, Assimilation, Surface Flux Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC).</li> <li>Huld, T., Müller, R. & Gambardella, A. A new solar radition database for estimating PV performance in Europe and Africa. Sol. Energy 86, 1803–1815 (2012).</li> <li>European Communities (2012), PVGIS interactive application. Available: http://re.jrc.ec.europa.eu/pvgis/apps4/pvest.php#.</li> </ol>
uGIM: week monitorization data of a microgrid with five agents (04/08/2019 - 10/08/2019)
<p>uGIM is a microgrid intelligent management software that can represent individual microgrid’s players using a multi-agent approach. This dataset has data regarding a week (from 04-08-2019 to 10-08-2019) of a microgrid with five players (all offices). All agents have consumption and generation data. One of the agents also has sensor data, such as temperature, movement and humidity.</p> <p>In uGIM, agents are deployed in the player’s facilities using single-board computers. All the data in this dataset is read and stored in five single-board computers. Each agent integrates several resources. In this microgrid deployment, all resources use TCP/IP communication. However, uGIM supports more protocols, such as Modbus/RTU, and Modbus/TCP.</p> <p>uGIM related publications:<br> - Gomes, L., Vale, Z., & Corchado, J. M. (2020). Microgrid management system based on a multi-agent approach: An office building pilot. Measurement: Journal of the International Measurement Confederation, 154. <a href="http://doi.org/10.1016/j.measurement.2019.107427">https://doi.org/10.1016/j.measurement.2019.107427</a><br> - Gomes, L., Vale, Z. A., & Corchado, J. M. (2020). Multi-Agent Microgrid Management System for Single-Board Computers: A Case Study on Peer-to-Peer Energy Trading. IEEE Access, 8, 64169–64183. <a href="http://doi.org/10.1109/ACCESS.2020.2985254">https://doi.org/10.1109/ACCESS.2020.2985254</a><br> - Gomes, L. (2020). μGIM - Microgrid intelligen management system based on a multi-agent approach and the active participation of end-users [Universidad de Salamanca]. <a href="http://doi.org/10.14201/gredos.144238">https://doi.org/10.14201/gredos.144238</a><br> - Gomes, L., Spínola, J., Vale, Z., & Corchado, J. M. (2019). Agent-based architecture for demand side management using real-time resources’ priorities and a deterministic optimization algorithm. Journal of Cleaner Production, 241, 118154. <a href="http://doi.org/10.1016/j.jclepro.2019.118154">https://doi.org/10.1016/j.jclepro.2019.118154</a></p> <p> </p> <p><em>(if you used this dataset in your publications, please send us your information so we can add your publication to the list above)</em></p> <p> </p> <p>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.</p>
Rye microgrid historical weather forecasts and stochastic scenarios
<p>This datasets connects historical weather forecasts from the Norwegian Meteorological Institute (met.no) and historical observations from Rye microgrid (https://doi.org/10.5281/zenodo.4448894).</p> <p>Each csv file represents a historical weather forecast for approximately 60 hours ahead. Each csv-file also contains the corresponding observations in the same time interval. Finally, the files also contain load, wind generation and solar PV generation predicitons.</p> <p>The predictions are generated using gradient boosting. The predictions ending with "_ls" are based on least square. The predictions ending with "_quantile_i" represent a quantile prediction. For example, "wind_quantile_2" means that there is a 20% probability the wind will be less than this value.</p> <p>The gradient boosting predictor has been trained to predict the wind power, solar power and load using the explanatory variables below:</p> <p>Solar PV: Cloud area fraction, initial production, clear sky production and forecast look-ahead time</p> <p>Wind power: wind speed, wind direction, wind power converted from wind speed forecast, initial production and forecast look-ahead time</p> <p>Load: hour of day, month of year</p>
Integration of Renewable Energy Sources into the Water-Energy-Food (WEF) Nexus – Modelling a Demand Side Management Approach and Application to a Microgrid Farm in Morocco Dataset
<p>Here you can find the official data used for the publication "Integration of Renewable Energy Sources into the Water-Energy-Food (WEF) Nexus – Modelling a Demand Side Management Approach and Application to a Microgrid Farm in Morocco"</p> <p>If you want to run the model, please update line 11 in the run.jl file, to select the dataset from the scenario you want to look at. It is also recommended to change the result path, to a directory that corresponts to the current model run in order to find the results files quicker.</p> <p> </p> <p>To create a new plot a file called newPlot.jl can be found, that already take care of most data handling, only lines 136 and 141 need to be changed, in order to read in the result files of the results you want to investigate.</p>
Data-Driven Control of Converters in DC Microgrids for Bus Voltage Regulation
<p>DDC_IECON2018</p> <p>Lisette Cupelli; Marco Cupelli; Antonello Monti</p> <p>Within the context of InterFlex project under GA 731289, a Data-Driven Control (DDC) framework for converter-interfaced Distributed Generators (DGs) in a Microgrid has been developed and compared with a non-linear Control technique i.e. Linearizing State Feedback (LSF) for bus voltage regulation. For further information, please refer to the respective publication: https://ieeexplore.ieee.org/document/8592812</p>
Microgrid Profiles
<p>This dataset has been used for validating the technology solution developed during eDREAM project in the Italian pilot site (GA n. 774478).</p>
Detailed Controller Synthesis and Laboratory Verification of a Matching-Controlled Grid-Forming Inverter for Microgrid Applications
<p><strong>Figure5 Blackstart:</strong><br>BS->blackstart<br>noload/min/inter->Initial load<br>U4 -> Voltage waveforms<br>Udq-> Voltage dq values</p> <p><strong>Figure6 Stat<br></strong>data_sati_voltage3 -> Current and voltage waveforms<br>THD -> THD values<br><br><strong>Figure7 Trans:</strong></p> <p>Trans4to7kwdq-> dq values<br>Trans4to7kwPower->P and Q values<br>Trans4to7kwUI->Waveforms</p> <p><strong>Figure8 DCSens:</strong><br>Test1-14 -> Tests corresponding to DC bus sensitivity<br>p/i min/max -> identifier wether p or i value were increased/decreased</p> <p><strong>Figure9 ACsens:</strong><br>AC0-22 -> Tests corresponding to AC sensitivity</p> <p> </p>
Supporting Information - Optimizing hydrogen microgrids to facilitate diesel exit and meet the energy needs of remote and northern communities
<p>This file accompanies "Optimizing hydrogen microgrids to facilitate diesel exit and meet the energy needs of remote and northern communities" by Ian Maynard and Ahmed Abdulla.</p> <p>This supporting information contains:</p> <ul> <li>Nomenclature</li> <li>Data inputs</li> <li>Cost ratios used in cost calculations</li> <li>References</li> <li>Optimization results of 40 communities discussed in the paper above</li> </ul>
Neues DC Microgrid-Konzept – netzunabhängige Elektrifizierung in Entwicklungsländern
<p><b>Abstract</b></p><p class="dhik-abstract-content">Das Projekt «Cyber-physical DC Microgrids» hat das Ziel, ein neues DC Microgrid-Konzept im Labor als Proof-of-Concept umzusetzen. Das Microgrid besteht aus einzelnen Leistungselektronik-Grundmodulen sowie einer DLT-Schicht zur Verwaltung von Energietransaktionen. Dadurch können Microgrids modular, bottom-up aufgebaut werden.</p><p></p><p><b>Weitere Beiträge aus dem DHIK-Forum 2022 auf Zenodo:</b></p><p class="dhik-session-list"></p><ul><li>Session #1: Viktor Sigrist: Internationalisierung - Partnerschaften für den Ausbau von Forschung und Entwicklung (DOI:<a href="https://zenodo.org/record/7123701">10.5281/zenodo.7123701</a>)</li><li>Session #2: Dieter Leonhard: DHIK- Strategien der internationalen Zusammenarbeit in Forschung und Lehre (DOI:<a href="https://zenodo.org/record/7123456">10.5281/zenodo.7123456</a>)</li><li>Session #3: Stephen Wittkopf: Wissens- und Innovationstransfer - Interdisziplinäre Zusammenarbeit mit Unternehmen und Institutionen (DOI:<a href="https://zenodo.org/record/7025707">10.5281/zenodo.7025707</a>)</li><li>Session #4: Xiao Feng: CDHAW - Chinesisch-Deutsche Hochschule für Angewandte Wissenschaften (DOI:<a href="https://zenodo.org/record/7123458">10.5281/zenodo.7123458</a>)</li><li>Session #5: Antonio Pita und Isabel Kreiner: Academy-Industry-Collaboration - Outreach Strategy (DOI:<a href="https://zenodo.org/record/7123460">10.5281/zenodo.7123460</a>)</li><li>Session #6: Martin Sternberg: Promotionsrecht – aktueller Stand an deutschen Hochschulen für angewandte Wissenschaften (DOI:<a href="https://zenodo.org/record/7123757">10.5281/zenodo.7123757</a>)</li><li>Session #7: Adrian Derungs: Duo mit Innovationskraft - Zusammenspiel von Forschung und Wirtschaft in der Zentralschweiz (DOI:<a href="https://zenodo.org/record/7123767">10.5281/zenodo.7123767</a>)</li><li>Session #8: Theres Paulsen: Transdisziplinäre Forschung - komplexe gesellschaftliche Herausforderungen erfordern diverse Ansätze (DOI:<a href="https://zenodo.org/record/7123769">10.5281/zenodo.7123769</a>)</li><li>Session #9: Jörg Schneider: International research collaboration - New funding opportunities for universities of applied sciences (DOI:<a href="https://zenodo.org/record/7123771">10.5281/zenodo.7123771</a>)</li><li>Session #10: Cornelia Spycher und Matthew Whellens: Horizon Europe - overview of funding opportunities for your research and innovation (DOI:<a href="https://zenodo.org/record/7123773">10.5281/zenodo.7123773</a>)</li><li>Session #11: Janique Siffert: Eureka Eurostars - erfolgreiche Förderung für internationale Innovationsprojekte (DOI:<a href="https://zenodo.org/record/7123777">10.5281/zenodo.7123777</a>)</li><li>Session #12: Ludger Fischer: Energy Lab - ein Netzwerk für innovative Lösungen im Energiebereich (DOI:<a href="https://zenodo.org/record/7123779">10.5281/zenodo.7123779</a>)</li><li>Session #13: Jörg Worlitschek: Thermal energy storage - heating the north, cooling the south (DOI:<a href="https://zenodo.org/record/7123781">10.5281/zenodo.7123781</a>)</li><li><b>Session #14: Jonas Mühlethaler: Neues DC Microgrid-Konzept – netzunabhängige Elektrifizierung in Entwicklungsländern (<a href="#collapseTwo">Video</a>)</b></li><li>Session #15: Tommy Claussen: Dekarbonisierung des Gebäudesektors - digitale Transformation in der Gebäudetechnik und im Gebäudemanagement (DOI:<a href="https://zenodo.org/record/7123785">10.5281/zenodo.7123785</a>)</li><li>Session #16: Christoph Imboden: Flexibility solutions - making the power grid fit for the future (DOI:<a href="https://zenodo.org/record/7123787">10.5281/zenodo.7123787</a>)</li><li>Session #17: Uwe Schulz: Spielerisches Sarnetz - Simulationen für die fossile Unabhängigkeit einer Ortschaft (DOI:<a href="https://zenodo.org/record/7123790">10.5281/zenodo.7123790</a>)</li><li>Session #18: Jana Koehler: Künstliche Intelligenz – Erfolg durch Erwünschtheit, Machbarkeit und Wirtschaftlichkeit (DOI:<a href="https://zenodo.org/record/7123792">10.5281/zenodo.7123792</a>)</li><li>Session #19: Rolf Kamps: KI in der Prävention - Befragungsmethoden und Schulungen trainieren, Krankheitserreger erkennen (DOI:<a href="https://zenodo.org/record/7123794">10.5281/zenodo.7123794</a>)</li><li>Session #20: Gwendolyne Pascua: Artificial Intelligence in Space - CIMON assisting astronauts on the International Space Station (DOI:<a href="https://zenodo.org/record/7123796">10.5281/zenodo.7123796</a>)</li><li>Session #21: Tobias Matter et.al.: Augmented Reality Soundscapes - mit maschinellem Lernen Klangkulissen von zukünftigen Bauvorhaben generieren (DOI:<a href="https://zenodo.org/record/7123798">10.5281/zenodo.7123798</a>)</li><li>Session #22: Angela Nicoara: Internet of Things - transforming businesses, people's lives and driving growth in the coming years (DOI:<a href="https://zenodo.org/record/7123800">10.5281/zenodo.7123800</a>)</li><li>Session #23: Adrian Koller: Feldrobotik - unermüdliche und zunehmend intelligentere Hilfe in der Landwirtschaft (DOI:<a href="https://zenodo.org/record/7123802">10.5281/zenodo.7123802</a>)</li><li>Session #24: Widar von Arx et.al.: Realisierung der Verkehrswende - Einfluss der Preispolitik in der Mobilität (DOI:<a href="https://zenodo.org/record/7124000">10.5281/zenodo.7124000</a>)</li><li>Session #25: Andreas Liebrich: Tourismusdateninfrastruktur - Was die Schweiz von Europa lernen kann (DOI:<a href="https://zenodo.org/record/7123806">10.5281/zenodo.7123806</a>)</li><li>Session #26: Frank Pöhlau und Stefan May: Find life on Mars - Schülerprojekte zur mobilien Robotik (DOI:<a href="https://zenodo.org/record/7123808">10.5281/zenodo.7123808</a>)</li><li>Session #27: Jiayun Shen: Open Innovation - Innovationsmanagement bei der Schweizerischen Post (DOI:<a href="https://zenodo.org/record/7123810">10.5281/zenodo.7123810</a>)</li><li>Session #28: Tobias Specker: Interkulturelles Management – innovative Konzepte zum Ausbau der China-Kompetenzen an Hochschulen (DOI:<a href="https://zenodo.org/record/7123812">10.5281/zenodo.7123812</a>)</li><li>Session #29: Elena Algorri: Swimming robots - exploring the unterwater from the surface (DOI:<a href="https://zenodo.org/record/7123814">10.5281/zenodo.7123814</a>)</li><li>Session #30: Sergio Camacho: Robotics and Digital Systems Engineering at the Tec de Monterrey (DOI:<a href="https://zenodo.org/record/7123816">10.5281/zenodo.7123816</a>)</li><li>Session #31: Thomas Dorn: Industrie 4.0 - Forschungskooperationen mit der CDHAW und der Tongji Universität Shanghai (DOI:<a href="https://zenodo.org/record/7123818">10.5281/zenodo.7123818</a>)</li><li>Session #32: Walter Reichert et.al.: Kollaboration und Unterstützung - Mobile Robotik und Exoskelette in der flexiblen Produktion (DOI:<a href="https://zenodo.org/record/7123820">10.5281/zenodo.7123820</a>)</li><li>Session #33: Louis Palmer: Solar Butterfly - climate pioneer world tour supported by HSLU (DOI:<a href="https://zenodo.org/record/7123822">10.5281/zenodo.7123822</a>)</li></ul><p></p>
Data in experimental stand-alone microgrid
<p>In 'Micro Reseau Mafate' project, the PIMENT Laboratory aims to develop microgrid stations in an isolated area called 'Mafate' at Reunion Island. Datasets from the first experimental microgrid at “Roche plate” are shared with the community.</p> <p>https://www.youtube.com/watch?v=dXmV415MmBs</p>
Power, voltage, frequency and temperature dataset from Mesa Del Sol microgrid
<p>Microgrids are small, self-contained power grids that can operate independently of the main grid. They are becoming increasingly popular as a way to improve the reliability and resilience of the power grid. This paper presents a dataset of power data collected from Mesa Del Sol microgrid located in Albuquerque, New Mexico. The dataset includes measurements of voltage, current, power, and energy for microgrid's components. This dataset contains 18 features and was collected over the past 13 months. The dataset is valuable for machine learning applications that can be used to improve the operation and management of microgrids. For example, the data could be used to train machine learning models to predict power outages or to optimise the microgrid's energy consumption.</p>
Power, voltage, frequency and temperature dataset from Mesa Del Sol microgrid
Open the record for dataset details and reuse information.
Data in experimental stand-alone microgrid
<p>In the 'Micro Reseau Mafate' project, the PIMENT Laboratory aims to develop microgrid stations in an isolated area called 'Mafate' at Reunion Island. Datasets from the first experimental microgrid at “Roche plate” are shared with the community.</p> <p>https://www.youtube.com/watch?v=dXmV415MmBs</p>
uGIM: week monitorization data of a microgrid with five agents (10/04/19-16/04/19)
<p>uGIM is a microgrid intelligent management software that can represent individual microgrid’s players using a multi-agent approach. This dataset has data regarding a week (from 10-04-2019 to 16-04-2019) of a microgrid with five players (all offices). All agents have consumption and generation data. One of the agents also has sensor data, such as temperature, movement and humidity.</p> <p>In uGIM, agents are deployed in the player’s facilities using single-board computers. All the data in this dataset is read and stored in five single-board computers. Each agent integrates several resources. In this microgrid deployment, all resources use TCP/IP communication. However, uGIM supports more protocols, such as Modbus/RTU and Modbus/TCP.</p> <p>uGIM related publications:<br> - Gomes, L., Vale, Z., & Corchado, J. M. (2020). Microgrid management system based on a multi-agent approach: An office building pilot. Measurement: Journal of the International Measurement Confederation, 154. <a href="http://doi.org/10.1016/j.measurement.2019.107427">https://doi.org/10.1016/j.measurement.2019.107427</a><br> - Gomes, L., Vale, Z. A., & Corchado, J. M. (2020). Multi-Agent Microgrid Management System for Single-Board Computers: A Case Study on Peer-to-Peer Energy Trading. IEEE Access, 8, 64169–64183. <a href="http://doi.org/10.1109/ACCESS.2020.2985254">https://doi.org/10.1109/ACCESS.2020.2985254</a><br> - Gomes, L. (2020). μGIM - Microgrid intelligen management system based on a multi-agent approach and the active participation of end-users [Universidad de Salamanca]. <a href="http://doi.org/10.14201/gredos.144238">https://doi.org/10.14201/gredos.144238</a><br> - Gomes, L., Spínola, J., Vale, Z., & Corchado, J. M. (2019). Agent-based architecture for demand side management using real-time resources’ priorities and a deterministic optimization algorithm. Journal of Cleaner Production, 241, 118154. <a href="http://doi.org/10.1016/j.jclepro.2019.118154">https://doi.org/10.1016/j.jclepro.2019.118154</a></p> <p> </p> <p><em>(if you used this dataset in your publications, please send us your information so we can add your publication to the list above)</em></p> <p> </p> <p>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.</p>
Microgrid II - Electrocorticography Signals for Human Hand Prosthetics
ClinicalTrials.gov study NCT03289572. IPD Sharing: NO. Countries: 1. Publications: 0.
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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.