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86 results for “Power Modeling”
Dataset for Accuracy of Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and CNN-LSTM Model
<p>This research paper introduces a deep learning hybrid model employing Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) for short-term photovoltaic (PV) solar energy forecasting.The proposed method integrates the Variational Mode Decomposition (VMD) algo-rithm with the CNN-LSTM model to predict PV power generation from a solar farm in Boussada, Algeria, from January 1, 2019, to December 31, 2020. The performance of the developed model is benchmarked against other deep learning models (VMD-CNN, VMD-LSTM, CNN-LSTM) across various time horizons (15, 30, and 60 minutes) to provide a comprehensive evaluation. Our findings exhibit greater performance of the developed model compared to other architectures, showcasing promising results in solar power forecasting. This research contributes to the main goal of enhancing EMS by providing accurate solar energy forecasts.</p>
Unlocking the power of computer modelling and simulation across the life sciences product lifecycle
<p><strong>Unlocking the Power of Computer Modelling and Simulation Across the Life Sciences Product Lifecycle</strong></p> <p>In an era where technology continuously reshapes the boundaries of research and development, the field of life sciences stands at the cusp of a transformative shift. The potent combination of computer modelling and simulation has begun to unlock unprecedented opportunities across the product lifecycle in life sciences, promising to revolutionize everything from medicinal product development to clinical research. Let's delve into how these technological advancements are paving the way for groundbreaking progress in medicine and healthcare.</p> <p><strong>The Fusion of Technology and Life Sciences</strong></p> <p><em>In Silico Methods: A New Frontier in Medicine</em></p> <p>The term 'in silico' refers to computer simulations used in the study of biological and chemical processes. The video highlights the growing importance of in silico methods in the life sciences sector, particularly in the United Kingdom. These methods allow for the virtual testing of new medicinal products, significantly reducing the need for costly and time-consuming physical trials.</p> <p><em>Bridging the Gap with Computational Modeling</em></p> <p>Computational modeling is another key aspect discussed in the presentation. It involves the use of computer algorithms and mathematical models to simulate real-world medical data. This approach enables researchers to predict how medicinal products will behave in various scenarios, including their interaction with different types of patient data. As a result, computational modeling is instrumental in enhancing the precision of clinical research and improving medical equitability by considering a broader range of patient profiles.</p> <p><strong>The Impact on Clinical Research and Patient Care</strong></p> <p><em>Enhancing Precision and Efficiency</em></p> <p>One of the most notable benefits of integrating computer modelling and simulation into the life sciences is the enhanced precision and efficiency it brings to clinical research. By leveraging real-world medical data, researchers can obtain more accurate predictions about the efficacy and safety of new medicinal products. This not only accelerates the development process but also ensures that treatments are more tailored to individual patient needs.</p> <p><em>Promoting Medical Equitability</em></p> <p>The video underscores the role of these technologies in promoting medical equitability. Through the use of patient data simulations, it becomes possible to account for a wider array of genetic, environmental, and lifestyle factors that influence health outcomes. This inclusive approach ensures that the benefits of medical advancements are accessible to a diverse population, addressing disparities in healthcare access and treatment efficacy.</p> <p><strong>Conclusion: The Future is Now</strong></p> <p>The integration of computer modelling and simulation in the life sciences heralds a new era of medical research and patient care. As we continue to explore the potential of these technologies, it's clear that they hold the key to unlocking more efficient, precise, and equitable healthcare solutions. The journey towards fully realizing this potential is just beginning, but the promise it holds is immense. As we stand on the brink of this technological revolution, one thing is certain: the future of medicine and healthcare is being shaped here and now, and it's brighter than ever.</p>
Southern African Power Pool GridPath Model Output Data - Chowdhury et al 2022 Joule
<p>This data repository holds GridPath model output data for the paper Chowdhury, A.K., Deshmukh, R., Wu, G., Uppal, A., Mileva, A., Curry, T., Armstrong, L., Galelli, S., and Kudakwashe, N. (2022) “Enabling a low-carbon electricity system for Southern Africa”, Joule. See Readme for more details. </p>
Modeled tritium in precipitation from Fukushima Daiichi Nuclear Power Plant accident simulations with MIROC5-iso
<p>This data set contains modeled tritium in precipitation values from different simulations of Fukushima Daiichi Nuclear Power Plant (FDNPP) accident produced with MIROC5-iso. The simulations are for the period 2011-20121 and were with different anthropogenic tritium source functions. A complete description can be found in Cauquoin, A., Gusyev, M., Bong, H., Okazaki, A., and Yoshimura, K.: Modeling tritium release to the atmosphere during the Fukushima Daiichi Nuclear Power Plant accident and application to estimating post-accident water system transit times, <em>Environ. Sci. Pollut. Res.</em>, <a href="https://doi.org/10.1007/s11356-025-35919-1" target="_blank" rel="noopener">https://doi.org/10.1007/s11356-025-35919-1</a>, 2025. </p> <p>The simulations are named fukushima_accident_{jra55, era5}_total_gas_{div100, div200, div500, div1000}, with {jra55, era5} describing a nudging to JRA-55 or ERA5 reanalyses, and with {div100, div200, div500, div1000} describing the anthropogenic tritium input function used in DatasetS1_table_tritium_release_atm_fukushima_input.csv.</p> <p>The modeled values of tritium in Hiso river water, Minamisoma spring and artesian groundwater, calculated using MIROC5-iso tritium in monthly precipitation in Fukushima, scaled Tokyo GNIP data, and tritium measurements in preciptation at Fukushima as input of the TracerLPM model, are included too. </p> <p>The model data can be downloaded as netcdf, csv or xlsx files:</p> <ul> <li>*_daymean.prcpTU.nc: daily mean tritium in precipitation over the period 2011-2021, expressed in TU;</li> <li>*_monmean.prcpTU.nc: monthly mean tritium in precipitation over the period 2011-2021, expressed in TU;</li> <li>*_daymean.prcp.nc: daily precipitation over the period 2011-2021, expressed in mm/day;</li> <li>*_monmean.prcp.nc: monthly precipitation over the period 2011-2021, expressed in mm/month;</li> <li>*_prcp_daymean.remapnn.csv: daily precitation at nearest grid cells of Tsukuba, Kashiwa, Hongo, Yokosuka, Konan, and Misasa over the period 2011-2012, expressed in mm/day;</li> <li>*_prcp_monmean.remapnn.csv: montly mean precitation at nearest grid cells of Chiba, Niigata, and Fukushima over the period 2011-2021, expressed in mm/month;</li> <li>*_prcpTU_daymean.remapnn.csv: tritium in daily precitation at nearest grid cells of Tsukuba, Kashiwa, Hongo, Yokosuka, Konan, and Misasa over the period 2011-2012, expressed in TU;</li> <li>*_prcpTU_monmean.remapnn.csv: tritium in montly precitation at nearest grid cells of Chiba, Niigata, and Fukushima over the period 2011-2021, expressed in TU;</li> <li>DatasetS1_table_tritium_release_atm_fukushima_input.csv: Table of anthropogenic tritium daily release, based on reconstructed iodine-131 total gas emissions from <a href="https://doi.org/10.5194/acp-15-1029-2015" target="_blank" rel="noopener">Katata et al. (2015)</a>, used as inputs for MIROC5-iso.</li> <li>TracerLPM_fukushima_with_peak_jra55.xlsx: Tritium input function Cin(t) and tritium concentration in Hiso river water, Minamisoma spring and artesian groundwater modeled by TracerLPM. Simulation div100 nudged to JRA-55 was used for constructing Cin(t).</li> <li>TracerLPM_fukushima_without_peak_jra55.xlsx: Tritium input function Cin(t) and tritium concentration in Hiso river water, Minamisoma spring and artesian groundwater modeled by TracerLPM. Simulation ctrl nudged to JRA-55 (without FDNPP peak) was used for constructing Cin(t).</li> <li>TracerLPM_fukushima_with_peak_era5.xlsx: Tritium input function Cin(t) and tritium concentration in Hiso river water, Minamisoma spring and artesian groundwater modeled by TracerLPM. Simulation div100 nudged to ERA5 was used for constructing Cin(t).</li> </ul>
Southern African Power Pool GridPath Model Input Data
<p>This data repository holds GridPath model input data for the paper Chowdhury, A.K., Deshmukh, R., Wu, G., Uppal, A., Mileva, A., Curry, T., Armstrong, L., Galelli, S., and Kudakwashe, N. (2022) “Enabling a low-carbon electricity system for Southern Africa”, Joule. See Readme for more details. </p>
Supplementary Data from, "Causal health impacts of power plant emission controls under modeled and uncertain physical process interference."
<p>These data are used to conduct the analysis in, "<a href="https://arxiv.org/abs/2306.05665">Causal health impacts of power plant emission controls under modeled and uncertain physical process interference</a>," by Wikle and Zigler (2024), to appear in <em>Annals of Applied</em> Statistics. This is purely for archival purposes to facilitate access to and replication of the aforementioned analysis. Data were obtained from the following sources:</p> <ol> <li> U.S. Emissions Data [<a href="https://ampd.epa.gov/ampd">U.S. EPA, Air markets program data (AMPD)</a>] <ul> <li>AMPD_Unit_with_Sulfur_Content_and_Regulations_with_Facility_Attributes.csv</li> </ul> </li> <li> US Census 2016 American Community Survey [<a href="https://www.census.gov/programs-surveys/acs">US Census Bureau ACS</a>] <ul> <li>Census_2016_TxZCTA.RDS</li> <li><em>Note: data were obtained using the r package ‘<a href="https://walker-data.com/tidycensus/">tidycensus</a>’.</em></li> </ul> </li> <li> Daymet Annual Climate Summaries [<a href="https://daac.ornl.gov/DAYMET/guides/Daymet_V4_Annual_Climatology.html">Daymet Version 4</a>] <ul> <li>daymet_v4_prcp_annttl_na_2016.nc</li> <li>daymet_v4_tmax_annavg_na_2016.nc</li> <li>daymet_v4_tmin_annavg_na_2016.nc</li> <li>daymet_v4_vp_annavg_na_2016.nc</li> </ul> </li> <li> SO<sub>4</sub> and Black Carbon Concentrations [<a href="https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V4.NA.03">Randall Martin Atmospheric Composition Analysis Group, North American Regional Estimates, version V4.NA.02</a>] <ul> <li>GWRwSPEC_BC_NA_201601_201612.nc</li> <li>GWRwSPEC_SO4_NA_201601_201612.nc</li> </ul> </li> <li> HyADS Coal-Attributed PM2.5 Concentrations [<a href="https://doi.org/10.1097/EDE.0000000000001024">Henneman et al. (2019)</a>] <ul> <li>HyADS_grids_pm25_byunit_2016.fst</li> <li>HyADS_grids_pm25_total_2016.fst</li> </ul> </li> <li> Mexico Emissions Data [<a href="https://www.epa.gov/air-emissions-modeling/2014-2016-version-7-air-emissions-modeling-platforms">National Emissions Inventory Collaborative, 2016v1 emissions modeling platform</a>] <ul> <li>Mexico_2016_point_interpolated_02mar2018_v0.csv</li> </ul> </li> <li> North American Regional Reanalysis Meteorological Data [<a href="https://psl.noaa.gov/data/gridded/data.narr.monolevel.html">NOAA</a>] <ul> <li>rhum.2m.mon.mean.nc</li> <li>uwnd.10m.mon.mean.nc</li> <li>vwnd.10m.mon.mean.nc</li> </ul> </li> <li> Cigarette smoking data [<a href="https://doi.org/10.1186/1478-7954-12-5">Dwyer-Lindgren et al. (2014)</a>] <ul> <li>smokedatwithfips_1996-2012.csv</li> </ul> </li> <li> Synthetic pediatric asthma data [<em>Note:<strong> synthetic data!</strong> Simulated to match the format, but not the observations, from the <a href="https://www.dshs.texas.gov/texas-health-care-information-collection">Texas Health Care Information Collection (THCIC), Texas DSHS</a></em>] <ul> <li>synth-ped-asthma-data.csv</li> </ul> </li> <li> Texas state shape file [<a href="https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html">US Census</a>] <ul> <li>texas-state-sf.RDS</li> </ul> </li> <li> US ZIPcode-to-county data crosswalk [<a href="https://mcdc.missouri.edu/applications/geocorr2014.html">Missouri Census Data Center</a>] <ul> <li>tx-zip-to-county.csv</li> </ul> </li> </ol> <p>Code and supplementary material from this analysis, as well as more detailed data descriptions, are available at: <a href="https://github.com/nbwikle/estimating-interference">https://github.com/nbwikle/estimating-interference</a></p>
A non-intrusive reduced order model for the characterisation of the spatial power distribution in large thermal reactors (dataset)
<p>This repository contains the software and datasets needed to reproduce the results presented in the article "<a href="https://doi.org/10.1016/j.anucene.2022.109674">A non-intrusive reduced order model for the characterisation of the spatial power distribution in large thermal reactors</a>", published in Annals of Nuclear Energy.</p>
Dataset: Random forest models of ultra-low frequency magnetospheric wave power.
<p>Predictive models of ground-based ultra-low frequency (ULF, 1-15 mHz) wave power, corresponding to magnetospheric waves. The series of decision tree ensembles (random forests) are dependent on solar wind properties, latitude and azimuthal angle around the Earth (magnetic local time, MLT).</p>
Database for Market uptake of concentrating solar power in Europe: model-based analysis of drivers and policy trade-offs. MUSTEC project.
<p>This dataset contains the data underlying the modelling activities of the MUSTEC (<em>Market Uptake of Solar Thermal Electricity through Cooperation</em>) project used in the models Green-X (TU Wien) and Enertile (Fraunhofer ISI).</p> <p>For description of the modelled scenarios, results and findings, see: Resch, G., Schöniger, F., Kleinschmitt, C., Franke, K., Sensfuß, F., Thonig, R., and Lilliestam, J.:<em> </em><em> Market uptake of concentrating solar power in Europe: model-based analysis of drivers and policy trade-offs. </em>Deliverable 8.2 MUSTEC project, TU Wien, Wien.</p> <p>For information on the project see: https://www.mustec.eu/</p> <p>For data descriptions, licence, and further information, see README.md.</p> <p> </p>
Data for Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications
<p>These data were generated for the Open-Acces Article :</p> <p>Kamerling, S.; Vuillerme, V.; Rodat, S. Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 3731. https://doi.org/10.3390/en14133731</p> <p>In these dataset, the data for the Case Study and the Sensitivity Analysis are available. Jupyter Notebooks for further process of these data are also available. The NoteBooks AnalyseHourlyValues, AnalyseDailyValues and AnalyseMonthlyValues allow for easy change of variable, whereas CaseStudyAnalysis is for one specific set of data. The AnalyseSets were created in order to analyse the influence of the optimization on the solar fraction of the different datasets.</p>
Illustrative dataset for the article: Vieira, R., McDonald, S., Araujo-Soares, V., Sniehotta, F., Henderson, R. (2017) "Dynamic modelling of n-of-1 data: Powerful and flexible data analytics applied to individualised studies"
<p>This dataset is supplementary material of the manuscript "Dynamic modelling of n-of-1 data: Powerful and flexible data analytics applied to individualised studies. McDonald et al. (2016) presents a series of novel n-of-1 studies that intended to explore the relationship between physical activity change during the retirement transition. The file contains the data of one participant. The column names correspond to the following variables:</p> <p>time: duration of follow-up (minutes);<br> minute: time of day (hours and minutes);<br> day_num: day since beginning of follow-up (the first two days were considered as adaptation phase and therefore removed); <br> PAscore: accelerometer raw score; <br> startBout: 1 (a bout of PA was initiated in this minute) or 0 (a bout of PA wasn't <br> initiated in this minute); <br> nPAbouts_day: number of PA bouts per day; <br> nPAbouts_day.l1: number of PA bouts in previous day (lag 1); <br> nPAbouts_day.l2: number of PA bouts two day before (lag 2); <br> nBoutsLast2hours: number of PA bouts in previous 2 hours; <br> retirement: 0 (before retirement) or 1 (after retirement)<br> weekday: 0 (workday) or 1 (weekend)<br> sleepLength: number of hours of sleep last night<br> sleepLength.l1: number of hours of sleep the night before<br> sleepLength.l2: number of hours of sleep two nights before<br> pers: personalised measure of partner's influence (scale 0-1)<br> periodDay: morning, evening or afternoon</p> <p>McDonald, S., Vieira, R., O'Brien, N., White, M., & Sniehotta, F. F. (2016). Does physical activity and sedentary behavior change during the retirement transition? Findings from a series of novel n-of-1 natural experiments. <em>International Journal of Behavioral Medicine, 23</em>, S261-S261.</p> <p> </p>
Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America
<p>This dataset provides model-ready data to include geospatial differentiation in solar and wind power investment options in energy models (primarily capacity expansion models and dispatch models) at the level of every Central and South American country. </p> <p>The methodology used to create the dataset takes into account resource quality, land use restrictions, distance from infrastructure, and other factors. It was previously applied to create an all-Africa dataset explained in Sterl et al. (2022) and published by Sterl, Hussain & Elabbas (2023). </p> <p>Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022).</p> <p>Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input.</p> <p>Folder (3) provides the corresponding shapefiles.</p> <p>Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 3, 5 or 10 depending on country size.</p> <p>Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening).</p> <p>Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022).</p> <p>Countries/territories included in the dataset: </p> <p>Argentina<br>Belize<br>Bolivia<br>Brazil<br>Chile<br>Colombia<br>Costa Rica<br>Cuba<br>Dominican Republic<br>Ecuador<br>El Salvador<br>French Guiana<br>Guatemala<br>Guyana<br>Haiti<br>Honduras<br>Jamaica<br>Nicaragua<br>Panama<br>Paraguay<br>Peru<br>Suriname<br>Uruguay<br>Venezuela</p> <p> </p> <p><strong>References</strong></p> <p>Sterl, S., Hussain, B., Miketa, A. <em>et al.</em> An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. <em>Sci Data</em> <strong>9</strong>, 664 (2022). <a href="https://doi.org/10.1038/s41597-022-01786-5">https://doi.org/10.1038/s41597-022-01786-5</a></p> <p>Sterl, S., Hussain, B., & Elabbas, M. (2023). Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power » (1.2.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.14870967">https://doi.org/10.5281/zenodo.14870967</a></p>
Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power »
<p>This dataset contains data provided alongside the paper "An all-Africa dataset of energy model “supply regions” for solar PV and wind power" by Sterl et al. (2022).</p> <p>It concerns a novel representative subset of attractive sites for solar PV and onshore wind power for the entire African continent. We refer to these sites as “Model Supply Regions” (MSRs). This MSR dataset was created from an in-depth analysis of various existing datasets on resource potential, grid infrastructure, land use, topography and others (see Methods), and achieves hourly temporal resolution and kilometre-scale spatial resolution. This dataset fills an important research need by closing the gap between comprehensive datasets on African VRE potential (such as the Global Solar Atlas and Global Wind Atlas) on the one hand, and the input needed to run cost-optimisation models on the other. It also allows a detailed analysis of the trade-offs involved in exploiting excellent, but far-from-grid resources as compared to mediocre but more accessible resources, which is a crucial component of power systems planning to be elaborated for many African countries.</p> <p>Five separate datasets are included:</p> <p>Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022).</p> <p>Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input.</p> <p>Folder (3) provides the corresponding shapefiles.</p> <p>Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 2, 5 or 10 depending on country size.</p> <p>Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening).</p> <p>Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022).</p> <p>Countries/territories included in the dataset: </p> <p>Algeria<br>Angola<br>Benin<br>Botswana<br>Burkina Faso<br>Burundi<br>Cameroon<br>Central African Republic<br>Chad<br>Congo Republic<br>Democratic Republic of the Congo<br>Djibouti<br>Egypt<br>Equatorial Guinea<br>Eritrea<br>Eswatini<br>Ethiopia<br>Gabon<br>The Gambia<br>Ghana<br>Guinea<br>Guiné-Bissau<br>Côte d'Ivoire<br>Kenya<br>Lesotho<br>Liberia<br>Libya<br>Madagascar<br>Malawi<br>Mali<br>Mauritania<br>Morocco<br>Mozambique<br>Namibia<br>Niger<br>Nigeria<br>Rwanda<br>Senegal<br>Sierra Leone<br>Somalia<br>South Africa<br>South Sudan<br>Sudan<br>Togo<br>Tunisia<br>Uganda<br>Tanzania<br>Zambia<br>Zimbabwe</p> <p> </p> <p><strong>References</strong></p> <p>Sterl, S., Hussain, B., Miketa, A. <em>et al.</em> An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. <em>Sci Data</em> <strong>9</strong>, 664 (2022). <span><a href="https://doi.org/10.1038/s41597-022-01786-5">https://doi.org/10.1038/s41597-022-01786-5</a></span></p> <p><strong>See also</strong></p> <p>Sterl, S. (2024). Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America (1.0.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10650822" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10650822</a></p>
Model Inputs and Results - The role of coal plant retrofitting strategies in decarbonizing India's power system
<p>These files are the model inputs and results for the submission based on GenX version v0.3.6 - The role of coal plant retrofitting strategies in decarbonizing India’s power system</p>
Analytical modeling of an hybrid power module based on diamond and SiC devices
<p>This dataset contains the raw data used for the publication (available here : <a href="https://doi.org/10.1016/j.diamond.2022.108936">10.1016/j.diamond.2022.108936</a> ).</p> <p><strong>Analytical modeling of an hybrid power module based on diamond and SiC devices</strong></p> <p>Marine Couret, Anne Castelan, Nazareno Donato, Florin Udrea, Julien Pernot, Nicolas Rouger</p> <p>Detailed descriptions for each file can be found in "Dataset_Description.docx".</p>
Assessment of future wind speed and wind power changes over South Greenland using the MAR regional climate model : MAR ouptuts and KATABATA weather stations timeseries
<p>Daliy MARv3.12 outputs and KATABATA weather stations timeseries used in :</p> <p>Lambin, C., Fettweis, X., Kittel, C., Fonder, M., & Ernst, D. (2022).Assessment of future wind speed and wind power changes over South Greenland using the Modèle Atmosphérique Régional regional climate model. <em>International Journal of Climatology</em>, 43(1),558–574. https://doi.org/10.1002/joc.7795574 </p> <p> </p>
Customised pre-built Sector-coupled Euro-Calliope Model - Focus on the power sector and additional SPORES options
<p><strong>Customised pre-built Sector-coupled Euro-Calliope Model - Focus on the power sector and additional SPORES options</strong></p> <p><em>Based on the <a href="https://zenodo.org/record/5774988#.YqwqYDJByUk">pre-built Sector-coupled Euro-Calliope model</a> developed by Bryn Pickering</em></p> <p>This model is pre-packaged and ready to be loaded into Calliope, based on 2015 input data. To run the model as done in the associated publication you will need to do the following:</p> <ol> <li>Install a specific conda environment to be working with the correct version of Calliope ( <code>conda env create -f requirements.yml</code> )</li> <li>Run the model including only those scenarios that relate to the power sector and SPORES</li> </ol> <p> </p> <p><strong>Main and parallel batches of SPORES</strong></p> <p>To facilitate this second point and the reproduction of results, you'll find some pre-packaged python script with all and only those model scenarios that allow you to run either the "main batch" of SPORES (<code>spores_model_run.py</code>) or any of the "parallel batches" of SPORES (e.g., <code>excl_bio</code> and <code>max_bio</code>, which generate SPORES while minimising and, respectively, maximising bioenergy deployment).</p> <p> </p> <p><strong>Strength of the anchoring to extremes of the decision space</strong></p> <p>To tweak the strength of the anchoring to a specific technology feature, as we do in the paper, you need to modify the <code>euro_calliope/spores.yaml</code> override file. More precisely, you need to change the <code>excl_score</code> parameter in the objective function at the end of the file:</p> <pre><code class="language-bash">max_mode.run.spores_options.objective_cost_class: {'spores_score': 1, 'monetary': 0, 'excl_score': -1} excl_mode.run.spores_options.objective_cost_class: {'spores_score': 1, 'monetary': 0, 'excl_score': 1}</code></pre> <p>A value of 1 (for maximisation) or -1 (for minimisation) is the default by which we generate the primary results in the paper. By changing it to 0.1, you can reproduce as well the secondary results that we use as a sensitivity for a "weaker anchoring" to extreme technology features of the decision space.</p> <p><br> <strong>Weight-assignment method</strong></p> <p>Finally, to change the weight-assignment method, you need to modify the <code>euro_calliope/eurospores/model.yaml</code> file. More precisely, the <code>scoring_method</code> parameter, which can be one of the following: <code>integer</code>, <code>relative_deployment</code>, <code>random</code> or <code>evolving_average</code>.</p> <pre><code class="language-bash">run.spores_options.scoring_method: integer</code></pre> <p> </p> <p><strong>Hard-coded changes to be aware of</strong></p> <p>The files in this model theoretically allow accounting for all energy sectors (power, heat, transport, industry). Yet, we subset the analysis in the associated publication to only the power sector. To this end, we have modified the original electricity demand file (<code>euro_calliope/eurospores/electricity-demand.csv</code>).</p> <p>In fact, the original file did not account for the fraction of electricity associated with heat, transport or industry consumption, which was instead allocated to sector-specific demand files. In such a way, the model was free to decide whether to electrify these sectoral demands or not. In the present study, instead, we wanted to run our analysis based on the current electricity demand, inclusive of the currently electrified sector-specific demand. Therefore, we have replaced the original file with a new one that includes the present-day electricity demand, with no subtractions.</p> <p>If you want to run the analysis for all sectors, unlike we do in the study, you'll first need to recover the original file. You'll quickly find it in the same folder, named as <code>__electricity-demand.csv</code>.</p> <p><br> <strong>Summary of results from the paper</strong></p> <p>The folder <code>paper_summary_results</code> features some CSV files that summarise the results we obtained for our study across all the different tested search strategies.</p>
Multi-model Hydropower Projections for the United States Federal Power Marketing Areas under CMIP5 Climate Change Conditions
<p>This dataset contains an ensemble of monthly hydropower generation projections for the United States Federal Hydropower plants for the periods of 1966-2005 (historical period) and 2011-2050 (future period). The dataset includes the monthly hydropower projections developed in (Kao et al. 2016) based on the Watershed Runoff-Energy Storage (WRES) model and is complemented with another ensemble based on the process-based Water Management Power (WMP) model.</p> <p>The hydrologic projections are estimated through a cascading modeling toolchain that include ten global climate change model projections (ACCESS1-0, BCC-CSM1-1, CCSM4, CMCC-CM, GFDL-ESM2M, MIROC5, MPI-ESM-MR, MRI-CGCM3, NorESM1-M and IPSL-CM5A-LR) under RCP8.5 scenario, which are dynamically downscaled with a regional climate model (RegCM4) ( Pal et al. 2007, Giorgi et al. 2012)), which then inform the Variable Infiltration Capacity (VIC) hydrology model (Liang et al. 1994). The ensemble of hydrologic projections is then informing two processes to translate runoff into hydropower projections. First, WRES models monthly river routing and employs a non-linear statistical approach relating monthly natural flow to hydropower generation, including processes such as spilling. Second, MOSART-WM (Voisin et al. 2013), a large-scale river routing and water management model, provides daily reservoir storage and regulated release at dam locations as well as regulated flow at run-of-the-river power plants. The WMP model then translates reservoir and regulated river dynamics into hydropower projections (Zhou et al. 2018). Those projections are further calibrated to monthly generation provided by the federal utilities. The US federal hydropower plants analyzed in this study include 132 facilities that were built and/or are operated by the US Army Corps of Engineers (USACE), the Bureau of Reclamation (Reclamation), and the International Boundary and Water Commission (IBWC). The electricity generation projected for these hydropower plants were aggregated by four Power Marketing Administrations (PMAs), including Bonneville Power Administration (BPA), Southeastern Power Administration (SEPA), Southwestern Power Administration (SWPA), and Western Area Power Administration (WAPA), and their associate subregions.</p> <p>The two files, <em>SWA9505V2_Gsim_PMA_WRES.mat</em> and <em>SWA9505V2_Gsim_PMA_WMP.mat</em>, represent model outputs from the two hydropower models, WRES and WMP respectively.</p> <p>Each file contains 6 variables:</p> <p>1) “Models”: the 10 global climate models (GCMs).</p> <p>2) “PMA_areas”: the 18 subregions of PMAs as defined in (Kao et al. 2015).</p> <p>3) “PMA_G_mn_6605”: 1966-2005 projected monthly hydropower generation for each PMA sub-regions. Dimension: (12 [months], 40 [years], 18 [subregions], 10 [GCMs]). Unit: MWH.</p> <p>4) “PMA_G_mn_1150”: Same as “PMA_G_mn_6605”, but for 2011-2050 projected hydropower generation.</p> <p>5) “PMA_G_yr_6605”: 1966-2005 projected annual hydropower generation. Dimension: (40 [years], 18 [subregions], 10 [GCMs]) . Unit: MWH.</p> <p>6) “PMA_G_yr_1150”: Same as “PMA_G_yr_6605”, but for 2011-2050 projected hydropower generation.</p> <p>The following journal paper details the method in creating the dataset:</p> <p><strong>Impacts of Climate Change on Subannual Hydropower Generation: A Multi-model Assessment of the United States Federal Hydropower Plants</strong></p> <p><strong>Zhou et al. (2022) Preparing for submission to Environmental Research Letters.</strong></p>
Data and code from: Learning a deep language model for microbiomes: The power of large scale unlabeled microbiome data
<p>We use open source human gut microbiome data to learn a microbial "language" model by adapting techniques from Natural Language Processing (NLP). Our microbial "language" model is trained in a self-supervised fashion (i.e., without additional external labels) to capture the interactions among different microbial species and the common compositional patterns in microbial communities. The learned model produces contextualized taxa representations that allow a single bacteria species to be represented differently according to the specific microbial environment it appears in. The model further provides a sample representation by collectively interpreting different bacteria species in the sample and their interactions as a whole. We show that, compared to baseline representations, our sample representation consistently leads to improved performance for multiple prediction tasks including predicting Irritable Bowel Disease (IBD) and diet patterns. Coupled with a simple ensemble strategy, it produces a highly robust IBD prediction model that generalizes well to microbiome data independently collected from different populations with substantial distribution shift.</p> <p>We visualize the contextualized taxa representations and find that they exhibit meaningful phylum-level structure, despite never exposing the model to such a signal. Finally, we apply an interpretation method to highlight bacterial species that are particularly influential in driving our model's predictions for IBD.</p>
"Demand-side policies for power generation in response to the energy crisis: A model analysis for Italy", scripts and data
<p>This repository contains the data, scripts and results for the paper "Demand-side policies for power generation in response to the energy crisis: A model analysis for Italy", https://doi.org/10.1016/j.esr.2024.101329.</p> <p>Results in the paper are divided into three sections, corresponding to the numbers of the folders inside this dataset. They are described as follows:</p> <p>1 - EU policy impact: What is the impact on the Italian electricity of the european proposal of cutting power demand and shifting it during peak hours on gas consumption, system costs and emissions?</p> <p>2 - Gas cost sensitivity: Which would be Italy’s most convenient power system considering different gas prices?</p> <p>3 - DSM in mitigation: What could be the role of demand side measures in power systems with a high penetration of RES?</p>
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.