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99 results for “Renewable energy”
German Renewable Energy Tariff Register
<p>The TariffRegister compiles all tariff types paid to renewable energy units in Germany under the RES Act 2000 and its subsequent amendments. This dataset is based on the tariff files reported by the four German Transmission System Operators (TSOs) on their conjoint reporting <a href="https://www.netztransparenz.de/EEG/Verguetungs-und-Umlagekategorien">webpage</a>. It includes over 6500 different tariff types applicable from 2000 to 2023. The file consists of 19 descriptors, the tariff identification code (Tariff_ID), publication year, associated technology, commissioning year of applicable units, unit characterization criteria (Criteria1 and Criteria2), bonus details (Bonus_name and Bonus_start), fuel type (Fuel), tariff values (Tariff), management premium, "Ausfallvergütung" value, “Mieterstromzuschlag”, and date of tariff introduction or change. Additionally, two tariff categorization levels (Category1 and Category2) group tariffs into 29 and 17 respective categories, while the KWK descriptor indicates CHP-biomass plant subsidies and the "Sign" descriptor signifies positive or negative tariffs.</p> <p>Notes:</p> <p>The TariffRegister.csv can be matched with the <a href="https://zenodo.org/record/8010410">PaymentRegister</a> dataset to identify the payment types received by renewable energy units.</p> <p>The provided categorization process reflects the specific research needs of the Chair of Energy Economics at BTU and does not affect the original data, allowing users to choose whether to utilize the categorization descriptors.</p> <p>Users can update the dataset by following the outlined steps:</p> <ol> <li>Update the “Original files” by downloading the latest published data (<a href="http://www.netztransparenz.de/EEG/Verguetungs-und-Umlagekategorien">here</a>)</li> <li>Clean the files from any row that is not including a Tariff_ID</li> <li>Include the categorization descriptors by matching the categories based on the Tariff_ID and update the categories for new tariffs.</li> <li>Paste this information into a CSV file and store it under “Cleaned files” and check that the column names and data types match the information from previous files.</li> <li>Run the provided R code after modifying the top-level directory.</li> </ol> <p>This publication is part of a series of datasets on German Renewable Energy Sources Data, which also includes a <a href="https://zenodo.org/record/7945029">UnitRegister</a>, a <a href="https://zenodo.org/record/8010410">PaymentRegister</a>, and the combination of this information to analyze individual bids in renewable auctions. For a comprehensive understanding of the datasets and their relationships, we recommend referring to two related publications:</p> <ul> <li>Batz Liñeiro, T. B., Müsgens, F. (2023). "Evaluating the German onshore wind auction programme: An analysis based on individual bids." Energy Policy, 172, 113317. <a href="https://doi.org/10.1016/j.enpol.2022.113317">https://doi.org/10.1016/j.enpol.2022.113317</a> | <a href="https://ssrn.com/abstract=4130232">Preprint</a></li> <li>Batz Liñeiro, T., Müsgens, F. (2021). "Evaluating the German PV auction program: The secrets of individual bids revealed." Energy Policy, 159, 112618. <a href="https://doi.org/10.1016/j.enpol.2021.112618">https://doi.org/10.1016/j.enpol.2021.112618</a> | <a href="http://arxiv.org/abs/2104.07536">Preprint</a></li> </ul>
German Renewable Energy Payment Register
<p>The PaymentRegister dataset offers a comprehensive compilation of subsidies and tariffs paid to renewable energy units installed in Germany between 2000 and 2021. The dataset is constructed based on the yearly statements provided by the four German Transmission System Operators (TSOs) on their conjoint reporting webpage. These statements are updated annually around August, with a one-year lag, and the dataset encompasses yearly statements from 2007 to 2021.<br> Containing over 53 million observations, the dataset comprises 12 variables/descriptors. It includes a unit identifier (Unit_ID) that can be matched with the <a href="https://zenodo.org/deposit/7945029">UnitRegister</a> dataset, along with information on generation (G_kWh) and associated payments (P_Euro). Additionally, descriptors related to the associated tariff are provided, such as the Tariff identification code (Tariff_ID) and the regulated tariff value (Tariff). The dataset also includes the technology of the unit, a tariff categorization (Ref1, Ref2) based on the payment type (e.g., full feed-in tariff, market premium, bonus payment, reduced payment due to sanction), the year of payment (Year), and the name of the TSO under which the unit is registered (TSO). Notably, two additional descriptors, "Tariff1" and "P_Euro1," are included to highlight any discrepancies between reported tariff values and actual payments.</p> <p>Notes:</p> <p>The PaymentRegister.csv dataset was compiled to meet the research needs of the Chair of Energy Economics at BTU. Payments were categorized based on their tariffs, resulting in a more specific first categorization process with 29 categories, followed by a second categorization level grouping the tariffs into 17 categories. However, these categorizations are solely descriptive and do not impact the original data, allowing users to choose whether to utilize the categorization descriptors or not.</p> <p>To assist users in utilizing the dataset, we have provided the R-code used for data processing. By downloading the data package and modifying the top-level directory (line 13), users can run the code. To update the dataset, the following steps are recommended:</p> <ol> <li>Update the TariffRegister following this publication.</li> <li>Download the new year statements from the TSOs into each TSO folder and run the individual TSO data analysis R-codes. Please modify the top-level directory for these codes as well and update the sections for the new data. Ensure that any updated datasets maintain consistency with the previous file structure (which has not always been the case in the past).</li> <li>Execute the main code to bind all the files together.</li> </ol> <p>This publication is part of a series of datasets on German Renewable Energy Sources Data, which also includes a <a href="https://zenodo.org/deposit/7945029">UnitRegister</a>, a <a href="https://zenodo.org/record/8013071">TariffRegister</a> and the combination of this information to analyze individual bids in renewable auctions. For a comprehensive understanding of the datasets and their relationships, we recommend referring to two related publications:</p> <p>• Batz Liñeiro, T. B., Müsgens, F. (2023). "Evaluating the German onshore wind auction programme: An analysis based on individual bids." Energy Policy, 172, 113317. <a href="https://doi.org/10.1016/j.enpol.2022.113317">https://doi.org/10.1016/j.enpol.2022.113317</a> | <a href="https://ssrn.com/abstract=4130232">Preprint</a></p> <p>• Batz Liñeiro, T., Müsgens, F. (2021). "Evaluating the German PV auction program: The secrets of individual bids revealed." Energy Policy, 159, 112618. <a href="https://doi.org/10.1016/j.enpol.2021.112618">https://doi.org/10.1016/j.enpol.2021.112618</a> | <a href="http://arxiv.org/abs/2104.07536">Preprint</a></p> <p> </p>
Dataset for: Utilizing high-resolution genetic markers to track population-level exposure of migratory birds to renewable energy development
<p class="MsoNormal"><span>With new motivation to increase the proportion of energy demands met by zero-carbon sources, there is a greater focus on efforts to assess and mitigate the impacts of renewable energy development on sensitive ecosystems and wildlife, of which birds are of particular interest. One challenge for researchers, due in part to a lack of appropriate tools, has been estimating the effects from such development on individual breeding populations of migratory birds. To help address this, we utilize a newly developed, high-resolution genetic tagging method to rapidly identify the breeding population of origin of carcasses recovered from renewable energy facilities and combine them with maps of genetic variation across geographic space (called 'genoscapes') for five species of migratory birds known to be exposed to energy development, to assess the extent of population-level effects on migratory birds. We demonstrate that most avian remains collected were from the largest populations of a given species. In contrast, those remains from smaller, declining populations made up a smaller percentage of the total number of birds assayed. Results suggest that application of this genetic tagging method can successfully define population-level exposure to renewable energy development and may be a powerful tool to inform future siting and mitigation activities associated with renewable energy programs.</span></p>
RENEW Scleroderma: A Peer-Mentored, Web Intervention for Resilience-based, Energy Management to Enhance Wellbeing and Fatigue
ClinicalTrials.gov study NCT04908943. IPD Sharing: YES. Countries: 1. Publications: 4.
Aligning renewable energy expansion with climate-driven range shifts
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Dataset for: Utilizing high-resolution genetic markers to track population-level exposure of migratory birds to renewable energy development
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Decision problem for renewable energy planning in Turkey (Dataset). June 2019
<p>This dataset compromises the decision problem for renewable energy planning in Turkey, adopted from the literature (Kahraman and Kaya 2010; Erdogan and Kaya 2015; Mousavi et al. 2017).</p> <p>The decision problem consists in evaluating and selecting the most appropriate renewable energy alternative for Turkey, and includes:</p> <ul> <li>Five alternatives (<strong>Hydro, Wind, Solar, Biomass, and Geothermal</strong>).</li> <li>Five dimensions fo criteria (<strong>Technological, Technical, Economical, Environmental, and Socio-politic</strong>), with 31 sub-criteria.</li> <li>Weights of criteria.</li> <li>Direction of criteria: "max" if the sub-criterion needs to be maximized ("bigger is better”) and "min" if it needs to be minimized (”smaller is better”).</li> </ul> <p>The dataset is used in the following paper:</p> <p><em>Ezbakhe, F. and Pérez-Foguet, A. (2019) Decision analysis for sustainable development: the case of renewable energy planning under uncertainty. European Journal of Operational Research. </em><a href="https://doi.org/10.1016/j.ejor.2020.02.037">https://doi.org/10.1016/j.ejor.2020.02.037</a></p>
Non-Profit Energy Cooperatives as the Catalyst of the Movement of People to Renewable Electricity
<p>The Environment, Energy and Natural Resources (EENR) Center of University of Houston Law Center hosted a webinar on April 3rd – 11:00 am EDT as part of the Energy Transition Governance Project Webinar/Lecture Series.</p> <p>The webinar features: Melissa K. Scanlan, Visiting Professor, Boston College Law School, Director, New Economy Law Center, Law Professor, Vermont Law School;</p> <p>Gabe Pacyniak, Assistant Professor of Law, University of New Mexico;</p> <p>Aubin Nzaou, Marie Sklodowska-Curie Fellow in Law and Energy Policy, University of Houston Law Center.</p> <p>The renewable energy revolution poses a once in a lifetime opportunity for co-benefits of democracy, bringing electricity to those who lack it, and equity. This transition has the potential for widely shared prosperity, not just a decrease in suffering. To unlock that potential, however, we also need to disrupt business as usual in the dominant business model. The co-benefits are more likely if the institutions that lead the way are democratically owned and managed with the explicit goal of bringing the benefits of ownership to those who have been traditionally marginalized by the current economic and energy system. The talk will sketch the pathways to sustainable development and deep decarbonization of energy at the global level. First, Professor Melissa Scanlan will discuss research on non-profit energy cooperatives she is leading, which demonstrates how different aspects of the transition are already underway. Each case study, from a comparative perspective of Spain and the U.S., includes an overview of the cooperative’s history, type, and economic sector; context based on its industry and country; best practices in environmental sustainability; and its governance and cooperative legal structure. The panel will discuss Spanish renewable energy cooperatives involved in various points in the energy system and a US electricity cooperative in the politically conservative Deep South that is leading the transition to renewables. Second, Professor Gabe Pacyniak will discuss how the rural electricity cooperative model in the United States also presents some challenges when it comes to shifting to a lower-carbon electricity grid. The over 900 electric cooperatives in the United States have generally been slower to shift to lower-carbon electricity in comparison to other types of utilities. Pacyniak will share research that identifies structural and institutional factors for this lag, and will also highlight opportunities to strengthen cooperative supports and oversight to accelerate the shift to a low-carbon electricity system.</p>
Energy consumption and renewable generation data of 5 aggregators - 15 minute resolution (13 bus grid)
<p>Type: Energy consumption and renewable generation data</p> <p>Period of data collection: 19-03-2019 to 25-03-2019 (15-minute 672 periods)</p> <p>Resolution: 15 minutes</p> <p>Network: 13-bus MV grid</p> <p>Aggregator list:</p> <ul> <li>Aggregator 1: Shopping Mall; Hospital; Fire Station</li> <li>Aggregator 2: 15 houses</li> <li>Aggregator 3: 7 Office buildings</li> <li>Aggregator 4: Wind, PV</li> <li>Aggregator 5: Slow and fast-charging stations of electric vehicles</li> </ul> <p>Further data:</p> <ul> <li>Market prices 2019 summer and winter</li> <li>Wind generation curve</li> </ul> <p>Data obtained from CENERGETIC project (<a href="http://www.gecad.isep.ipp.pt/CENERGETIC/">http://www.gecad.isep.ipp.pt/CENERGETIC/</a>)</p> <p>National Funds through the FCT—Portuguese Foundation for Science and Technology, under Project PTDC/EEI-EEE/28983/2017 (CENERGETIC), CEECIND/02814/2017, UIDB/00760/2020.</p>
Data from: Conservation planning for offsetting the impacts of development: a case study of biodiversity and renewable energy in the Mojave Desert
Balancing society's competing needs of development and conservation requires careful consideration of tradeoffs. Renewable energy development and biodiversity conservation are often considered beneficial environmental goals. However, the direct footprint and disturbance of renewable energy can displace species' habitat and negatively impact populations and communities if sited without ecological consideration. To mitigate residual impacts, offsets have emerged as a potentially useful tool after trying to avoid, minimize, or restore affected sites. Yet where many species or many sites are involved, the problem of efficiently designing a set of offset sites becomes increasingly complex. Spatial conservation prioritization tools are designed to handle this problem, but have seen little application to offset siting and analysis. To address this need we designed an offset siting support tool for the Desert Renewable Energy Conservation Plan (DRECP) of California, and present a case study of hypothetical impacts from solar development in the Western Mojave subsection. We compare two offset scenarios designed to mitigate a hypothetical 15,331 ha derived from proposed utility-scale solar energy development (USSED) projects. The first prioritizes offsets based precisely on impacted features, while the second offsets impacts based on the potential to maximize biodiversity conservation gains in the region. The two methods only agree on 28% of their prioritized sites and differ in meeting species-specific offset goals. Differences between the two scenarios highlight the importance of clearly specifying choices and priorities for offset siting and mitigation in general. Similarly, the effects of background climate and land use change may lessen the durability or effectiveness of offsets if not considered. Our offset siting support tool was designed specifically for the DRECP area, but with minor code modification could work well in other offset analyses, and provide continuing support for a potentially innovative mitigation solution to environmental impacts.
High resolution, interactive, or animated versions of illustrations used in the paper "Quantifying the Dunkelflaute: An analysis of variable renewable energy droughts in Europe"
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A comprehensive review of stationary energy storage devices for large scale renewable energy sources grid integration
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Renewable energy integration in intralogistics
<p>Data supporting the study of renewable integration in robotic compact storage and retrieval systems.</p>
Networks files of main scenarios analysed in "Distributed photovoltaics provides key benefits for a highly renewable European energy system"
<p>This repository contains the network files (.nc) of the main scenarios (A, B, C, and D) used for analysis in the paper. The code for reproducing these files plus other network files used for sensitivity analysis plus the Jupyter notebooks used for creating all the figures in the paper are available at: https://github.com/Parisra/Distributed-PV-paper </p>
NCSR Renewable Energy Pilot Building Dataset
<p>Sensor data and heat production data, from pilot building installation. Combined with external weather data. CVS format.</p>
Drivers of renewable energy promotion in the EU-27, 2013-2021
<p><span>27 EU member states data on economic, geographical, environmental and structural indicators for the period 2013-2021</span></p>
renewable energy share by region
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Google Trend Enhanced Deep Learning Dataset for Renewable Energy Asset Price Prediction
<h3>Overview</h3> <p>This dataset accompanies the research paper titled <strong>“<a href="https://doi.org/10.1016/j.knosys.2024.112733">A Google Trend Enhanced Deep Learning Model for the Prediction of Renewable Energy Asset Price</a>”</strong> by Dr. Nachiketa Mishra, Dr. Lalatendu Mishra, Balaji Dinesh, P M Kavyassree . The study investigates the predictive efficiency of various forecasting models using oil prices and investor sentiment for renewable energy assets, specifically focusing on renewable energy ETFs such as ICLN, PBD, and QCLN.</p> <p>The dataset contains the processed inputs and raw data used in the analysis, including sentiment indices derived from Google Trends and traditional financial indices.</p> <h3>Citation :</h3> <p>Please cite this dataset as:</p> <ul> <li>Mishra, L., Dinesh, B., Kavyassree, P.M. and Mishra, N., 2024. A Google Trend enhanced deep learning model for the prediction of renewable energy asset price. <em>Knowledge-Based Systems</em>, p.112733.</li> </ul> <pre><code>@bibtex<br><br>@article{MISHRA2025112733,<br>title = {A Google Trend enhanced deep learning model for the prediction of renewable energy asset price},<br>journal = {Knowledge-Based Systems},<br>volume = {308},<br>pages = {112733},<br>year = {2025},<br>issn = {0950-7051},<br>doi = {https://doi.org/10.1016/j.knosys.2024.112733},<br>url = {https://www.sciencedirect.com/science/article/pii/S0950705124013674},<br>author = {Lalatendu Mishra and Balaji Dinesh and P.M. Kavyassree and Nachiketa Mishra},<br>}</code><code><br></code></pre> <h2>Code : </h2> <p>Refer Repository URL provided</p> <h2>Directory Structure and Description</h2> <pre><code>📦 data ├── 📂 etf-data │ ├── 📜 ICLN_INPUT.csv # Input data for ICLN │ ├── 📜 PBD_INPUT.csv # Input data for PBD │ ├── 📜 QCLN_INPUT.csv # Input data for QCLN │ └── 📂 raw-data # Original unprocessed data │ ├── 📂 market-data # ETF market prices and oil volatility (OVX) │ ├── 📂 navs # Net Asset Value (NAV) data │ └── 📂 volatility # Volatility data (GARCH and Moving Average models) ├── 📂 google-trends │ ├── 📜 keys.txt # Keywords for Google Trends search │ ├── 📂 trends │ ├── 📂 first-principal-components # Final Google Trend Index (PCA) │ ├── 📂 formatted-trends # Cleaned trends data │ └── 📂 raw-google-trends # Raw fetched Google Trends data</code></pre> <pre>Key Files</pre> <ul> <li><strong>ICLN_INPUT.csv</strong>, <strong>PBD_INPUT.csv</strong>, <strong>QCLN_INPUT.csv</strong>: Processed inputs for the prediction models of each ETF.</li> <li><strong>raw-data</strong>: Contains original data for market prices, NAVs, and volatility measures (GARCH, Moving Average).</li> <li><strong>google-trends</strong>: Data related to Google search trends, including raw, formatted, and the final index derived using Principal Component Analysis (PCA).</li> </ul> <h3>Usage Notes</h3> <ol> <li><strong>Google Trends Data</strong>: The Google Trend Index constructed from the keywords can be found in the <code>first-principal-components</code> folder. This index was a key input in the predictive models and used to construct modified indices in data>*_INPUT.csv’s.</li> <li><strong>Reproducibility</strong>: For reproducing the results from the study, you can directly use the inputs provided under <code>/data</code> to build predictive models.</li> <li><strong>Modifications</strong>: If you aim to modify or extend the dataset, be cautious of the index construction process, particularly around Principal Component Analysis (PCA) in the Google Trends data.</li> </ol> <h2>License</h2> <p>This dataset is released under the <strong>Creative Commons Attribution 4.0 International (CC BY 4.0)</strong> license. You are free to share and adapt the data, provided appropriate credit is given.</p> <h2>Contact Information</h2> <p>For any questions or further information, please contact:</p> <ul> <li><strong>Dr. Nachiketa Mishra</strong>: Department of Mathematics, Indian Institute of Information Technology Design and Manufacturing Kancheepuram, India</li> <li><strong>Dr. Lalatendu Mishra</strong>: Department of Management Sciences, Indian Institute of Technology Kanpur, India</li> <li><strong>Balaji Dinesh</strong>: Department of Computer Science, Indian Institute of Information Technology Design and Manufacturing Kancheepuram, India. email : <a href="mailto:balajidinesh918@gmail.com">balajidinesh918@gmail.com</a></li> </ul>
Online companion -Technical Impacts of the Deployment of Renewable Energy Community on Electricity Distribution Grids
<p>Online companion for the article "Technical Impacts of the Deployment of Renewable Energy Community on Electricity Distribution Grids".</p>
Renewable energies and biodiversity: impact of ground-mounted solar photovoltaic sites on bat activity
<ol> <li>Renewable energy is growing at a rapid pace globally, but as yet there has been little research on the effects of ground-mounted solar photovoltaic (PV) developments on bats, many species of which are threatened or protected.</li> <li>We conducted a paired study at 19 ground-mounted solar PV developments in southwest England. We used static detectors to record bat echolocation calls from boundaries (i.e., hedgerows) and central locations (open areas) at fields with solar PV development, and simultaneously at matched sites without solar PV developments (control fields). We used generalized linear mixed-effect models to assess how solar PV developments and boundary habitat affected bat activity and species richness.</li> <li>The activity of six of eight species/species groups analysed was negatively affected by solar PV panels, suggesting that loss and/or fragmentation of foraging/commuting habitat is caused by ground-mounted solar PV panels. <em>Pipistrellus</em> <em>pipistrellus</em> and <em>Nyctalus</em> spp. activity was lower at solar PV sites regardless of the habitat type considered. Negative impacts of solar PV panels at field boundaries were apparent for the activity of <em>Myotis</em> spp. and <em>Eptesicus</em> <em>serotinus</em>, and in open fields for <em>Pipistrellus</em> <em>pygmaeus</em> and <em>Plecotus</em> spp.</li> <li>Bat species richness was greater along field boundaries compared with open fields, but there was no effect of solar PV panels on species richness.</li> <li> <em>Policy Implications</em>: Ground-mounted solar PV developments have a significant negative effect on bat activity, and should be considered in appropriate planning legislation and policy. Solar PV developments should be screened in Environmental Impact Assessments for ecological impacts, and appropriate mitigation (e.g., maintaining boundaries, planting vegetation to network with surrounding foraging habitat) and monitoring should be implemented to highlight potential negative effects.</li> </ol>
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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.