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35 results for “Energy technology”
Probabilistic projections of granular energy technology diffusion at subnational level - solar photovoltaics, heat pumps, and battery electric vehicles in Switzerland
<p>The probabilistic projections are part of the work: <br><em>Nik Zielonka, Xin Wen, Evelina Trutnevyte, Probabilistic projections of granular energy technology diffusion at subnational level, PNAS Nexus, Volume 2, Issue 10, October 2023, pgad321, </em><a href="https://doi.org/10.1093/pnasnexus/pgad321"><em>https://doi.org/10.1093/pnasnexus/pgad321</em></a></p> <p>Please cite the article together with the Zenodo link when you use the data.</p> <p>The provided data files contain the estimated probabilistic projections for all Swiss municipalities on the actual diffusion of solar photovoltaics (PV), heat pumps, and battery electric vehicles (BEVs) in Switzerland for the indicated years:</p> <p>Version 2022-2050: Projections for the years 2022-2050 as presented by Zielonka et. al (2023), PNAS Nexus.<br>Version 2023-2050: Projections for the years 2023-2050, using the latest data of 2022.<br>Version 2024-2050: Projections for the years 2024-2050, using the latest data of 2023.</p> <p>The computations were performed at University of Geneva using Baobab HPC service.</p> <p>This research was carried out with the support of the Swiss Federal Office of Energy SFOE as part of the SWEET project SURE (N.Z., E.T.) and the Swiss National Science Foundation Eccellenza Grant as part of the project "Accuracy of long-range national energy projections" (Grant no. 186834, X.W., E.T.). The authors bear sole responsibility for the conclusions and the results.</p>
Dataset: energy consumption patterns in a science and technology park
<p>This dataset contains time series of energy consumption and external temperature for a group of buildings in a science and technology park, from 2018 to 2022, that is suitable for the development of algorithms to improve energy efficiency and for the early detection of energy consumption peaks based on night-time outdoor temperatures.</p> <p>Time series of power, energy consumption and external temperature for group of tertiary buildings, from 2018-01-01 to 2022-09-15.</p> <p>Peaks in electricity consumption are a major concern for building owners, especially during summer, when external temperatures are high, and users demand air conditioning. Owners may face high costs, observe increased risk of overheating in energy intensive equipment, and may exceed the power threshold set out in the electricity supply contract.</p> <p>Several effective "peak-shaving" strategies can be put in place, such as a higher temperature set-point (which implies a temporary reduction of comfort levels), switching off low-priority processes, and starting the cooling process earlier than usual.</p> <p>This dataset can be used to develop algorithms for early detection of peaks in energy consumption, based on external temperatures measured during the night.</p> <p> </p>
Teaser SUNRISE releases its technological roadmap to a clean energy EU
<p>Promotional video roadmap: We are thrilled to present our freshly released technological roadmap! The SUNRISE technological roadmap is the result of the integrated knowledge of a broad group of scientists across Europe and a key step to engage the whole community towards building a climate neutral EU. This roadmapping process was launched in May 2019 by a dedicated working group within the SUNRISE consortium, collecting and analyzing broad input from over 180 stakeholders at the SUNRISE Stakeholder Workshop on 17-18 June, 2019, in Brussels.</p>
Library of price and performance data of domestic and commercial technologies for low-carbon energy systems
<p>This library consists of extensive price and performance data of commercially available technologies for low-carbon energy systems on the UK market, including domestic and commercial applications. All information is obtained from published manufacturer datasheets and pricelists. The library contains useful information for energy-system and technology modellers in their efforts to capture the techno-economic characteristics of different technology options, minimise uncertainties and suggest reliable system- and technology-design strategies.</p> <p>The data analysis shows how technology characteristics vary with component choice, technology size and to capture the spread of values found between different suppliers, which can be used to determine uncertainty bounds on key performance indicators. Fitting techniques can be used to determine relationships arising from the collected data, infer values for which data are not available and quantify the related variability in technology characteristics.</p> <p>This work was conducted by members of the <a href="https://www.imperial.ac.uk/clean-energy-processes/">Clean Energy Processes (CEP) Laboratory</a> and is part of Project 2 of the <a href="https://www.imperial.ac.uk/energy-futures-lab/idles/">Integrated Development of Low-Carbon Energy Systems (IDLES)</a> project. IDLES brings together researchers across Imperial College London and partner organisations and companies to provide the evidence needed to facilitate a cost-effective and secure transition to a low-carbon future. The overarching aim of Project 2 is: (i) to characterise current and new/future technologies in terms of cost and performance to provide evidence for whole-energy system modelling; and (ii) to extend the capabilities of whole-energy-system models so that they can, apart from optimising energy network infrastructures, provide information to manufacturers about the optimal choice of materials, components and the design of key technologies.</p> <p>The library will be updated regularly as more data regarding existing and new technologies are collected.</p>
Energy Efficiency Technologies Costs - Support File for Data Manipulation Starter Data Kits
<p>This file can be used to manipulate the Energy Efficiency Technologies Costs data for the Starter Data Kits. </p>
Consider Social Acceptability of Contentious Low-Carbon Energy Technologies
<p>SSH CENTRE (Social Sciences and Humanities for Climate, Energy aNd Transport Research Excellence) is a Horizon Europe project, engaging directly with stakeholders across research, policy, and business (including citizens) to strengthen social innovation, SSH-STEM collaboration, transdisciplinary policy advice, inclusive engagement, and SSH communities across Europe, accelerating the EU’s transition to carbon neutrality. <br>SSH CENTRE is based in a range of activities related to Open Science, inclusivity and diversity – especially with regards Southern and Eastern Europe and different career stages – including: development of novel SSH-STEM collaborations to facilitate the delivery of the EU Green Deal; SSH knowledge brokerage to support regions in transition; and the effective design of strategies for citizen engagement in EU R&I activities. Outputs include action-led agendas and building stakeholder synergies through regular Policy Insight events.<br>This is captured in a high-profile virtual SSH CENTRE generating and sharing best practice for SSH policy advice, overcoming fragmentation to accelerate the EU’s journey to a sustainable future.<br>The documents uploaded here are part of WP2 whereby novel, interdisciplinary teams were provided funding to undertake activities to develop a policy recommendation related to EU Green Deal policy. Each of these policy recommendations, and the activities that inform them, will be written-up as a chapter in an edited book collection. Three books will make up this edited collection - one on climate, one on energy and one on mobility.</p> <p> <br>The present project proposes to conduct a research on social acceptability of three controversial technologies in France: nuclear fusion, agrivoltaics and offshore wind turbines. We review the literature and develop a framework for studying the dynamics of acceptability of these technologies over the entire 2013-2023 period. </p> <p>This file contains:</p> <ul> <li>Appendix 1 : This output uploaded is the literary corpus from the EUROPRESSE database used to carry out the sentimental analysis and the topics modelling</li> <li>Appendix 2 : The scraping results of the corpus from the EUROPRESSE database</li> <li>Appendix 3 : The topic modeling results of the corpus from the EUROPRESSE database</li> <li>Appendix 4 : The sentimental analysis results of the corpus from the EUROPRESSE database</li> <li>Appendix 5 : This file provides a comprehensive analysis of the factors shaping the social acceptability of low-carbon energy technologies in France</li> <li>Appendix 6 : This file sorted the 10 most positive and the 10 most negative articles associated with each low-carbon energy technology</li> </ul>
Dataset: Clean Energy Technologies, Inc. (CETY) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: CBAK Energy Technology, Inc. (CBAT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
NB-IoT vs. LTE-M: Measurement Data of the Energy Consumption of LPWAN Technologies
<p><strong>NB-IoT vs. LTE-M: Measurement Data of the Energy Consumption of LPWAN Technologies</strong></p> <p>This dataset contains the raw energy measurements as well as R scripts to reproduce the energy consumption plot for the corresponding paper.</p> <p>Each .csv file contains a specific set of measurements and we provide a script to read, process and plot the contained data.</p> <p><strong>Figure 3</strong></p> <p>Mean energy consumption of the different phases for Authentication for NB-IoT and LTE-M.</p> <p>Due to the fact that the duration of <em>Idle Connected</em> in the measurement scripts was 30 seconds and 60 seconds for <em>Idle Not Connected</em>, the D-value and the mean power consumption are divided by 2.</p> <ul> <li>Data – energy_measurements_fig3.csv</li> <li>Code – fig3.R</li> </ul> <p><strong>Figure 4</strong></p> <p>Mean energy consumption of the different phases for Data Connection and Download for NB-IoT and LTE-M for 1KB of data in HTTP.</p> <p>The delay between the measurements for Figure 4 were all 30 seconds long, but the identified <em>Standby</em> and <em>Idle</em> phases have different lengths. Therefore, the <em>Idle</em> phase values for both access technologies have been normalized and calculated for 20 seconds each.</p> <ul> <li>Data – energy_measurements_fig4.csv</li> <li>Code – fig4.R</li> </ul> <p><strong>Figure 5</strong></p> <p>Mean energy consumption of the different phases for Data Connection and Download for HTTP and MQTT for 1KB of data in NB-IoT.</p> <p>In this scenario the delay between the measurements were different again. For <em>MQTT</em> the delay was 150 seconds and for <em>HTTP</em> 30 seconds. Therefore, the data during the <em>Idle</em> and <em>Standby</em> (only for <em>MQTT</em>) phase is normalized and calculated for 20 seconds and 10 seconds, respectively. During the <em>MQTT</em> <em>Idle</em> phase measurements, the device disconnects. This is not taken into account for the evaluation, which is why these energy values are discarded for this figure.</p> <ul> <li>Data – energy_measurements_fig5.csv</li> <li>Code – fig5.R</li> </ul> <p><strong>Contact</strong></p> <p>For questions or issues with this code, please contact Viktoria Vomhoff (viktoria.vomhoff@uni-wuerzburg.de) or any of the authors of the related publication.</p>
The dataset for the research "Evaluation of Digital Supply Chain Technology’s Impact on Sustainability Under the Moderate Effect of Supply Chain Dynamism: An Empirical Research in the Chinese Energy Supply Chain"
In recent years, the topic of digitalisation and sustainability of supply chains has become increasingly important. In addition, as the environmental dynamism becomes more complex, it is essential to explore how technologies impacts on sustainability under the supply chain dynamism. Hence, there is a study to explore the relationship between technologies and sustainability under the supply chain dynamism in the energy supply chain. In this study, the author collects quantitative data from two Chinese companies, including China Resources Power Zhejiang Company and Hunan HuaDian Changsha Electric Co., Ltd. This is a questionnaire survey and it has 24 questions, including 3 general questions, 5 technologies dimension questions, 12 sustainability dimension questions and 4 supply chain dynamism questions. The author collected data from 30 May 2024 to 6 June May 2024, and there are totally 316 answers.
Effects of water source and technology on energy use and environmental impacts of rice production in northern Iran
<p>To analyze the energy flow and greenhouse gas emissions, a total of 200 paddy fields were selected based on the water source utilized for irrigation (river as the surface water source and well as the underground water source), transplanting methods (traditional and mechanical) and grain yield (low- and high-yielding rice cultivars) as a factorial experimental design (2×2×2=8 conditions). Data were collected in 2020 from 200 rice growers in northern Iran using a face-to-face questionnaire survey.</p>
The role of energy storage technologies in the context of the Swiss energy transition (SwissStore)
<p>The role of energy storage is subject to intense debates at the national and international level, reflecting a lack of consensus about the techno-economic potential and respective merits of the various energy storage technologies. This is largely due to the wide spectrum of possible applications, evolving technology and unclear market conditions. In the context of the SwissStore project we studied energy storage in different contexts and from different perspectives, i.e.<br> • in households, in combination with photovoltaic panels (coupled with batteries and thermal storage), taking the perspective of the private investor<br> • in all buildings (all sectors, also with PV), with a focus on increased short-term flexibility, thereby also considering other strategies next to storage (e.g. demand-side response) and taking the perspective of the social planner.<br> In both cases, optimisation frameworks were developed for Switzerland and different scenarios were studied for the next decades.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Cost and performance data for electricity generation and storage technologies
<p>Here, we present a database which collates historical, current, and future cost and performance data and assumptions for the six most prominent electricity generation technologies; coal, gas, hydroelectric, nuclear, solar photovoltaic (PV) and wind power, which together accounted for over 92% of installed generation capacity in 2022. In addition, we provide the same data for utility-scale battery energy storage systems (BESS), regarded as critical to the integration of variable renewables such as wind and solar PV.</p> <p>The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 5510 datapoints from 56 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability in future.</p>
Material Intensity data for Renewable Energy Technologies
<p>The file contains detailed data on material intensities (measured in tons per gigawatt) collected from various literature sources. This data encompasses the material intensity of 30 metals through a range of technologies, including offshore and onshore wind power, solar energy, nuclear power, heat pumps, hydroelectric power, hydrogen production via electrolyzers, biomass energy, and biomass with carbon capture and storage (CCS).</p> <p>The variables are:</p> <ul> <li><strong>tech:</strong> Technology (<em>offshore_wind, onshore_wind, Solar, heat_pumps, hydrogen, biomass, biomassccs, hydroelectric, nuclear</em>).</li> <li><strong>material: </strong>Metal the material intensity refers to (<em>Aluminium, Arsenic, Bismuth, Boron, Cadmium, Chromium, Copper, Gallium, Germanium, Indium, Iron, Lead, Molybdenum, Nickel, Selenium, Silicon, Silver, Tellurium, Tin, Vanadium, Zinc, Dysprosium, Manganese, Neodymium, Niobium, Praseodymium, Terbium, Titanium, Iridium, Platinum</em>).</li> <li><strong>type: </strong>for Solar defines whether it is <em>roof_mounted</em> or <em>open_field.</em></li> <li><strong>m_int:</strong> The value of material intensity for the corresponding metal in the corresponding technology.</li> <li><strong>classification: </strong>specific classification for Solar, Wind and Heat pumps' technologies: e.g. for solar, defines the type of photovoltaic cell.</li> <li><strong>year: </strong>year in which it is assumed the data was collected, usually defined as the year of publication of the source paper.</li> </ul> <p>This dataset is part of the Master Thesis <em><span>Mining Industry and Energy Transition </span><span>Scenarios for Europe: Promoting a </span><span>Pluriversal Approach, </span></em><span>developed in the TISE (Transition, Innovation, and Sustainability Environments) program with collaboration with the Complexity Science Hub.</span></p>
The dataset of "Evaluation of Digital Supply Chain Technology's Impact on Sustainability Under the Moderate Effect of Supply Chain Dynamism: An Empirical Research in the Chinese Energy Supply Chain"
<p>This dataset involves the data from the questionnaire, which come from the project "Evaluation of Digital Supply Chain Technology’s Impact on Sustainability Under the Moderate Effect of Supply Chain Dynamism: An Empirical Research in the Chinese Energy Supply Chain". It comprises three dimensions questions, technology, sustainability and supply chain dynamism. The datas come from two Chinese energy firms, <span>China Resources Power Zhejiang Company and Hunan HuaDian Changsha Electric Co., Ltd.</span></p>
Data to calculate metal demand from IAM energy projections and add flexibility to the technological mix
<p>In the "Data" folder, you’ll find all necessary data to run the <strong>"IAM_metal_optimisation.py"</strong> script, available on GitHub (<a href="https://github.com/Pelotte/iam-metal-demand-optimizer/tree/master" target="_new" rel="noopener">https://github.com/Pelotte/iam-metal-demand-optimizer/tree/master</a>).</p> <p>The <strong>"IAM_metal_optimisation.py"</strong> script is a tool designed to:</p> <ol> <li>Quantify metal supply and demand by sector through 2050, using energy projections from various IAMs and SSP-RCP scenarios.</li> <li>Optimize the IAM technological mix, minimizing adjustments needed to prevent metal demand from exceeding supply constraints.</li> </ol> <p>This tool supports all IAMs with power projections for SSP-RCP scenarios (except SSP3) provided in the IPCC’s Sixth Assessment Report (AR6), Working Group III, and available in the IIASA database.</p> <p>Data for RCP 2.6, SSPs 1, 2, 4, 5, of the SSP marker IAM models of the IPCC, are organized in the "Capacity Factor IAM," "GDP IAM," and "Power Capacity IAM" folders. See the GitHub README for more details.</p>
Dataset for 'Heat and Humidity Exposure in Mega-cities - an Applied Tool for Energy and Water Harvesting Technologies'
<p>This is the dataset for the article named '<strong>Heat and Humidity Exposure in Mega-cities - an Applied Tool for Energy and Water Harvesting Technologies </strong>'</p>
Biochar Technologies at the Energy-Food nexus in sub-Saharan Africa: A unique window for Climate-Smart Agriculture
<p>Dataset to support the publication</p>
Dataset related to Research Article: Understanding the vicious cycle of myopic foresight and constrained technology deployment in transforming the European energy system
<p>Dataset related to research article:</p> <p><strong>Understanding the vicious cycle of myopic foresight and constrained technology deployment in transforming the European energy system</strong></p> <p>by Jacob Mannhardt, Paolo Gabrielli, Giovanni Sansavini (sansavig@ethz.ch)<br>Reliability and Risk Engineering Lab, Institute of Energy and Process Engineering, ETH Zurich</p> <p>All rights lie with the authors. All input data, source code, and result files needed to reproduce results and study. <br>Refer to README.docx for further information on content.</p>
Energy Balancing Modeling and Mobile Technology to Support e-Weight Loss
ClinicalTrials.gov study NCT02857595. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ScienceDex guides
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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)
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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.