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21 results for “energy infrastructure”
Dataset of IoT-Based Energy and Environmental Parameters in a Smart Building Infrastructure
<p>This dataset includes detailed measurements from IoT sensors deployed throughout the M5 building, capturing energy consumption from various devices like coffee machines, microwaves, etc., as well as environmental data such as temperature, humidity, and occupancy in key areas like the Interdisciplinary lab, kitchen, and mailroom.</p> <p>This release aims to provide researchers and practitioners with comprehensive data to facilitate research on energy efficiency and environmental monitoring within smart building infrastructures. The data are structured to support various types of analysis, from operational efficiency assessments to environmental impact studies.</p> <p>For detailed information on the dataset's structure and usage, please refer to the README.md file included in this repository.</p>
Zambezi dataset to "WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the Water-Energy-Food-Climate Nexus"
<p>This is the dataset used in the HESS publication "<a href="https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-167/">WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the Water-Energy-Food-Climate Nexus</a>"</p> <p>The dataset describes the water-energy-food nexus of the Zambezi River Basin used as input to the <a href="https://github.com/RaphaelPB/WHAT-IF">WHAT-IF model</a>.</p> <p>The file Data_Organization.pdf, summarizes the available data. For more info look at the <a href="https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-167/">publication</a> and/or <a href="https://github.com/RaphaelPB/WHAT-IF">Github</a>.</p>
Will it float? Exploring the social feasibility of floating solar energy infrastructure in the Netherlands
<p>Floating photovoltaic (FPV) is emerging as a promising renewable energy concept in which solar panels are installed on floating infrastructure to enable the production of renewable energy on water. While the body of knowledge on technical, financial and environmental aspects is expanding steadily, so far the societal implications of FPV remain largely unstudied. Here, we investigate public attitudes to a FPV pilot project at the Oostvoornse lake, the Netherlands. We conducted interviews with stakeholders to explore how the local community with high interest and involvement in the lake perceives the pilot project. Thereupon, we conducted a field survey with recreational users of the lake and carried out a random forest regression analysis to examine what factors shape recreationists’ support or opposition. Interview results show that the diversity of stakeholders and their diverging use of the Oostvoornse lake leads to a broad variety of concerns about how the pilot project could affect their activities and interests. Particularly the uncertainty on possible impacts due to the newness of FPV was a reason for stakeholders to take a reluctant stance towards the pilot. In contrast, our quantitative results show that recreationists were highly supportive of the project, mainly due to their positive attitudes towards local authorities and the broader societal benefits the pilot project is perceived to generate. Landscape alteration was identified to be by far the most important objection, which indicates that negative implications from a recreation perspective could be largely accommodated through appropriate siting decisions or other measures that mitigate visibility.</p>
What energy infrastructure to support 1.5°C scenarios? - Scenario data
<p>This excel file summarizes the main assumptions considered for the analysis " What energy infrastructure to support 1.5°C scenarios?", prepared by Artelys on behalf of the European Climate Foundation in 2020. The report is available here: https://www.artelys.com/wp-content/uploads/2020/12/Artelys-2050EnergyInfrastructureNeeds.pdf</p>
Energy Savings Related to Green Infrastructure in Valladolid city
<p>Improved green infrastructure results in energy savings through a variety of mechanisms. These include:</p> <ul> <li>Reducing the need to heat buildings by insulating them against the cold</li> <li>Reducing the need to cool buildings by insulating them against the heat</li> <li>Reducing the volume of stormwater entering the sewer system, thus reducing energy consumption in sewage processing</li> </ul> <p>The energy savings resulting from these three mechanisms are estimated by GI-Val <a href="#_ftn1">[</a><a href="https://www.merseyforest.org.uk/gi-val/">https://www.merseyforest.org.uk/gi-val/</a><a href="#_ftn1">]</a> tools 1.1, 1.5 and 2.1 respectively. </p>
Dataset for the paper Congestion management via increasing integration of electric and thermal energy infrastructures
<p>Accompanying dataset for the paper <em>Congestion management via increasing integration of electric and thermal energy infrastructures</em></p>
Datasets for the study "Hydro-economic analysis of infrastructure investment options within the Water-Energy-Food nexus in the Niger River basin"
<p>This is the dataset used in the study "Hydro-economic analysis of infrastructure investment options within the Water-Energy-Food nexus in the Niger River basin". This study has been conducted within the Department of Environmental Engineering at the Technical University of Denmark as a Master Thesis project. </p> <p>The dataset describes the water-energy-food nexus of the Niger River basin used as input to the open source single objective hydro-economic optimization model WHAT-IF. Note that the Energy Module is partly filled, but has not been used in the study (except the hydropower sheet).</p> <p>More information on data organization <a href="https://zenodo.org/record/2646476#.XxqORudS93g">here</a>.</p> <p>The model is documented in this <a href="https://hess.copernicus.org/articles/23/4129/2019/hess-23-4129-2019-discussion.html">publication</a>.</p> <p>The code is available on <a href="https://github.com/RaphaelPB/WHAT-IF">Github</a>.</p>
Climate Energy Dataset For Off Grid Electricity Infrastructure
<p>The dataset comprises real-time electrical measurements—including voltages, currents, and power factors—for three-phase and single-phase systems across generation, distribution, and consumption stages showcasing the energy generation and demand within an off-grid electricity infrastructure located in the Kalam Region, a specific climate zone in Pakistan. Collected every five minutes from March 6, 2023, to October 24, 2024, it includes over 45 million instances covering data from four micro-hydropower generators, 26 transformers, and 585 end-users. Additionally, the dataset incorporates climate data—such as temperature, dew point, wind components, precipitation, snowfall, and snow cover—from the ERA5 dataset. This comprehensive collection enhances its utility for research in energy systems analysis, climate change studies, electrical engineering, and artificial intelligence applications.</p> <p>This dataset was originally created with support from Lacuna Fund, the world’s first collaborative effort to provide data scientists, researchers, and social entrepreneurs in low- and middle-income contexts globally with the resources they need to produce labeled datasets that address urgent problems in their communities. Lacuna Fund is a funder collaborative that includes The Rockefeller Foundation, Google.org, Canada’s International Development Research Centre, the German Federal Ministry for Economic Cooperation and Development (BMZ) with GIZ as implementing agency, Wellcome Trust, Gordon and Betty Moore Foundation, Patrick J. McGovern Foundation, and The Robert Wood Johnson Foundation. See https://lacunafund.org/about/ for more information.</p>
Hotspots in the grid: Avian sensitivity and vulnerability to collision risk from energy infrastructure interactions in Europe and North Africa
<p>Wind turbines and power lines can cause bird mortality due to collision or electrocution. The biodiversity impacts of energy infrastructure (EI) can be minimised through effective landscape-scale planning and mitigation. The identification of high-vulnerability areas is urgently needed to assess potential cumulative impacts of EI while supporting the transition to zero-carbon energy.</p> <p>We collected GPS location data from 1,454 birds from 27 species susceptible to collision within Europe and North Africa and identified areas where tracked birds are most at risk of colliding with existing EI. Sensitivity to EI development was estimated for wind turbines and power lines by calculating the proportion of GPS flight locations at heights where birds were at risk of collision and accounting for species' specific susceptibility to collision. We mapped the maximum collision sensitivity value obtained across all species, in each 5x5 km grid cell, across Europe and North Africa. Vulnerability to collision was obtained by overlaying the sensitivity surfaces with density of wind turbines and transmission power lines.</p> <p>Results: Exposure to risk varied across the 27 species, with some species flying consistently at heights where they risk collision. For areas with sufficient tracking data within Europe and North Africa, 13.6% of the area was classified as high sensitivity to wind turbines and 9.4% was classified as high sensitivity to transmission power lines. Sensitive areas were concentrated within important migratory corridors and along coastlines. Hotspots of vulnerability to collision with wind turbines and transmission power lines (2018 data) were scattered across the study region with highest concentrations occurring in central Europe, near the strait of Gibraltar and the Bosporus in Turkey.</p> <p>Synthesis and Applications: We identify the areas of Europe and North Africa that are most sensitive for the specific populations of birds for which sufficient GPS tracking data at high spatial resolution were available. We also map vulnerability hotspots where mitigation at existing EI should be prioritised to reduce collision risks. As tracking data availability improves our method could be applied to more species and areas to help reduce bird-EI conflicts.</p>
Creating Comparability among European Neighbourhoods to Enable the Transition of District Energy Infrastructures towards Positive Energy Districts
<p>The aim of the this raster file is to allow comparability of districts across Europe, to support energy transformation in urban areas. Comparability will allow to transfer knowledge between district energy renovation projects. This dataset combines different raster files from the <a href="https://gitlab.com/hotmaps">Hotmaps project</a>.<br> The dataset combines data from <a href="https://gitlab.com/hotmaps/heat/heat_res_curr_density/-/tree/master">Heat density</a>, <a href="https://gitlab.com/hotmaps/heat/heat_res_curr_density/-/tree/master">Total Gross Floor Area</a>, <a href="https://gitlab.com/hotmaps/gfa_res_curr_density">Residential Gross Floor Area</a>. These three dataset act as a proxy to Heat Demand, Floor Space Index and Share of Residential Gross Floor Area. Further description can be found in the paper "Creating comparability among European neighbourhoods to enable district energy infrastructure towards positive energy districts".</p>
Business load profiles used in "Maximising the benefits of renewable energy infrastructure in displacement settings: Optimising the operation of a solar-hybrid mini-grid for institutional and business users in Mahama Refugee Camp, Rwanda"
<p>Version used in the submission of "Maximising the benefits of renewable energy infrastructure in displacement settings: Optimising the operation of a solar-hybrid mini-grid for institutional and business users in Mahama Refugee Camp, Rwanda" by Hamish Beath, Javier Baranda Alonso, Richard Mori, Ajay Gambhir, Jenny Nelson and Philip Sandwell.</p>
Energy consumption, execution time and fail requests rate of a proactive energy-aware auto-scaling solution for edge-based infrastructures applied to real-world workload.
<p>Spreadsheet of the results obtained with our horizontal auto-scaling proposal presented in "A proactive energy-aware auto-scaling solution for edge-based infrastructures". In that research, we present a proactive horizontal auto-scaling framework for edge infrastructures, which considers both the base (idle) and dynamic (due to application execution) energy consumption of edge nodes and the node scaling mechanism. Simulations were performed with the EdgeCloudSim simulator with a workload provided by Shanghai Telecom and the results show up to a 92.5% decrease in energy consumption, a failed request rate of up to 0%, and reasonable execution times of the auto-scaling process for different problem sizes.</p> <p>Proactive auto-scaling mechanisms in edge-based infrastructures can anticipate user service requests by allocating computing resources while supporting the quality of service needed by a vast range of applications requiring, e.g., a low latency or response time. </p> <p>This work is supported by the European Union's H2020 research and innovation program under grant agreement DAEMON 101017109 and by the projects co-financed by FEDER funds LEIA UMA18-FEDERJA-15, MEDEA RTI2018-099213-B-I00 (MCI/AEI) and RHEA P18-FR-1081.</p>
HADES: An NFV solution for energy-efficient placement and resource allocation in heterogeneous infrastructures. Study dataset
<p>The publication and research associated with this dataset are currently under review in the Journal of Network and Computer Applications.</p> <p>In that research, we present HADES, an NFV solution for energy-efficient placement and resource allocation in heterogeneous infrastructures. HADES is an OSM (Open Source MANO) extension that allows the configuration of virtual network functions and their subsequent resource allocation and deployment at the edge, minimizing energy consumption and ensuring the quality of service.</p> <p>This dataset contains the following:</p> <ul> <li>1 CSV file with execution time results of the iTAREA module for (60) different problem sizes and (3) execution environments.</li> <li>1 CSV file with energy consumption results of HADES deployments and other 5 assignment policies: First-fit, Random-fit, Fastest-fit, Best-fit, and kube-scheduler (the Kubernetes’ assignment policy).</li> </ul> <p>This work is supported by the European Union's H2020 research and innovation programme under grant agreement DAEMON 101017109 and by the projects co-financed by FEDER funds LEIA UMA18-FEDERJA-15 and IRIS PID2021-122812OB-I00 (MCI/AEI).</p>
Field Application of a High-Power Density Electromagnetic Energy Harvester to Power Wireless Sensors in Transportation Infrastructures
<p>Traffic-induced vibration of transportation infrastructures is a reliable source of kinetic energy, which can be harvested to power conventional monitoring sensors and peripherals installed on bridges, thereby reducing some dependence on non-renewable energy. The highway statistics shows that the average daily vehicles miles travelled in the US is more than 5 billion. This is a massive source of kinetic energy that lies unused in the national transportation network. This study focuses on the design and field testing of a high-power density electromagnetic energy harvester (EMEH) to convert such a kinetic energy into electrical energy for powering ubiquitous sensors installed on transportation infrastructures. The principal investigators have been investigating the design of the EMEH using analytical and finite element simulations, as well as, its laboratory prototype fabrication and testing in the first phase. The proposed EMEH utilizes the innovative concept of creating planar array of large number of small permanent magnets through certain optimization criteria to achieve strong and focused magnetic field in a particular orientation. The proposed EMEH has a compact design, such that it can be integrated into the power circuit of wireless sensor nodes (WSNs) and installed at suitable part of a transportation infrastructure without elaborate wiring. It is capable of continuously charging the rechargeable battery of a WSN, thereby extending the lifespan of the monitoring system, almost, indefinitely. For the next phase of this research, the principal investigators propose the development and field implementation of a larger scale and more compact version of the EMEH with a minimum of 500 mW output power to be installed on selected transportation infrastructures for the evaluation of its energy harvesting efficiency and capability to derive different types of monitoring sensors and peripherals. Three different highway bridges with different fundamental frequencies, ideally between 2Hz to 8Hz, will be selected for the field testing of the EMEH. An acceleration sensor will be used to record the traffic-induced vibration of each bridge during a normal daily traffic that after signal processing is used to measure the fundamental frequency of that bridge. The dynamic characteristics of the proposed EMEH (i.e. tip mass and spring stiffness) will be modified to put it into a resonant condition with the bridge by matching their natural frequencies. The output power will be monitored and used to continuously charge a rechargeable battery powering a wireless sensor. The focus is on the feasibility of the proposed EMEH to power sensors that are used to regularly monitor the structural integrity of materials and components of highway bridges such as acceleration and temperature sensors.</p>
A meta-analysis investigating the effects of energy infrastructure proximity on grouse demography and space use
<p>The increased global demand for energy will require additional tools to help guide policy and management actions to conserve wildlife. Grouse (Tetraoninae) are adversely affected by infrastructure associated with energy development, but the magnitude of effects are difficult to quantify in a singular management prescription. Advancement in monitoring and analysis techniques have allowed researchers to evaluate complex questions surrounding the effects of infrastructure on grouse populations, rapidly increasing our knowledge. To better inform management decisions, especially with the emergence of renewable energy, a quantitative synthesis of previous research evaluating the effects of infrastructure on grouse populations is needed. We reviewed studies evaluating the effect of energy infrastructure on grouse, with the main objective to determine the magnitude of effect on grouse lek attendance, resource selection, and survival to help inform future conservation actions. We modeled slope coefficients for distance to energy infrastructure, standardized by scale, on various behaviors to determine overall effect sizes in a meta-analysis. We used 93 study-result combinations from 21 studies that directly evaluated resource selection, survival, or lek attendance relative to energy infrastructure. Trends in overall effect sizes suggest an adverse effect of distance to energy infrastructure on grouse behavior; however, the combination of non-significant pooled regression slopes and high among-study heterogeneity suggest the effect of distance to energy infrastructure is context dependent. While distance to infrastructure is a common metric used in many grouse management plans, our results suggest distance to infrastructure may not be a reliable predictor of grouse behavior and the effect is context dependent making management prescriptions based solely on distance to infrastructure in a one size fits all approach difficult. Our analysis points to numerous aspects that scientists can improve upon by evaluating density in conjunction with distance to energy infrastructure as well as reporting the necessary statistics for future meta-analyses.</p>
A meta-analysis investigating the effects of energy infrastructure proximity on grouse demography and space use
Open the record for dataset details and reuse information.
Hotspots in the grid: Avian sensitivity and vulnerability to collision risk from energy infrastructure interactions in Europe and North Africa
Open the record for dataset details and reuse information.
European power system infrastructure in the open energy system model PyPSA-Eur
<p>The image is created using the data and scripts in the European open energy system model <a href="https://github.com/PyPSA/pypsa-eur">PyPSA-Eur.</a></p>
Energy Harvesting for Self-Powered Sensors for Smart Transportation Infrastructure
<p>This research aims to develop an innovative approach for energy harvesting from transportation infrastructures and demonstrate the feasibility of the approach through laboratory testing and field demonstration. The proposed innovative approach of energy harvesting, termed as electromagnetic energy harvesting system (EMEHSs), can be used to power wireless sensors commonly used for health monitoring of bridges. This EMEHS has expected to be simple, but effective in harvesting kinetic energy and converting it to electric power for wireless sensors. Practical and economic feasibility and field implementation of the device on a bridge will also be investigated in this work. Based on detailed numerical simulations and modeling, a larger-scale device will be first tested in the laboratory and then will be installed on a bridge to demonstrate the technology and its effectiveness in powering typical health monitoring sensors.</p>
Modules for Experiments in Stellar Astrophysics (MESA): Time-Dependent Convection, Energy Conservation, Automatic Differentiation, and Infrastructure
<p>We update the capabilities of the open-knowledge software instrument Modules for Experiments in Stellar Astrophysics (MESA). The new auto_diff module implements automatic differentiation in MESA, an enabling capability that alleviates the need for hard-coded analytic expressions or finite difference approximations. We significantly enhance the treatment of the growth and decay of convection in MESA with a new model for time-dependent convection, which is particularly important during late-stage nuclear burning in massive stars and electron degenerate ignition events. We strengthen MESA's implementation of the equation of state, and we quantify continued improvements to energy accounting and solver accuracy through a discussion of different energy equation features and enhancements. To improve the modeling of stars in MESA we describe key updates to the treatment of stellar atmospheres, molecular opacities, Compton opacities, conductive opacities, element diffusion coefficients, and nuclear reaction rates. We introduce treatments of starspots, an important consideration for low-mass stars, and modifications for superadiabatic convection in radiation-dominated regions. We describe new approaches for increasing the efficiency of calculating monochromatic opacities and radiative levitation, and for increasing the efficiency of evolving the late stages of massive stars with a new operator split nuclear burning mode. We close by discussing major updates to MESA's software infrastructure that enhance source code development and community engagement.</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.