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84 results for “energy efficiency”
The Role of Roofing Structure in Energy Efficiency: Strategies for a Greener Home
<p>In the pursuit of a greener and more sustainable lifestyle, energy efficiency plays a crucial role. Among the various components of a home, the roofing structure plays a significant part in reducing energy consumption and maximizing energy efficiency. A well-designed and properly installed roofing structure can contribute to improved insulation, reduced heat transfer, and optimized ventilation, resulting in a more energy-efficient home.</p> <p>In this article, we will explore the essential role of the roofing structure in energy efficiency and discuss strategies for creating a greener home. From choosing the right roofing materials to implementing insulation techniques and promoting proper ventilation, homeowners can take proactive steps to enhance their home's energy performance and minimize environmental impact. Let's delve into the key strategies for optimizing energy efficiency through a well-designed roofing structure.</p> <p>Roofing Materials and Insulation</p> <p>The choice of roofing materials and insulation is crucial in creating an energy-efficient <a href="https://seco.pk/service/roofing-structure/"><strong>roofing structure</strong></a>. Opting for materials with high thermal resistance, such as reflective roof coatings, cool roofs, or energy-efficient shingles, can help reduce heat absorption and minimize heat transfer into the living space. These materials reflect a significant portion of solar radiation, keeping the interior cooler during hot summers and reducing the reliance on air conditioning.</p> <p>Proper insulation is equally important in preventing heat loss during colder months. Installing insulation materials, such as foam insulation boards or fiberglass batts, beneath the roof can significantly improve thermal performance and reduce energy consumption for heating.</p> <p>Ventilation and Air Circulation</p> <p>Adequate ventilation and air circulation are essential for maintaining a comfortable indoor environment and optimizing energy efficiency. Roofing structures should incorporate ventilation systems that allow for the exchange of fresh air while removing excess heat and moisture. This helps regulate temperature and humidity levels, reducing the need for artificial cooling and dehumidification.</p> <p>Different ventilation strategies, such as ridge vents, soffit vents, or attic fans, can be employed to promote natural airflow and prevent the accumulation of hot air in the attic or roof space. Proper ventilation also helps prevent the formation of moisture-related issues, such as mold or rot, which can negatively impact both energy efficiency and indoor air quality.</p> <p>Solar Energy Harvesting</p> <p>Roofing structures can play a dual role by not only protecting the home but also serving as a platform for harnessing solar energy. Installing solar panels on the roof allows homeowners to generate clean and renewable energy, reducing reliance on traditional power sources and lowering utility bills. Solar energy systems can be integrated into the roofing structure, maximizing sun exposure and optimizing energy production.</p> <p>When considering solar energy harvesting, it's essential to ensure proper alignment, orientation, and shading analysis to maximize energy generation potential. Consulting with solar energy experts or roofing professionals can help determine the most efficient and effective solar panel placement on the roofing structure.</p> <p>Regular Maintenance and Inspections</p> <p>Regular maintenance and inspections are vital to preserving the integrity and energy efficiency of the roofing structure. Periodic roof inspections can identify potential leaks, damaged insulation, or deteriorating materials that may compromise energy performance. Timely repairs or replacements can prevent energy losses and maintain the efficiency of the roofing system.</p> <p>Additionally, keeping the roof clean from debris, leaves, or algae growth can contribute to better solar reflectance and prevent moisture retention, which can impact insulation properties. Regular maintenance, including gutter cleaning and roof surface treatments, ensures that the roofing structure functions optimally and maintains its energy-efficient characteristics.</p> <p>Conclusion</p> <p>A well-designed and properly maintained roofing structure plays a crucial role in optimizing energy efficiency in a home. By carefully selecting energy-efficient roofing materials, implementing insulation techniques, promoting proper ventilation, and considering solar energy harvesting, homeowners can create a greener and more sustainable living space.</p> <p>Investing in energy-efficient roofing structures not only reduces energy consumption and utility costs but also minimizes environmental impact. The strategies discussed in this article empower homeowners to take proactive steps towards creating an energy-efficient home, contributing to a greener future. By prioritizing energy efficiency in roofing structures, we can enhance the comfort, sustainability, and long-term value of our homes while making a positive impact on the planet.</p>
Data Set For Efficient Calculation of Dispersion Energy for Multireference Systems with Cholesky Decomposition. Application to Excited-state Interactions
<p>Data Set to Accompany:</p> <p>"Efficient Calculation of Dispersion Energy for Multireference Systems with Cholesky Decomposition. Application to Excited-state Interactions"</p>
Achieving Energy Efficiency with a Software Product Line Engineering Approach
<p>This is a Live PhD. Thesis Presentation; Also playable at:</p><p><a href="https://youtu.be/yaYEoj4S3pc">https://youtu.be/yaYEoj4S3pc</a></p><p>Please access and cite the published PhD. Thesis book: <a href="https://doi.org/10.5281/zenodo.10007921 ">https://doi.org/10.5281/zenodo.10007921 </a></p><ul><li><strong>Author</strong>: Daniel-Jesus Munoz</li><li><strong>Directors</strong>: Lidia Fuentes and Monica Pinto.</li></ul><p>CAOSD group, Universidad de Málaga, Andalucía Tech, Spain </p><p>Post-defense presentation recorded at Universität Ulm in 2023.</p><p>Energy-aware software design can energy-aware software design can reduce total energy consumption by 30-90%. However, the different ways of measuring energy consumption in real time are very complex. Energy readings are provided as the total energy consumption in joules or the rate of energy consumption in watts. For battery-powered battery-powered devices, joules per task is a more interesting metric, while watts per task is more commonly used for battery-powered devices. per task is more used for devices directly connected to power. The usual approach to modelling and storing approach to modelling and storing power consumption readings is to describe them as a characteristic of individual components, such as monetary cost. individual components, such as the monetary cost of a hardware component. However, the energy consumption values have many interactions between components, which makes it difficult to describe them with individual, static energy values. This makes it difficult to describe them in terms of individual, static energy values. Instead, we can store and energy information can be stored and populated in collaborative databases. The IEA and Datarade offer free databases with energy consumption data. free databases with energy consumption data for energy efficiency and sustainability analysis. Without However, the databases are not scalable for highly configurable systems because of the curse of the dimension. Our work focuses on Industry 4.0, specifically on Cyber-Physical Systems (acronym CPS), which are characterised by their high configurability and adaptability, presenting a large number of alternatives and a colossal number of alternatives and a colossal number of different systems in operation. This is known as the This is known as the search/solution space, the size of which is the set of all possible points that satisfy an optimisation problem. optimisation problem. Partially known solution spaces are a common problem in many fields, such as computer engineering, machine learning, artificial intelligence and goal-oriented optimisation. engineering, machine learning, artificial intelligence and goal-oriented optimisation. The Constraint Satisfaction Problems (CSP) are mathematical problems defined as a set of objects whose state must satisfy a set of constraints. set of objects whose state must satisfy a set of constraints. Variability Models (acronym VMs) are tree-like structures used to represent the commonalities and differences of a CPS. Numerical Features (NFs) can be used in VMs to represent quantitative properties of the system, but they can be used to represent quantitative properties of the system, but most tools do not support NFs. In addition, NFs increase the size of the VM, NFs increase the size of the solution space by multiplying it by its domain size, which makes large solution spaces colossal. large solution spaces into colossal ones. Quality Models (QMs) are tree structures that are used to determine which Quality Attributes (QAs) such as energy efficiency are to be taken into account when evaluating a project. which Quality Attributes (QAs) such as energy efficiency are to be taken into account when evaluating a system. system. ISO/IEC 25010 is the most popular QM formalisation, which groups QAs into eight different types. The Automated reasoning is the automation of formal logical reasoning to compute different types of information about system models. Examples are providing a VM or QM to a reasoning tool, and calculating the size of the reasoning tool, and calculating the size of the solution space, checking the satisfiability of the model, or generating only optimal systems based on objective functions. only optimal systems based on objective functions based on one or more QAs. This thesis aims to find a native modelling and reasoning approach for a unified Quality and Variability Model (QVM). Variability and Quality Model (QVM). The aim is to develop an approach that supports modelling and reasoning for the reasoning oriented optimisation of numerical characteristics, an algebraic framework for unified QVMs, an online eco-assistant for optimising a user-constrained solution space measured for quality, and an algorithm and an online quality, and an algorithm and a web tool for learning the influences of energy and characteristics of user-constrained, domain-unknown and partially measured solution spaces.</p>
Data from: Energy efficient homes for rodent control across cityscapes
Open the record for dataset details and reuse information.
Data set on Energy Efficiency potentials on top of reference scenarios
<p>As it is not possible to include the entire dataset in this report, we only include the Energy Efficiency potentials for Austria. The full dataset, showing the savings potentials for all EU27 (and UK) countries, is available upon request to the project coordinator.</p>
Identifying Energy Efficiency Patterns in Sorting Algorithms via Abstract Syntax Tree Mining
<p>Replication package for Submission "Identifying Energy Efficiency Patterns in Sorting Algorithms via Abstract Syntax Tree Mining".</p> <p>Authors kept anonymous for review.</p>
Accurate and efficient representation of intramolecular energy in ab initio generation of crystal structures. Part I: Adaptive local approximate models
<p>The global search stage of Crystal Structure Prediction (CSP) methods requires a fine balance between accuracy and computational cost, particularly for the study of large flexible molecules. A major improvement in the accuracy and cost of the intramolecular energy function used in the CrystalPredictor II (Habgood, M., Sugden, I. J., Kazantsev, A. V., Adjiman, C. S. & Pantelides, C. C. (2015).<em> J Chem Theory Comput</em> <strong>11</strong>, 1957-1969) program is presented, where the most efficient use of computational effort is ensured via the use of adaptive Local Approximate Model (LAM) placement. The entire search space of relevant molecule’s conformations is initially evaluated using a coarse, low accuracy grid. Additional LAM points are then placed at appropriate points determined via an automated process, aiming to minimise the computational effort expended in high energy regions whilst maximising the accuracy in low energy regions. As the size, complexity, and flexibility of molecules increase, the reduction in computational cost becomes marked. This improvement is illustrated with energy calculations for benzoic acid and the ROY molecule, and a CSP study of molecule XXVI from the sixth blind test (Reilly <em>et al.</em>, (2016).<em> Acta Cryst. B, accepted</em>.), which is challenging due its size and flexibility. Its known experimental form is successfully predicted as the global minimum. The computational cost of the study is tractable without the need to make unphysical simplifying assumptions. </p>
Sensor solutions for an energy-efficient and user-centered heating system
<p>Corresponding dataset for the article "Sensor solutions for an energy-efficient and user-centered heating system" published in the Journal of Sensors and Sensor Systems Special Issue "Sensors and Measurement Systems 2016".</p>
Putting a new `spin' on energy information: Measuring the impact of reframing energy efficiency information on tumble dryer choices in a multi-country experiment.
<p>Public open-access repository for the data used within the paper titled "Putting a new `spin' on energy information: Measuring the impact of reframing energy efficiency information on tumble dryer choices in a multi-country experiment".</p>
EfficientBioAI: Making Bioimaging AI Models Efficient in Energy and Latency
<p>This dataset contains trained deep learning models, dataset and experiment files for the manuscript "EfficientBioAI: Making Bioimaging AI Models Efficient in Energy and Latency". Please find the software and more information including tutorials here: <a href="https://github.com/MMV-Lab/EfficientBioAI">MMV-Lab/EfficientBioAI (github.com)</a>.</p>
Supplemental data for the report "Optimisation of lattice simulations energy efficiency"
<p>Supplemental data for the report <a href="http://doi.org/10.5281/zenodo.7057319">"Optimisation of lattice simulations energy efficiency"</a>. Also available as a <a href="https://git.dev.dirac.ed.ac.uk/portelli/tursa-energy-efficiency">git repository</a>.</p> <p>It contains:</p> <ul> <li>Full copy of benchmark run directories</li> <li>Power monitoring scripts</li> <li>Power monitoring raw measurements</li> <li>Power monitoring data analysis and results used in the report</li> </ul> <p>For a more complete description, please see the README.md file.</p>
Machine Learning Guided AQFEP: A Fast & Efficient Absolute Free Energy Perturbation Solution for Virtual Screening
<p>Data to reproduce primary figures in the manuscript titled: Machine Learning Guided AQFEP: A Fast & Efficient Absolute Free Energy Perturbation Solution for Virtual Screening.</p> <p>URL: https://chemrxiv.org/engage/chemrxiv/article-details/6583785e66c1381729ac86f5</p>
The ESCAPE project: Energy-efficient Scalable Algorithms for Weather Prediction at Exascale
<p>Data and figures presented in the paper "The ESCAPE project: Energy-efficient scalable algorithms for weather prediction at exascale". The discussion paper is available at: https://doi.org/10.5194/gmd-2018-304</p>
ERL-118550 Data: Rebates and Grid Decarbonization from the Inflation Reduction Act Promote Equitable Adoption of Energy Efficiency Retrofits
<p>The authors have self-reported an issue in how they used RSMeans 2019 City Cost Index (CCI) data to adjust for regional cost differences. The publicly available webpage stated these data could be used to “adjust for cost differences when compared to the national average, show cost differences between cities, compare cost differences between quarters of the same year, or adjust costs to Canadian cities.” However, the RSMeans Data and Engineering Department later clarified that these values “were intended to show how much CCI values changed for each city at the start of 2019 compared to the values in our 2019 book.” Nevertheless, our capital cost estimates closely align with several peer-reviewed studies and publicly available data sources. Based on our review, we do not believe our method significantly affected the study’s overall findings or conclusions. Further discussion is provided in the manuscript’s Limitations section and Appendix S5 of the Supplementary Materials.</p> <p>Peer-reviewed article available here: https://iopscience.iop.org/article/10.1088/1748-9326/adb765</p> <p>Article DOI: 10.1088/1748-9326/adb765</p>
Dataset for publication Harnessing Ti3C2-WS2 Nanostructures as Efficient Energy Scaffoldings for Photocatalytic Hydrogen Generation
<p>The dataset contains all relevant data and figures regarding the manuscript "Harnessing Ti3C2-WS2 Nanostructures as Efficient Energy Scaffoldings for Photocatalytic Hydrogen Generation".</p> <p>All Figures are in tiff format and all relevant data are in csv formats. </p> <p>The data in csv format are labelled as specified in the corresping images (e.g. Figure 1a csv file corresponds to data used to plot graphs from Figure 1a etc.). </p> <p>Axis labeling and units are always specified at the beginning of individual columns. If more than one curve was plotted from the csv file, the conditions can also be found at the beginning of corresponding columns.</p>
Data to estimate energy efficiency and environmental friendliness of Ukrainian GDP from indicators of structural, economic, and social development in Ukraine
<p><span>Data to estimate regression dependences of energy efficiency and environmental friendliness of GDP from indicators of structural, economic, and social development in Ukraine </span></p>
Dataset supporting publication: 'GEOFIT: Ground source heat pump systems for energy efficient building retrofitting'
<p>Dataset supporting publication: Presentation of GEOFIT at the <a href="https://www.hp-summit.de/en">European Heat Pump Summit</a> (2019) </p> <p>Available for download: <a href="https://zenodo.org/record/3901527">GEOFIT Zenodo</a></p> <p>The integration of geothermal systems for heating / cooling solutions in conjunction with heat pump technology is a major challenge, particularly in the case of renovation. In order to find the best solution for the increased flow temperatures compared to a new building for the renovation, various heat pump configurations are evaluated according to energy and economic criteria. A refrigerant with low greenhouse gas potential (GWP) is used as the working medium, e.g. R1234ze (E)) less than 10 used. A favorable refrigeration circuit configuration for high flow temperatures is a twin-circuit system, which essentially consists of two heat pumps with different condensing temperatures and the same evaporation temperatures. An alternative to this is a single-stage configuration with a significantly larger condenser for improved supercooling. Both systems should enable efficient operation when renovating buildings.</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>
A three-year building operational performance dataset for informing energy efficiency
Open the record for dataset details and reuse information.
Energy Efficiency Survey: Twin Cities Household Ecosystem Project
We designed our methods to estimate carbon, nitrogen, and phosphorus fluxes through individual households, and to address two primary questions: 1. How are these fluxes distributed across households? 2. What biophysical and socioeconomic factors contribute to differences in these fluxes across households? Our hybrid approach combines: 1. A mailed survey 2. Energy provider records 3. On-the-ground landscape measurements 4. A computational tool (the Household Flux Calculator) 4. Parcel data (interpreted using GIS) The resulting dataset includes information on biophysical and socioeconomic variables that potentially influence household-level fluxes of elements. Using this method to study element fluxes at the household level allows us to explicitly link consumption choices and element fluxes.
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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)
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.