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64 results for “energy optimization”
Production line dataset for task scheduling and energy optimization - Demand Response Participation
<p>Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> it was simulated an announcement of a demand response program at period 757, describing a demand response event from period 937 (Friday at 21:00h) to 960 (Friday at 23:00h) , where each period represents five minutes. The demand response program imposed a limit consumption, during its event, of 2.5 kWh. The announcement of the demand response allowed the use of the proposed solution to limit the energy consumption. For that, the algorithm described in section 3.3 was executed at period 769 (Friday at 7:00h).</p> <p>The API can be found at <<a href="http://www.gecad.isep.ipp.pt/api/spear/%3E">http://www.gecad.isep.ipp.pt/api/spear/</a>></p> <p>File Description:</p> <ul> <li>Input_JSON_Demand_Response_Optimization - JSON input data for the demand response participation</li> <li>Output_JSON_Demand_Response_Optimization - JSON output data for the demand response participation</li> <li>Output_Statistics_Demand_Response_Optimization - Excel output demand response participation statistics</li> <li>Comparison_Output_Statistics_Demand_Response - Excel output statistics comparing the before and after the demand response participation</li> </ul>
Production line dataset for task scheduling and energy optimization - Schedule Optimization
<p>The case study of this dataset uses real production data, provided by a textile company that manufactures hang tags. Their working schedule is from 7h00 of Monday to 23h00 of Saturday. This dataset uses a period of 5 minutes for all task durations and energy data. The case study considers a six-day period from 7h00 of Monday to 23h00 of Saturday. The scheduling algorithm was used for three machines that share the same cell.<br> <br> The API can be found at <http://www.gecad.isep.ipp.pt/api/spear/><br> <br> File Description:</p> <ul> <li>Input_JSON_Schedule_Optimization - JSON input data for the schedule optimization</li> <li>Output_JSON_Schedule_Optimization - JSON output data for the schedule optimization</li> <li>Output_Statistics_Schedule_Optimization - Excel output schedule optimization statistics</li> </ul>
Docked structures from "Optimizing active learning for free energy calculations"
<p>This archive contains the docked TYK2 structures used in the paper "Optimizing active learning for free energy calculations" (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ailsci.2022.100050" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.ailsci.2022.100050</span></span></a>). AM1-BCC charges are stored in the field "AM1Cache" in the SD file. The charges can be extracted using the code sample below. </p> <p> </p> <pre><code>from rdkit import Chem import base64 import pickle suppl = Chem.SDMolSupplier("10k_most_similar_tyk2_charged.sdf", removeHs=False) for mol in suppl: am1 = mol.GetProp("AM1Cache") am1_charges = pickle.loads(base64.b64decode(mol.GetProp("AM1Cache"))) assert len(am1_charges) == mol.GetNumAtoms(), "Charge cache has different number of charges than mol atoms"</code></pre>
Optimized structures of the stationary points on the potential energy surface of the OH(2Π) + C2H4 reaction
<p>This Zip file contains the cartesian coordinates of optimized stationary points of the OH(<sup>2</sup>Π) + C<sub>2</sub>H<sub>4</sub> potential energy surface published in our article “OH(<sup>2</sup>Π) + C<sub>2</sub>H<sub>4</sub> Reaction: A Combined Crossed Molecular Beam and Theoretical Study” (P<em>hys. Chem. A</em> 2023, 127, 21, 4609–4623), that can be found in <a href="https://doi.org/10.1021/acs.jpca.2c08662">https://doi.org/10.1021/acs.jpca.2c08662</a>.</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.</p>
Optimized structures of the stationary points on the potential energy surface of the O(3P, 1D) + HCCCN(X1Σ+) reaction
<p>This Zip file contains the cartesian coordinates of optimized stationary points of the O(<sup>3</sup>P, <sup>1</sup>D) + HCCCN(X<sup>1</sup>Σ<sup>+</sup>) potential energy surface published in our article “Reactions O(<sup>3</sup>P, <sup>1</sup>D) + HCCCN(X<sup>1</sup>Σ<sup>+</sup>) (Cyanoacetylene): Crossed-Beam and Theoretical Studies and Implications for the Chemistry of Extraterrestrial Environments” (<em>J. Phys. Chem. A</em> 2023, 127, 3, 685–703), that can be found in <a href="https://doi.org/10.1021/acs.jpca.2c07708">https://doi.org/10.1021/acs.jpca.2c07708</a>.</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.</p>
Optimized structures of the stationary points on the potential energy surface of the dissociation of the CH3OH˙+ cation
<p>This Zip file contains the optimized stationary points structures of the potential energy surface (PES) for the dissociation of the CH3OH˙+ cation.</p> <p>The PES has been published in our paper “Fragmentation of interstellar methanol by collisions with He˙<sup>+</sup>: an experimental and computational study” (<em><strong>Phys. Chem. Chem. Phys.</strong></em>, 2022, <strong>24</strong>, 22437-22452), that can be found in https://doi.org/10.1039/D2CP02458F .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01 and the structures were optimized at ωB97X-D/aug-cc-pVTZ level of theory.</p>
Dataset of multi-objective optimization results for a new latent energy storage approach in buildings based on several phase change materials with different melting temperatures
<p>This dataset comprises the multi-objective optimization results obtained for a new latent energy storage approach based on several phase change materials (PCMs) with different melting temperatures in buildings. The results were obtained for a small office building in eight climate-representative locations according to the ASHRAE 169-2020 climate classification and within the WMO Region VI (Europe).</p> <p>The dataset contains:</p> <p>- The EnergyPlus baseline models employed as a case study for each climate.</p> <p>- The Pareto fronts obtained after the multi-objective optimization in each climate.</p> <p>- The EnergyPlus models for the best designs achieved on the Pareto fronts in terms of annual total load reductions.</p>
Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Energy Cost Optimization with Energy Selling
<p>Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> an energy cost optimization considering the presence of an energy buyer is proposed to validate the scheduler’s ability to maximize profits while also minimizing energy costs. The scenario considers an added sales value corresponding to 50% of the buying. For this scenario, the genetic algorithm was executed for 2 hours, with 1 and 0 for the optimization weights total cost and machine occupancy deviation, respectively.</p> <p> </p> <p>File Description:</p> <ul> <li>Input_JSON_Energy_Cost_Energy_Selling_Optimization - JSON input data for the energy cost optimization with energy selling</li> <li>Output_JSON_Energy_Cost_Energy_Selling_Optimization - JSON output data for the energy cost optimization with energy selling</li> <li>Output_Statistics_Energy_Cost_Energy_Selling_Optimization - Excel output energy cost optimization with energy selling statistics</li> </ul>
Optimized stationary points on the potential energy surface of the reaction of atomic oxygen O(3P) with acrylonitrile
<p>This Zip file contains the cartesian coordinates of optimized stationary points of the O(<sup>3</sup>P) + acrylonitrile potential energy surface (PES).</p> <p>The PES has been published in our article “A Computational Analysis of the Reaction of Atomic Oxygen O(<sup>3</sup>P) with Acrylonitrile”</p> <p>(<em>Lecture Notes in Computer Science</em> <strong>2021</strong>, 12958, 339-350), that can be found in https://doi.org/10.1007/978-3-030-87016-4_25 .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.</p>
Optimized stationary points on the potential energy surfaces of the N(2D) + CH2CHCN and CN + CH2CHCN reactions
<p>This Zip file contains the cartesian coordinates of optimized stationary points on the potential energy surfaces (PESs) of two reactions: N(<sup>2</sup>D) + CH<sub>2</sub>CHCN (acrylonitrile) and CN + CH<sub>2</sub>CHCN.</p> <p>The PES has been published in our article “A Theoretical Investigation of the Reactions of N(<sup>2</sup>D) and CN with Acrylonitrile and Implications for the Prebiotic Chemistry of Titan”</p> <p>(<em>Lecture Notes in Computer Science</em> <strong>2022</strong>, 13378, 246-259), that can be found in https://doi.org/10.1007/978-3-031-10562-3_18 .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.</p>
Optimized stationary points on the potential energy surfaces of the N(2D)+ C2H4 and N(2D)+ CH2CHCN reactions
<p>This Zip file contains the cartesian coordinates of optimized stationary points on the potential energy surfaces (PESs) of two reactions: N(<sup>2</sup>D)+ C<sub>2</sub>H<sub>4</sub> and N(<sup>2</sup>D)+ CH<sub>2</sub>CHCN.</p> <p>The PESs have been published in our article “Computational Investigation of the N(<sup>2</sup>D)+ C<sub>2</sub>H<sub>4</sub> and N(<sup>2</sup>D)+ CH<sub>2</sub>CHCN Reactions: Benchmark Analysis and Implications for Titan’s Atmosphere”</p> <p>(<em>Lecture Notes in Computer Science</em> <strong>2023</strong>, 14105, 705-717), that can be found in https://doi.org/10.1007/978-3-031-37108-0_45 .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.</p>
Optimized stationary points on the potential energy surfaces of the S+(4S) + SiH2(1A1) and HSiS+/SiSH+ + NH3 reactions
<p>This Zip file contains the cartesian coordinates of optimized stationary points on the potential energy surfaces (PESs) of three reactions: S<sup>+</sup>(<sup>4</sup>S) + SiH<sub>2</sub>(<sup>1</sup>A<sub>1</sub>), <sup>3</sup>HSiS<sup>+</sup> + NH<sub>3</sub> and <sup>3</sup>SiSH<sup>+</sup> + NH<sub>3</sub>.</p> <p>These PESs are part of our paper “The S<sup>+</sup>(<sup>4</sup>S)+SiH<sub>2</sub>(<sup>1</sup>A<sub>1</sub>) Reaction: Toward the Synthesis of Interstellar SiS”</p> <p>(<em>Lecture Notes in Computer Science</em> <strong>2022</strong>, 13378, 233-245), that can be downloaded in https://doi.org/10.1007/978-3-031-10562-3_17 .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pV(T+d)Z level of theory.</p>
Optimized structures of selected stationary points on the potential energy surface of the HC3N + CN reaction
<p>This Zip file contains the cartesian coordinates of optimized stationary points of the HC<sub>3</sub>N + CN potential energy surface published in our article “Semiempirical Potential in Kinetics Calculations on the HC<sub>3</sub>N + CN Reaction” (<em>Molecules</em> <strong>2022</strong>, <em>27(7)</em>, 2297), that can be found in <a href="https://doi.org/10.3390/molecules27072297">https://doi.org/10.3390/molecules27072297</a> .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at M06-2X/6-311+G(d,p) level of theory.</p>
Optimal planning of autonomous electric vehicles charging stations with photovoltaic generations and energy storage systems
<p>This database contains technical information on the 69-bus electrical distribution system. This system was tested in a mixed integer linear programming model for allocating autonomous electric vehicle charging stations equipped with photovoltaic generation and energy storage systems. Additionally, this document contains data related to charging stations, energy storage systems, and operational scenarios applied to the case studies.</p>
Less is More: Exploiting the Standard Compiler Optimization Levels for Better Performance and Energy Consumption
<p>The data used to generate the graphs in Figures 2 and 3 of the paper "Less is More: Exploiting the Standard Compiler Optimization Levels for Better Performance and Energy Consumption" at SCOPES'18. The data includes power consumption, code size, and running time, indexed by an ID that identifies the combination of compiler options. Data is stored in a CSV file that identifies the specific benchmark, organized into a zip file that identifies the chip architecture.</p> <p>See README.txt for details.</p>
Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization
<p>Data for the Wind Energy Science paper "Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization".</p> <p>The data includes a file describing the wind direction distribution. The i<sup>th</sup> probability value corresponds to the probability of the wind coming between direction i and i+1.</p> <p>The other data files, corresponding to the wind farm layouts, provide the x,y coordinates of the wind turbines. </p>
Load Shifting Optimization with Genetic Algorithms for Energy Cost Minimization in Households - Case Study Data2
<p>The case study of this dataset uses real household data, representing five days from 0h00 to 23h59. This dataset uses a period of 15 minutes for all loads execution time and energy data. The case study considers twenty unique houses that can have up to five different shiftable appliances, each executing three process cycles.<br> <br> File Description:</p> <ul> <li>Case_Studies_Data-BAU_and_Load_Shifting - Excel containing appliances energy profile, load execution preferences, BAU consumption, and houses' data</li> <li>Houses_Input_Output_JSONs_and_Statistics - Zip containing the input and output files from the proposed system, as well as their corresponding schedule statistics</li> </ul>
Load Shifting Optimization with Genetic Algorithms for Energy Cost Minimization in Households - Case Study Data
<p>The case study of this dataset uses real household data, representing five days from 0h00 to 23h59. This dataset uses a period of 15 minutes for all loads execution time and energy data. The case study considers twenty unique houses that can have up to five different shiftable appliances, each executing three process cycles.<br> <br> File Description:</p> <ul> <li>Case_Studies_Data-BAU_and_Load_Shifting - Excel containing appliances energy profile, load execution preferences, BAU consumption, and other house data</li> <li>Houses_Input_JSONs - Zip containing the input files, from each house, for the proposed system</li> </ul>
Optimal Dynamic Service Restoration of Distribution Networks Considering Energy Storage System Data
<p>The power distribution system presented is composed with 53 node and 61 branches and can be employed in multi-time service restoration problem, islading operation and energy storage system optimal operation. The system was designed based on a 53 node system (available <a href="https://ieee-dataport.org/documents/optimal-service-restoration-active-distribution-networks-considering-microgrid-formation">here</a>). The dataset was modified to include 6 photovoltaic generation, 3 energy storage system and time-changing demand load nodes.</p>
Optimization procedure of low frequency vibration energy harvester based on magnetic levitation: Datasets and scripts
<p>****** Please view the README.txt file for detailed documentation of data. ******</p> <p> </p> <p>Title: Optimization procedure of low frequency vibration energy harvester based on magnetic levitation: Datasets and scripts<br>Version: 2.0<br>Date of Release: 2023/08/23<br>Identifier: doi:10.5281/zenodo.8317223<br>Permalink: http://dx.doi.org/10.5281/zenodo.8317223</p> <p><br>Associated publication: I. Royo-Silvestre, J. J. Beato-López, C. Gómez-Polo "Optimization procedure of low frequency vibration energy harvester based on magnetic levitation", Applied Energy, Volume 360, 15 April 2024, 122778</p> <p>Link to publication: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.apenergy.2024.122778" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.apenergy.2024.122778</span></a></p> <p><br>Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README file.</p> <p> </p> <p>Contact information: Isaac Royo Silvestre, Universidad Pública de Navarra, Pamplona, Spain., isaac.royo@unavarra.es<br>Co-authors: juanjesus.beato@unavarra.es, gpolo@unavarra.es</p> <p> </p> <p>Dates of data collection: 2023/03<br>Geographic location: Pamplona, Spain</p> <p> </p> <p>This directory contains the following datasets and scripts:</p> <p>SCRIPTS</p> <p>- harvester_op.m: Matlab script to automate the design and optimize a magnetic spring based vibration energy harvester (more information in the associated paper)</p> <p>- harvester_op_par.m: Matlab script, a version of harvester_op.m modified for parallel computing and shorter execution time in multicore computers (file added in v 2.0 of the data upload).</p> <p>DATASETS<br>- data.zip: Experimental data recorded by the datalogger as well as tabular data required to plot curves (compressed zip file) in csv format</p> <p> </p> <p>Specific documentation of each file is described in readme files.</p> <p> </p> <p>Refer to the original manuscript (see above) and the text of the Supplementary Materials published alongside this manuscript for additional information regarding the collection and generation of these data.</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.