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63 results for “Energy Management”

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dryad36/100

A real-world energy management data set from a smart company building for optimization and machine learning

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publicFeb 2025View details →
dryad36/100

Energetic limits: Defining the bounds and trade-offs of successful energy management in a capital breeder

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publicAug 2020View details →
dryad36/100

Determinants of heart rate in Svalbard reindeer reveal mechanisms of seasonal energy management

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publicJun 2021View details →
dryad36/100

Data from: Stress-coping styles are associated with energy budgets and variability in energy management strategies in a capital breeder

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publicApr 2025View details →
dryad36/100

Data for: Causal mechanisms for negative impacts of energy development inform management triggers for sagebrush birds

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publicMar 2023View details →
dryad32/100

Data from: State-dependent behavior alters endocrine-energy relationship: implications for conservation and management

Glucocorticoids (GC) and triiodothyronine (T3) are two endocrine markers commonly used to quantify resource limitation, yet the relationships between these markers and the energetic state of animals has been studied primarily in small-bodied species in captivity. Free-ranging animals, however, adjust energy intake in accordance with their energy reserves, a behavior known as state-dependent foraging. Further, links between life-history strategies and metabolic allometries cause energy intake and energy reserves to be more strongly coupled in small animals relative to large animals. Because GC and T3 may reflect energy intake or energy reserves, state-dependent foraging and body size may cause endocrine-energy relationships to vary among taxa and environments. To extend the utility of endocrine markers to large-bodied, free-ranging animals, we evaluated how state-dependent foraging, energy reserves, and energy intake influenced fecal GC and fecal T3 concentrations in free-ranging moose (Alces alces). Compared with individuals possessing abundant energy reserves, individuals with few energy reserves had higher energy intake and high fecal T3 concentrations, thereby supporting state-dependent foraging. Although fecal GC did not vary strongly with energy reserves, individuals with higher fecal GC tended to have fewer energy reserves and substantially greater energy intake than those with low fecal GC. Consequently, individuals with greater energy intake had both high fecal T3 and high fecal GC concentrations, a pattern inconsistent with previous documentation from captive animal studies. We posit that a positive relationship between GC and T3 may be expected in animals exhibiting state-dependent foraging if GC is associated with increased foraging and energy intake. Thus, we recommend that additional investigations of GC- and T3-energy relationships be conducted in free-ranging animals across a diversity of body size and life-history strategies before these endocrine markers are applied broadly to wildlife conservation and management.

opencc-zeroDec 2016View details →
zenodo32/100

A comparative study between air cooling and liquid cooling thermal management systems for a high-energy lithium-ion battery module

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opencc-by-4.0Aug 2021View details →
dryad32/100

Data from: The active mouse rests within: Energy management among and within individuals

<p>1. The relationship between daily energy expenditure (DEE) and resting metabolic rate (RMR) provides insight into how organisms allocate energy to maintenance <i>versus</i> energetically expensive activities such as locomotor activity.</p> <p>2. Three models have been devised to describe energy management: the allocation, independent, and performance models, which respectively predict a DEE-RMR slope of <i>b</i>&lt;1, <i>b</i>=1, and <i>b</i>&gt;1.</p> <p>3. Here, we took paired repeated metabolic and behavioural measurements in 51 female white-footed mice to 1) evaluate which energy management models apply at the among- and within-individual levels, and to 2) quantify the relationship between metabolic traits and two energetically expensive behaviours.</p> <p>4. The DEE-RMR slope was different at the among- <i>versus</i> within-individual levels, with values supporting the performance and allocation models at the among- and within-individual levels, respectively. Accordingly, the relationship between voluntary wheel running and RMR was positive at the among-individual level (<i>r</i><sub> </sub>= 0.40±0.21), but negative at the within-individual level (<i>r </i>­= -0.23±0.10).</p> <p>5. To our knowledge, this is the first study to simultaneously partition the relationship between RMR and behaviour at the among- <i>versus</i> within-individuals levels while determining which energy management models apply at each of these levels. In doing so, we have identified a mechanism through which compensation occurs at the within-individual level.</p>

opencc-zeroDec 2021View details →
zenodo32/100

Electronic Companion -- Optimal Management of Distribution-Connected Assets Operating under Carbon and Energy Day-Ahead Markets

<p>This release is associated with a paper entitled &quot;Optimal Management of Distribution-Connected Assets Operating under Carbon and Energy Day-Ahead Markets&quot;.</p> <p>In this document, we provide information regarding the&nbsp;transmission and distribution power systems adopted to present the results shown in the paper. These systems are modified versions&nbsp;of the IEEE&nbsp;<a href="https://icseg.iti.illinois.edu/ieee-14-bus-system/">14-bus</a>&nbsp;and the IEEE&nbsp;<a href="https://cmte.ieee.org/pes-testfeeders/resources/">34-bus</a>, respectively.&nbsp;Five dispatchable generators&nbsp;were added to the distribution system and&nbsp;13 load shapes were considered. The data regarding the costs and physical parameters of each of the power system&#39;s elements are provided in this document.</p>

opencc-by-4.0Dec 2022View details →
zenodo32/100

Dataset for Energy Resource Management Considering Participation in the Wholesale Day-Ahead Market

<p>This release is associated with a paper entitled &quot;A Novel Framework for Day-Ahead Market Clearing Process Featuring a Hybrid Pricing Mechanism&quot;.</p> <p>In this document, we provide the mathematical manipulation employed to linearize an MPEC problem that models the market-clearing process considering the participation of generation companies and distribution operators in the day-ahead market.&nbsp;Additionally, we provide a numerical example of the pricing mechanism designed for solving the optimization problem.&nbsp;</p> <p>Finally, we include information regarding the&nbsp;transmission and distribution power systems adopted to present the results shown in the paper. These systems are modified versions&nbsp;of the IEEE&nbsp;<a href="https://icseg.iti.illinois.edu/ieee-14-bus-system/">14-bus</a> and the IEEE <a href="https://cmte.ieee.org/pes-testfeeders/resources/">34-bus</a>, respectively.&nbsp;Three renewable non-dispatchable generators were added to the original 14-bus system, while 5 dispatchable generators and 3 non-dispatchable generators were added to the distribution system. 13 load shapes were considered as well as 5 scenarios for each uncertain variable&nbsp;(wind velocity, solar irradiance, distribution load oscillation, and transmission load oscillation). The data regarding the costs and physical parameters of each of the power system&#39;s elements are provided in this document.</p>

opencc-by-4.0Sep 2022View details →
zenodo32/100

Dataset for Energy Resource Management Considering Participation in the Wholesale Day-Ahead Market

<p>This release is associated with a paper entitled &quot;A Novel Bilevel Programming Formulation for Optimizing the Interaction of Distribution System Operators and GENCOs in Day-Ahead Markets&quot;.</p> <p>In this document, we provide the mathematical manipulation employed to linearize an MPEC problem that models the market-clearing process considering the participation of generation companies and distribution operators in the day-ahead market.&nbsp;Additionally, we provide a numerical example of the pricing mechanism designed for solving the optimization problem.&nbsp;</p> <p>Finally, we include information regarding the&nbsp;transmission and distribution power systems adopted to present the results shown in the paper. These systems are modified versions&nbsp;of the IEEE&nbsp;<a href="https://icseg.iti.illinois.edu/ieee-14-bus-system/">14-bus</a> and the IEEE <a href="https://cmte.ieee.org/pes-testfeeders/resources/">34-bus</a>, respectively.&nbsp;Three renewable non-dispatchable generators were added to the original 14-bus system, while 5 dispatchable generators and 3 non-dispatchable generators were added to the distribution system. 13 load shapes were considered as well as 5 scenarios for each uncertain variable&nbsp;(wind velocity, solar irradiance, distribution load oscillation, and transmission load oscillation). The data regarding the costs and physical parameters of each of the power system&#39;s elements are provided in this document.</p>

opencc-by-4.0Sep 2022View details →
zenodo32/100

Dataset for Energy Resource Management Considering Participation in the Wholesale Day-Ahead Market

<p>This release is associated with a paper entitled &quot;A Novel Framework for the Day-Ahead Market Clearing Process Featuring the Participation of Distribution System Operators and a Hybrid Pricing Mechanism&quot;.</p> <p>In this document, we provide the mathematical manipulation employed to linearize an MPEC problem that models the market-clearing process considering the participation of generation companies and distribution operators in the day-ahead market.&nbsp;Additionally, we provide a numerical example of the pricing mechanism designed for solving the optimization problem.&nbsp;</p> <p>Finally, we include information regarding the&nbsp;transmission and distribution power systems adopted to present the results shown in the paper. These systems are modified versions&nbsp;of the IEEE&nbsp;<a href="https://icseg.iti.illinois.edu/ieee-14-bus-system/">14-bus</a> and the IEEE <a href="https://cmte.ieee.org/pes-testfeeders/resources/">34-bus</a>, respectively.&nbsp;Three renewable non-dispatchable generators were added to the original 14-bus system, while 5 dispatchable generators and 3 non-dispatchable generators were added to the distribution system. 13 load shapes were considered as well as 5 scenarios for each uncertain variable&nbsp;(wind velocity, solar irradiance, distribution load oscillation, and transmission load oscillation). The data regarding the costs and physical parameters of each of the power system&#39;s elements are provided in this document.</p>

opencc-by-4.0Sep 2022View details →
zenodo32/100

Dataset for Energy Resource Management Considering Participation in the Wholesale Day-Ahead Market

<p>This release is associated with a paper entitled &quot;A Novel Framework for the Day-Ahead Market Clearing Process Featuring the Participation of Distribution System Operators and a Hybrid Pricing Mechanism&quot;.</p> <p>In this document, we provide the mathematical manipulation employed to linearize an MPEC problem that models the market-clearing process considering the participation of generation companies and distribution operators in the day-ahead market.&nbsp;Additionally, we provide a numerical example of the pricing mechanism designed for solving the optimization problem.&nbsp;</p> <p>Finally, we include information regarding the&nbsp;transmission and distribution power systems adopted to present the results shown in the paper. These systems are modified versions&nbsp;of the IEEE&nbsp;<a href="https://icseg.iti.illinois.edu/ieee-14-bus-system/">14-bus</a>&nbsp;and the IEEE&nbsp;<a href="https://cmte.ieee.org/pes-testfeeders/resources/">34-bus</a>, respectively.&nbsp;Three renewable non-dispatchable generators were added to the original 14-bus system, while 5 dispatchable generators and 3 non-dispatchable generators were added to the distribution system. 13 load shapes were considered as well as 5 scenarios for each uncertain variable&nbsp;(wind velocity, solar irradiance, distribution load oscillation, and transmission load oscillation). The data regarding the costs and physical parameters of each of the power system&#39;s elements are provided in this document.</p>

opencc-by-4.0Sep 2022View details →
ClinicalTrials.gov32/100

Training and Energy Management Education to Improve Quality of Life in Persons With Multiple Sclerosis

ClinicalTrials.gov study NCT04356248. IPD Sharing: Not stated. Countries: 1. Publications: 5.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov32/100

Energy Expenditure From ECAL Indirect Calorimeter in a Multicomponent Weight Management Service

ClinicalTrials.gov study NCT03638895. IPD Sharing: NO. Countries: 1. Publications: 7.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov32/100

A Self-Management Energy Conservation Program for Cancer-Related Fatigue

ClinicalTrials.gov study NCT03282214. IPD Sharing: YES. Countries: 1. Publications: 1.

controlledIPD-YESFeb 2026View details →
ClinicalTrials.gov32/100

Protocol of the Packer Managing Fatigue Program Versus Standard Information to Improve Energy Conservation Self-Efficacy in Parkinson's Disease

ClinicalTrials.gov study NCT07094269. IPD Sharing: YES. Countries: 1. Publications: 11.

controlledIPD-YESFeb 2026View details →
ClinicalTrials.gov32/100

Effectiveness of Muscles Energy Technique in the Management of Chronic Non-specific Low Back Pain

ClinicalTrials.gov study NCT03449810. IPD Sharing: UNDECIDED. Countries: 1. Publications: 16.

restrictedIPD-UNDECIDEDFeb 2026View details →
dryad32/100

Data from: State-dependent behavior alters endocrine-energy relationship: implications for conservation and management

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publicJul 2017View details →
dryad32/100

Data from: The active mouse rests within: Energy management among and within individuals

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publicDec 2021View details →

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
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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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record