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13 results for “hourly series”
Hourly time series of Ives Lake (Huron Mountains, Marquette County, MI) Water Temperature-Depth Profiles, 2013-2022 (continuing study)
Long-term measurements of lake temperatures are essential to providing insights into local and regional changes in climate since large, still water bodies effectively act as a high-frequency filter. Ives Lake is a 30.7 m deep water body in northern Marquette County, in Michigan's Upper Peninsula. Beginning in 2013, temperature readings have been collected hourly from a string of twelve sensors located near the deepest point of the lake (approximately 46.84874 N lat, 87.84895 W long; identified by sonar survey in 2010 by first author). Measurements are continuing. The purpose of the project is to collect a data record of sufficient duration to determine if water temperatures are warming, and if the dates of autumn lake turnover are shifting toward later in the year.
A DETAILED TIME SERIES OF HOURLY CIRCUMFERENCE VARIATIONS IN PINUS PINEA L. IN CHILE
<ul> <li>The dataset provides digital dendrometer measurements on stem circumference of irrigated and non-irrigated<em> Pinus pinea</em> trees. Data were obtained in a xeric non-native habitat of central Chile. Forest mensuration were hourly collected from six adult trees during a growth year.</li> </ul>
Data and code to accompany the manuscript "Ground subsidence and heave over permafrost: hourly time series reveal inter-annual, seasonal and shorter-term movement caused by freezing, thawing and water movement"
<p>Data and code to accompany the manuscript "Ground subsidence and heave over permafrost: hourly time series reveal inter-annual, seasonal and shorter-term movement caused by freezing, thawing and water movement" submitted to The Cryosphere.</p>
Hourly time series of soil and atmosphere variables at the experimental site of El Cautivo, Tabernas Desert, Almeria, Spain (February 2018 to December 2019)
<p>Measurements were performed along a hypothetical succession of biological soil crusts. Main studied variables were the soil-atmosphere CO2 and water vapor fluxes. This dataset was used by Lopez-Canfin et al. (2022) and Kim and al. (2024) at the time of publication.</p>
GODEEEP Light Duty Vehicle (LDV) Hourly Time Series Loads by County
<p>Each file contains projected light duty vehicle (LDV) load by county for a particular U.S. state, GCAM-USA scenario, and climate pathway as specified in the file name. For the full discussion of the methodology, please see <a href="https://godeeep.pnnl.gov/pubs/EV_Load_Shapes_GODEEEP_Arxiv.pdf">https://godeeep.pnnl.gov/pubs/EV_Load_Shapes_GODEEEP_Arxiv.pdf</a>. For the balancing authority level timeseries, please see <a href="https://doi.org/10.5281/zenodo.7888568">10.5281/zenodo.7888568</a>. The code used to produce this data is available at <a href="https://github.com/GODEEEP/transportation_electrification">https://github.com/GODEEEP/transportation_electrification</a>. Note that fleet sizes at the state scale are derived from the GCAM-USA scenario output. Downscaling to the county scale uses the electric vehicle penetration rates found in the appendix of <a href="https://www.pnnl.gov/sites/default/files/media/file/EV-AT-SCALE_1_IMPACTS_final.pdf">M. Kintner-Meyer, S. Davis, S. Sridhar, D. Bhatnagar, S. Mahserejian and M. Ghosal, "Electric vehicles at scale-phase I analysis: High EV adoption impacts on the western US power grid", Tech. Rep., 2020</a>. To harmonize the state scale LDV energy use from the GCAM-USA scenarios with the LDV load calculated with EV-Pro Lite, a scale factor was applied to the county level loads, so note that if the reported scale factor is much different than 1.0 there is potentially some disagreement between the load and the fleet size. This scale factor has already been applied to the loads reported in these files (but has NOT been applied to the fleet sizes).</p><h4><strong>GCAM-USA scenarios</strong></h4><p>See <a href="https://doi.org/10.5281/zenodo.7838871">10.5281/zenodo.7838871</a> and <a href="https://doi.org/10.5281/zenodo.8377778">10.5281/zenodo.8377778</a> for more details</p><ul><li>BAU_Climate - a business-as-usual scenario without IRA incentives</li><li>business_as_usual_ira_ccs_climate - a business-as-usual scenario with IRA incentives for CCS technology</li><li>NetZeroNoCCS_Climate - a scenario targeting net-zero by 2050 without IRA incentives, disallowing CCS technology</li><li>net_zero_ira_ccs_climate - scenario targeting net-zero by 2050 with IRA incentives for CCS technology</li></ul><h4><strong>Climate pathways</strong></h4><p>See <a href="https://doi.org/10.1038/s41597-023-02485-5">10.1038/s41597-023-02485-5</a> for more details</p><ul><li>rcp45cooler - historical weather patterns projected into the future with a warming signal applied commensurate with a cooler ensemble of RCP4.5 CMIP6 models</li><li>rcp85hotter - historical weather patterns projected into the future with a warming signal applied commensurate with a hotter ensemble of RCP4.5 CMIP6 models</li></ul><h4><strong>Fields in the data files:</strong></h4><ul><li>time - hourly timestamp in UTC representing the preceding hour of data</li><li>county - the county name</li><li>State - the state abbreviation for this county</li><li>FIPS - FIPS code for the county</li><li>balancing_authority - the balancing authority responsible for the load reported in this row; note that some counties span multiple balancing authorities and their load is divided between those balancing authorities proportional to the population residing within that balancing authority</li><li>load_MWh - load on the grid caused by the charging of LDVs during this hour within this county and balancing authority in megawatt hours</li><li>temperature_celsius - mean temperature within this county and balancing authority in degrees Celsius</li><li>fleet_size - number of electrified LDV cars within this county and balancing authority</li><li>daily_miles - average number of miles traveled per day per LDV within this county and balancing authority in miles/day</li><li>scale_factor - the values in the load_MWh field have been scaled by this multiplier in order to harmonize the state scale LDV loads with the GCAM-USA scenarios</li></ul><h4><strong>Changelog</strong></h4><ul><li>v1.0.1 - added fleet_size, daily_miles, and scale_factor to the output, and updated the README accordingly</li></ul><h4><strong>Acknowledgements</strong></h4><p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p><p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p>
Forecasting hourly emergency department arrival using time series analysis
<p></p> Background/aims <p>The stochastic arrival of patients at hospital emergency departments complicates their management. More than 50% of a hospital's emergency department tends to operate beyond its normal capacity and eventually fails to deliver high-quality care. To address this concern, much research has been carried out using yearly, monthly and weekly time-series forecasting. This article discusses the use of hourly time-series forecasting to help improve emergency department management by predicting the arrival of future patients.</p> Methods <p>Emergency department admission data from January 2014 to August 2017 was retrieved from a hospital in Iowa. The auto-regressive integrated moving average (ARIMA), Holt–Winters, TBATS, and neural network methods were implemented and compared as forecasters of hourly patient arrivals.</p> Results <p>The auto-regressive integrated moving average (3,0,0) (2,1,0) was selected as the best fit model, with minimum Akaike information criterion and Schwartz Bayesian criterion. The model was stationary and qualified under the Box–Ljung correlation test and the Jarque–Bera test for normality. The mean error and root mean square error were selected as performance measures. A mean error of 1.001 and a root mean square error of 1.55 were obtained.</p> Conclusions <p>The auto-regressive integrated moving average can be used to provide hourly forecasts for emergency department arrivals and can be implemented as a decision support system to aid staff when scheduling and adjusting emergency department arrivals.</p> <p></p><p></p><p></p>
Building thermal and electrical energy hourly time series
<p>The results reported are the <strong>hourly thermal and electrical energy use</strong> for one year of a renovated five-floor small Multi-Family house with ten dwellings in different EU climates.</p> <p>A simulation-based database organized in Excel files (one per each climate) collects the hourly thermal and electrical energy use in the considered reference building (a small Multi-Family house) in three EU climates: Nordic, EU Continental, and Mediterranean considering the HVAC system solution with room booster heat pumps (HPs) developed within Horizon 2020 HAPPENING project and two reference systems. </p> <p>The building, HVAC and on-site renewable energy production systems are modeled and simulated using TRNSYS as dynamic simulation software. </p> <p>The fact that this simulation work is performed in different EU climates allows to extrapolate indications about how the HVAC system solutions work in different EU climatic contexts.</p> <p>Regarding the <strong>building</strong>, a small Multi-Family house is considered as it represents the target building for the HP-base solutions developed within the HAPPENING project. The building has five floors with two dwellings per floor with a floor area of 50 m<sup>2</sup> each, for a total floor area of 500 m<sup>2</sup>. In terms of thermal envelope performance, a renovated building has been considered, with a yearly space heating thermal demand in the range of 70 kWh/m<sup>2</sup>/y in the Nordic and EU Continental climates.</p> <p>Regarding the <strong>HVAC system</strong> solutions, the dataset reports the results obtained considering the cascade HP-based solution with room booster HPs developed in the project HAPPENING. In addition to that, a typical (reference) air-water HP system, and a gas boiler system are used as reference systems to assess and compare the HAPPENING system solution performance.</p> <p>Regarding the <strong>on-site renewable energy production system</strong>, a PV plant has been considered. The presence of an electric battery is also accounted for to allow a more complete assessment of the interactions between on-site renewable production and HVAC system consumption, highlighting the effects, advantages, and disadvantages of having also an electric storage. The PV and battery system is considered in all HVAC system solutions (HAPPENING solution, reference air-water HP system, gas boiler system) to highlight the differences in the interactions between different HVAC system solutions with PV and battery.</p> <p>The project deliverable D2.7, available in the material, reports more details about all the quantities included in the dataset, as well as a series of system performance analyses, both over representative weeks and on a yearly basis.</p> <p> </p> <p>The research leading to these results has received funding from the European Community's Horizon 2020 Programme (H2020) under grant agreement n° 957007.</p>
Forecasting hourly emergency department arrival using time series analysis
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Meyerowitz Lab Arabidopsis Operon Array v4 - 4200A scanner - AP1-GR ap1cal inflorescences - time series (hours)
GEO Series GSE20182. Arabidopsis thaliana. 20 samples. Type: Expression profiling by array.
Ath_Agilent_v1_Riechmann array expression profile AP1-GR ap1cal inflorescences - time series (hours)
GEO Series GSE20139. Arabidopsis thaliana. 20 samples. Type: Expression profiling by array.
Meyerowitz Lab Arabidopsis Operon Array v4 - 4000B scanner - AP1-GR ap1cal inflorescences - time series (hours)
GEO Series GSE20183. Arabidopsis thaliana. 20 samples. Type: Expression profiling by array.
SC35 (SRSF2) ChIP-Seq analysis of liver samples from wild type (WT) mice and XBP1 liver-specific knockout (LKO) mice over a 48 hour time series
GEO Series GSE174170. Mus musculus. 32 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Time series (T= 0-4 hours) for E. coli K-12 MC4100 after a shift from 23˚C to 37˚C (M9 minimal glycerol medium, exponential phase growth)
GEO Series GSE165794. Escherichia coli. 9 samples. Type: Expression profiling by array.
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