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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Sathre, Roger; Gustavsson, Leif;

    Heavy trucks contribute significantly to climate change, and in 2020 were responsible for 7% of total Swedish GHG emissions and 5% of total global CO2 emissions. Here we study the full lifecycle of cargo trucks powered by different energy pathways, comparing their biomass feedstock use, primary energy use, net biogenic and fossil CO2 emission, and cumulative radiative forcing. We analyse battery electric trucks with bioelectricity from standalone or combined heat and power (CHP) plants, and pathways where bioelectricity is integrated with wind and solar electricity. We analyse trucks operated on fossil diesel fuel and on dimethyl ether (DME). All energy pathways are analysed with and without carbon capture and storage (CCS). Bioelectricity and DME are produced from forest harvest residues. Forest biomass is a limited resource, so in a scenario analysis we allocate a fixed amount of biomass to power Swedish truck transport. Battery lifespan and chemistry, the technology level of energy supply, and the biomass source and transport distance are all varied to understand how sensitive the results are to these parameters. The scenario spans 100 years into the future. We find that pathways using electricity to power battery electric trucks have much lower climate impacts and primary energy use, compared to diesel and DME based pathways. The pathways using bioelectricity with CCS result in negative emissions leading to global cooling of the earth. The pathways using diesel and DME have significant and very similar climate impact, even with CCS. The robust results show that truck electrification and increased renewable electricity production is a much better strategy to reduce the climate impact of cargo transport and much more primary energy efficient than the adoption of DME trucks. This climate impact analysis includes all fossil and net biogenic CO2 emissions as well as the timing of these emissions. Considering only fossil emissions is incomplete and could be misleading. This dataset contains data on 4 metrics (primary energy use, biomass feedstock use, cumulative CO2 emissions, and cumulative radiative forcing) resulting from scenario modeling of cargo truck use in Sweden powered by different energy pathways. The energy pathways include battery electric trucks powered by bioelectricity, solar photovoltaic electricity and wind electricity, and internal combustion trucks powered by fossil diesel and dimethyl ether. The scenario spans 100 years into the future. The Excel sheet "tables" contains input data for the scenario modeling, with sources listed where applicable. The remaining sheets contains the modeled results and generated figures that are also a published in the associated article Sathre & Gustavsson (2023). Refer to the method description and reference list in the included documentation files for details. Tunga lastbilar bidrar kraftigt till klimatförändringarna och stod 2020 för 7% av de totala svenska växthusgasutsläppen och 5% av de totala globala CO2-utsläppen. Här studerar vi hela livscykeln för lastbilar som drivs av olika energivägar, jämför deras användning av biomassaråvaror, primär energianvändning, biogena och fossila CO2-utsläpp netto och kumulativ strålningstvingning. Vi analyserar batterielektriska lastbilar med bioel från fristående eller kraftvärmeverk och vägar där bioel integreras med vind- och solkraft. Vi analyserar lastbilar som drivs med fossilt dieselbränsle och med dimetyleter (DME). Alla energivägar analyseras med och utan avskiljning och lagring av koldioxid (CCS). Bioelektricitet och DME produceras av skogsavverkningsrester. Skogsbiomassa är en begränsad resurs, så i en scenarioanalys avsätter vi en fast mängd biomassa för att driva svenska lastbilstransporter. Batteriets livslängd och kemi, tekniknivån för energiförsörjning och biomassakällan och transportavståndet varierar alla för att förstå hur känsliga resultaten är för dessa parametrar. Scenariot sträcker sig 100 år in i framtiden. Vi finner att vägar som använder el för att driva batterielektriska lastbilar har mycket lägre klimatpåverkan och primär energianvändning, jämfört med diesel- och DME-baserade vägar. De vägar som använder bioelektricitet med CCS resulterar i negativa utsläpp som leder till global kylning av jorden. Vägarna med diesel och DME har betydande och mycket liknande klimatpåverkan, även med CCS. De robusta resultaten visar att elektrifiering av lastbilar och ökad förnybar elproduktion är en mycket bättre strategi för att minska godstransporternas klimatpåverkan än införandet av DME-lastbilar, och mycket mer primärenergieffektiv. Denna klimatkonsekvensanalys omfattar alla fossila och biogena CO2-utsläpp samt tidpunkten för dessa utsläpp. Att bara ta hänsyn till fossila utsläpp är ofullständigt och kan vara missvisande. Detta dataset innehåller data om 4 mätvärden (primär energianvändning, biomassaråvara, kumulativa CO2-utsläpp och kumulativ strålkraftspåverkan) som härrör från scenariomodellering av lastbilsanvändning i Sverige som drivs av olika energivägar. Energivägarna inkluderar batterielektriska lastbilar som drivs av bioelektricitet, solcellselektricitet och vindkraft samt förbränningsbilar som drivs av fossil diesel och dimetyleter. Scenariot sträcker sig 100 år in i framtiden. På arket "tables" i Excelfilen återfinns den indata som använts i modelleringen med angivna källor där detta är tillämpligt. Övriga ark innehåller resultat samt figurer som också publiceras i den samhörande artikeln Sathre & Gustavsson (2023). Se metodbeskrivning samt referenslista i tillhörande dokumentationsfiler för detaljer.

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    Swedish National Data Service
    Dataset . 2023
    Data sources: Datacite
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    Swedish National Data Service
    Dataset . 2023
    Data sources: Datacite
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      Swedish National Data Service
      Dataset . 2023
      Data sources: Datacite
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      Swedish National Data Service
      Dataset . 2023
      Data sources: Datacite
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    Authors: Fosas, Daniel; Nikolaidou, Elli; Roberts, Matt; Allen, Stephen; +2 Authors

    Dataset for the journal paper "Towards Active Buildings: rating grid-servicing buildings", which describes the simulations for the 20 case study buildings. The simulation inputs describe the intended characteristics as part of the early design stage process, and the outputs the performance metrics under the rating system introduced in the journal paper, called the ABCode1. Such outputs rate the relative merits of each case study in terms of embodied carbon, energy requirements, energy generation and energy flexibility. The simulation outputs have been generated using the inputs included in the dataset, which were then simulated in David Coley’s ZEBRA and then evaluated with the rating system proposed in the journal publication as part of ABCode1. The files are in the original Excel xlsx file (Microsoft Office 365), but it may be viewed by any other spread sheet tools such as LibreOffice's Calc.

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    University of Bath Research Data Archive
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ University of Bath R...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      University of Bath Research Data Archive
      Dataset . 2020
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Russell, Debbie J. F.; Hastie, Gordon D.; Thompson, David; Janik, Vincent M.; +6 Authors

    As part of global efforts to reduce dependence on carbon-based energy sources there has been a rapid increase in the installation of renewable energy devices. The installation and operation of these devices can result in conflicts with wildlife. In the marine environment, mammals may avoid wind farms that are under construction or operating. Such avoidance may lead to more time spent travelling or displacement from key habitats. A paucity of data on at-sea movements of marine mammals around wind farms limits our understanding of the nature of their potential impacts. Here, we present the results of a telemetry study on harbour seals Phoca vitulina in The Wash, south-east England, an area where wind farms are being constructed using impact pile driving. We investigated whether seals avoid wind farms during operation, construction in its entirety, or during piling activity. The study was carried out using historical telemetry data collected prior to any wind farm development and telemetry data collected in 2012 during the construction of one wind farm and the operation of another. Within an operational wind farm, there was a close-to-significant increase in seal usage compared to prior to wind farm development. However, the wind farm was at the edge of a large area of increased usage, so the presence of the wind farm was unlikely to be the cause. There was no significant displacement during construction as a whole. However, during piling, seal usage (abundance) was significantly reduced up to 25 km from the piling activity; within 25 km of the centre of the wind farm, there was a 19 to 83% (95% confidence intervals) decrease in usage compared to during breaks in piling, equating to a mean estimated displacement of 440 individuals. This amounts to significant displacement starting from predicted received levels of between 166 and 178 dB re 1 μPa(p-p). Displacement was limited to piling activity; within 2 h of cessation of pile driving, seals were distributed as per the non-piling scenario. Synthesis and applications. Our spatial and temporal quantification of avoidance of wind farms by harbour seals is critical to reduce uncertainty and increase robustness in environmental impact assessments of future developments. Specifically, the results will allow policymakers to produce industry guidance on the likelihood of displacement of seals in response to pile driving; the relationship between sound levels and avoidance rates; and the duration of any avoidance, thus allowing far more accurate environmental assessments to be carried out during the consenting process. Further, our results can be used to inform mitigation strategies in terms of both the sound levels likely to cause displacement and what temporal patterns of piling would minimize the magnitude of the energetic impacts of displacement. Wash_diagWash_diag.xlsx is the historic location data (pre windfarm construction) for the 19 individuals used in the analysis described in Russell et al.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Dataset . 2017
    License: CC 0
    Data sources: ZENODO
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    B2FIND
    Dataset . 2016
    Data sources: B2FIND
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    EASY
    Dataset . 2016
    Data sources: EASY
    DRYAD
    Dataset . 2017
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2017
      License: CC 0
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      B2FIND
      Dataset . 2016
      Data sources: B2FIND
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      EASY
      Dataset . 2016
      Data sources: EASY
      DRYAD
      Dataset . 2017
      License: CC 0
      Data sources: Datacite
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Hedberg, Per; Sundquist, Göran;

    Oskarshamn is one of the municipalities being discussed as a possible site for disposal of nuclear waste from the Swedish nuclear power plants, and there has been inquiries made for a pilot study in the area. In view of this the local council of Oskarshamn appointed a ´Youth team´, consisting of ten young politicians from all political parties represented in the local council. The aim of the team was to inform and create debate among adolescents about how to store the radioactive waste from nuclear power plants. The purpose of this survey, addressed to young people in Oskarshamn, was to shed light on their opinion towards a pilot study and possible disposal of nuclear waste in Oskarshamn. The respondents had to answer questions about their opinion on the use of nuclear power in Sweden, if they believed nuclear power to be abolished by year 2010, and about their general interest in issues concerning energy and nuclear power. Other questions concerned risks associated with nuclear power, the influence different groups have/ought to have when it comes to disposal of nuclear waste, and if the respondent would accept a decision to dispose nuclear waste in Oskarshamn. A number of questions dealt with the suggested pilot study; if the respondent was for or against a pilot study; who should decide about the pilot study; if there had been enough information about the study; and if the respondent had attended any meeting, signed any petition, contacted any politician, contacted or participated in mass media, or tried to influence anyone´s opinion on any issue concerning the pilot study. The respondents also had to state the issues they considered to be important to study in a pilot study. Furthermore the respondents had to give their opinion about a number of risks discussed in connection with disposal of nuclear waste in Oskarshamn. Other questions concerned the influence on job opportunities and tourism. Demographic items include age, gender, marital status, children, education, occupation, and trade union membership. Oskarshamn is one of the municipalities being discussed as a possible site for disposal of nuclear waste from the Swedish nuclear power plants, and there has been inquiries made for a pilot study in the area. In view of this the local council of Oskarshamn appointed a 'Youth team', consisting of ten young politicians from all political parties represented in the local council. The aim of the team was to inform and create debate among adolescents about how to store the radioactive waste from nuclear power plants. The purpose of this survey, addressed to young people in Oskarshamn, was to shed light on their opinion towards a pilot study and possible disposal of nuclear waste in Oskarshamn. The respondents had to answer questions about their opinion on the use of nuclear power in Sweden, if they believed nuclear power to be abolished by year 2010, and about their general interest in issues concerning energy and nuclear power. Other questions concerned risks associated with nuclear power, the influence different groups have/ought to have when it comes to disposal of nuclear waste, and if the respondent would accept a decision to dispose nuclear waste in Oskarshamn. A number of questions dealt with the suggested pilot study; if the respondent was for or against a pilot study; who should decide about the pilot study; if there had been enough information about the study; and if the respondent had attended any meeting, signed any petition, contacted any politician, contacted or participated in mass media, or tried to influence anyone's opinion on any issue concerning the pilot study. The respondents also had to state the issues they considered to be important to study in a pilot study. Furthermore the respondents had to give their opinion about a number of risks discussed in connection with disposal of nuclear waste in Oskarshamn. Other questions concerned the influence on job opportunities and tourism. Demographic items include age, gender, marital status, children, education, occupation, and trade union membership.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Swedish National Dat...arrow_drop_down
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    Swedish National Data Service
    Dataset . 1998
    Data sources: Datacite
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Swedish National Data Service
    Dataset . 1998
    Data sources: Datacite
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Swedish National Dat...arrow_drop_down
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      Swedish National Data Service
      Dataset . 1998
      Data sources: Datacite
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      Swedish National Data Service
      Dataset . 1998
      Data sources: Datacite
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  • Authors: Tosato, Giacomo (ENEX); Artuso, Paolo (National Research Council, Construction Technologies Institute); Minetto, Silvia (National Research Council, Construction Technologies Institute); Rossetti, Antonio (National Research Council, Construction Technologies Institute); +2 Authors

    This dataset, in the context of the MultiPACK Project, describes the development of a CO2 air/water reversible heat pump, specifically investigating the domestic hot water (DHW) production operating mode. A dynamic model of the heat pump is developed with the software Simcenter Amesim. After validation against experimental data, the numerical model is utilized to predict the performance of the heat pump to varying hot water demand, evaporator air inlet conditions and high-pressure value, leading to the discussion of the optimal control strategy. A paper, based on this dataset, "Experimental and numerical investigation of a transcritical CO2 air/water reversible heat pump: analysis of domestic hot water production (14th Gustav Lorentzen Conference, Kyoto, Japan, 6th- 9th December 2020, DOI:10.18462/iir.gl.2020.1160).

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    DataverseNO
    Dataset . 2021
    Data sources: Datacite
    B2FIND
    Dataset . 2021
    Data sources: B2FIND
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      DataverseNO
      Dataset . 2021
      Data sources: Datacite
      B2FIND
      Dataset . 2021
      Data sources: B2FIND
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Minx, Jan C.; Lamb, William F.; Andrew, Robbie M.; Canadell, Josep G.; +13 Authors

    Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.

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    ZENODO
    Dataset . 2021
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/

    Outputs from the Network Innovation Allowance project "Control REACT" (workstream 2), sponsored by National Grid Electricity System Operator (NGESO). This deposit contains underlying data used in this project. The R code (Rmarkdown) and html renders of these workbooks are available in a separate deposit linked below. See description there for further details. In order to run the R scripts, data and code must be arranged in the directory structure given in "Directory Structure.pdf". Wind, solar and net-demand data are derived from raw data made available by Elexon and Solar Sheffield via public APIs. See respective websites for details, our processed (aggregated and cleaned) versions of this data are shared here under a CC-BY license. Weather forecast data are derived from historic operational forecasts from the ECMWF HRES model and are shared under a CC-BY licence. For details on how these were processed please see references. {"references": ["J. Browell and M. Fasiolo, \"Probabilistic Forecasting of regional net-load with conditional extremes and gridded NWP\", IEEE Transactions on Smart Grid, vol. 12, no, 6, pp. 5011-5019, 2021", "C. Gilbert \"Topics in high dimensional energy forecasting\", J. Browell & D. McMillan, degree supervisors; Centre for Doctoral Training in Wind and Marine Energy Systems; Department of Electronic and Electrical Engineering Thesis [PhD] 2021"]}

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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2022
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      ZENODO
      Dataset . 2022
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      ZENODO
      Dataset . 2022
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Pullinger, Martin; Few, Jessica; McKenna, Eoghan; Elam, Simon; +2 Authors

    This is a set of aggregated data tables that underly the key figures in the SERL stats report "Smart Energy Research Lab: Energy use in GB domestic buildings 2021" (Volume 1). The report describes domestic gas and electricity energy use in Great Britain in 2021 based on data from the Smart Energy Research Lab (SERL) Observatory, which consists of smart meter and contextual data from approximately 13,000 homes that are broadly representative of the GB population in terms of region and Index of Multiple Deprivation (IMD) quintile. The report shows how residential energy use in GB varies over time (monthly over the year and half-hourly over the course of the day), with occupant characteristics (number of occupants, tenure), property characteristics (age, size, form, and Energy Performance Certificate (EPC)), by type of heating system, presence of solar panels and of electric vehicles, and by weather, region and IMD quintile.

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    UCL Discovery
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    UCL Discovery
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
    B2FIND
    Dataset . 2022
    Data sources: B2FIND
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      UCL Discovery
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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      UCL Discovery
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
      B2FIND
      Dataset . 2022
      Data sources: B2FIND
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  • Authors: Soylemez, Engin (NTNU - Norwegian University of Science and Technology); Hafner, Armin (NTNU - Norwegian University of Science and Technology); Schlemminger. Christian (Sintef Energy Research); Kriezi, E., Ekaterini (Danfoss A/S); +1 Authors

    In this dataset, the field data from an integrated CO2 refrigeration system installed in a supermarket located north of the capital of Lisbon was shared. The scheme of the refrigerating system is provided in Figure 1-2, while the installed capacities and main components characteristics are listed in Table 1 and Table 2, respectively.The system can meet AC demand by direct evaporation in the air handling units (AHUs) units. Due to the summer season and warm ambient temperatures, AC is applied to meet the temperature set-point inside the shop (Figure 2). Moreover, compressor racks and AHU units are shown only with a single symbol. The system consists of the LT compressor rack (three semi-hermetic compressors), the MT compressor rack (four semi-hermetic compressors), and the parallel compressor (PC) rack (four semi-hermetic compressors) for AC; a gas cooler (GC); MEs; liquid receiver, MT suction line accumulator; LT and MT evaporators, expansion valves (EVs), oil recovery system, and two rooftop AHUs. For each compressor rack, one compressor is equipped with an inverter to allow smoother capacity modulation. The PCs are organized in such a way that they can manage different suction pressures according to heat pump functionality and/or PC. The ME blocks were sized for vapor pre-compression (HPE) according to the climate profile of the region and liquid return (LE) in the case of liquid leaving the MT evaporators. The AHU comprises two identical rooftop units. These units deliver the entire heating and cooling capacity of the supermarket. CO2 is directly applied inside the heating and cooling coils of the AHUs. SH demand can be covered seamlessly by means of a 3-way valve allowing high pressure CO2 gas supply to the heating coils in the AHU. An increasing high pressure and separate heat pump functionality can be utilized to cover high heating demands. In summertime, the AHU’s cooling coils can be operated in two different ways: the first alternative is the DX downstream of the GC, i.e., the refrigerant expands from the high-pressure side directly into the coils where it is evaporated and enters the liquid receiver. The second alternative is using a low-pressure lift high entrainment ratio ejector (AC ejector). The AC ejector sucks the whole vapor of the AC evaporators to compress it to the receiver pressure level. The first alternative was in operation during the time period analyzed in this study; thus, the effect of AC ejectors will not be mentioned in the following sections. High-pressure lift and liquid ejectors are applied to return both vapor and liquid from the suction line accumulator to the liquid receiver.

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    Dataset . 2022
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    B2FIND
    Dataset . 2022
    Data sources: B2FIND
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      Dataset . 2022
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      B2FIND
      Dataset . 2022
      Data sources: B2FIND
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Mitchell, Rachel; Natarajan, Sukumar;

    This dataset consists of hourly internal and daily external temperature data from 82 certified Passivhaus dwellings in the UK. The data can be used for calculating overheating risk and guaging how comfortable a home would be in the summer. This data come from 16 different sites and includes houses and flats. Some of the data is from the living room only, for other dwellings there were sensors in muitple rooms and these are indicated. As this data was compared to CIBSE TM59 "Design methodology for the assessment of overheating risk in homes", there is a calculation of the running mean temperature and maximum temperature. The variables are Timestamp = time and date SiteID = Site number (1-16) DWType = dwelling type (House or Flat) HouseID = unique reference number for each dwelling in dataset Room = room type LR = living room , BR= bedroom, KI= Kitchen, BT= bathroom T.int = internal temperature (mean hourly) T.ext.daily = external temperature (mean daily) T.rm = running mean temperature calculated using the method described in CIBSE TM59 T.max = maximum daily intenral temperature calculated using the method described in CIBSE TM59 This data was provided by the Technology Stratergy Board Building Performance Evaluation Program, and is available from the digital catapault. Other data was provided by WARM low energy Consultancy and indidiual home owners. All data has been anonymised

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    University of Bath Research Data Archive
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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      University of Bath Research Data Archive
      Dataset . 2020
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Sathre, Roger; Gustavsson, Leif;

    Heavy trucks contribute significantly to climate change, and in 2020 were responsible for 7% of total Swedish GHG emissions and 5% of total global CO2 emissions. Here we study the full lifecycle of cargo trucks powered by different energy pathways, comparing their biomass feedstock use, primary energy use, net biogenic and fossil CO2 emission, and cumulative radiative forcing. We analyse battery electric trucks with bioelectricity from standalone or combined heat and power (CHP) plants, and pathways where bioelectricity is integrated with wind and solar electricity. We analyse trucks operated on fossil diesel fuel and on dimethyl ether (DME). All energy pathways are analysed with and without carbon capture and storage (CCS). Bioelectricity and DME are produced from forest harvest residues. Forest biomass is a limited resource, so in a scenario analysis we allocate a fixed amount of biomass to power Swedish truck transport. Battery lifespan and chemistry, the technology level of energy supply, and the biomass source and transport distance are all varied to understand how sensitive the results are to these parameters. The scenario spans 100 years into the future. We find that pathways using electricity to power battery electric trucks have much lower climate impacts and primary energy use, compared to diesel and DME based pathways. The pathways using bioelectricity with CCS result in negative emissions leading to global cooling of the earth. The pathways using diesel and DME have significant and very similar climate impact, even with CCS. The robust results show that truck electrification and increased renewable electricity production is a much better strategy to reduce the climate impact of cargo transport and much more primary energy efficient than the adoption of DME trucks. This climate impact analysis includes all fossil and net biogenic CO2 emissions as well as the timing of these emissions. Considering only fossil emissions is incomplete and could be misleading. This dataset contains data on 4 metrics (primary energy use, biomass feedstock use, cumulative CO2 emissions, and cumulative radiative forcing) resulting from scenario modeling of cargo truck use in Sweden powered by different energy pathways. The energy pathways include battery electric trucks powered by bioelectricity, solar photovoltaic electricity and wind electricity, and internal combustion trucks powered by fossil diesel and dimethyl ether. The scenario spans 100 years into the future. The Excel sheet "tables" contains input data for the scenario modeling, with sources listed where applicable. The remaining sheets contains the modeled results and generated figures that are also a published in the associated article Sathre & Gustavsson (2023). Refer to the method description and reference list in the included documentation files for details. Tunga lastbilar bidrar kraftigt till klimatförändringarna och stod 2020 för 7% av de totala svenska växthusgasutsläppen och 5% av de totala globala CO2-utsläppen. Här studerar vi hela livscykeln för lastbilar som drivs av olika energivägar, jämför deras användning av biomassaråvaror, primär energianvändning, biogena och fossila CO2-utsläpp netto och kumulativ strålningstvingning. Vi analyserar batterielektriska lastbilar med bioel från fristående eller kraftvärmeverk och vägar där bioel integreras med vind- och solkraft. Vi analyserar lastbilar som drivs med fossilt dieselbränsle och med dimetyleter (DME). Alla energivägar analyseras med och utan avskiljning och lagring av koldioxid (CCS). Bioelektricitet och DME produceras av skogsavverkningsrester. Skogsbiomassa är en begränsad resurs, så i en scenarioanalys avsätter vi en fast mängd biomassa för att driva svenska lastbilstransporter. Batteriets livslängd och kemi, tekniknivån för energiförsörjning och biomassakällan och transportavståndet varierar alla för att förstå hur känsliga resultaten är för dessa parametrar. Scenariot sträcker sig 100 år in i framtiden. Vi finner att vägar som använder el för att driva batterielektriska lastbilar har mycket lägre klimatpåverkan och primär energianvändning, jämfört med diesel- och DME-baserade vägar. De vägar som använder bioelektricitet med CCS resulterar i negativa utsläpp som leder till global kylning av jorden. Vägarna med diesel och DME har betydande och mycket liknande klimatpåverkan, även med CCS. De robusta resultaten visar att elektrifiering av lastbilar och ökad förnybar elproduktion är en mycket bättre strategi för att minska godstransporternas klimatpåverkan än införandet av DME-lastbilar, och mycket mer primärenergieffektiv. Denna klimatkonsekvensanalys omfattar alla fossila och biogena CO2-utsläpp samt tidpunkten för dessa utsläpp. Att bara ta hänsyn till fossila utsläpp är ofullständigt och kan vara missvisande. Detta dataset innehåller data om 4 mätvärden (primär energianvändning, biomassaråvara, kumulativa CO2-utsläpp och kumulativ strålkraftspåverkan) som härrör från scenariomodellering av lastbilsanvändning i Sverige som drivs av olika energivägar. Energivägarna inkluderar batterielektriska lastbilar som drivs av bioelektricitet, solcellselektricitet och vindkraft samt förbränningsbilar som drivs av fossil diesel och dimetyleter. Scenariot sträcker sig 100 år in i framtiden. På arket "tables" i Excelfilen återfinns den indata som använts i modelleringen med angivna källor där detta är tillämpligt. Övriga ark innehåller resultat samt figurer som också publiceras i den samhörande artikeln Sathre & Gustavsson (2023). Se metodbeskrivning samt referenslista i tillhörande dokumentationsfiler för detaljer.

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    Swedish National Data Service
    Dataset . 2023
    Data sources: Datacite
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    Swedish National Data Service
    Dataset . 2023
    Data sources: Datacite
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      Swedish National Data Service
      Dataset . 2023
      Data sources: Datacite
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      Swedish National Data Service
      Dataset . 2023
      Data sources: Datacite
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    Authors: Fosas, Daniel; Nikolaidou, Elli; Roberts, Matt; Allen, Stephen; +2 Authors

    Dataset for the journal paper "Towards Active Buildings: rating grid-servicing buildings", which describes the simulations for the 20 case study buildings. The simulation inputs describe the intended characteristics as part of the early design stage process, and the outputs the performance metrics under the rating system introduced in the journal paper, called the ABCode1. Such outputs rate the relative merits of each case study in terms of embodied carbon, energy requirements, energy generation and energy flexibility. The simulation outputs have been generated using the inputs included in the dataset, which were then simulated in David Coley’s ZEBRA and then evaluated with the rating system proposed in the journal publication as part of ABCode1. The files are in the original Excel xlsx file (Microsoft Office 365), but it may be viewed by any other spread sheet tools such as LibreOffice's Calc.

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    University of Bath Research Data Archive
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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      University of Bath Research Data Archive
      Dataset . 2020
      License: CC BY
      Data sources: Datacite
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    Authors: Russell, Debbie J. F.; Hastie, Gordon D.; Thompson, David; Janik, Vincent M.; +6 Authors

    As part of global efforts to reduce dependence on carbon-based energy sources there has been a rapid increase in the installation of renewable energy devices. The installation and operation of these devices can result in conflicts with wildlife. In the marine environment, mammals may avoid wind farms that are under construction or operating. Such avoidance may lead to more time spent travelling or displacement from key habitats. A paucity of data on at-sea movements of marine mammals around wind farms limits our understanding of the nature of their potential impacts. Here, we present the results of a telemetry study on harbour seals Phoca vitulina in The Wash, south-east England, an area where wind farms are being constructed using impact pile driving. We investigated whether seals avoid wind farms during operation, construction in its entirety, or during piling activity. The study was carried out using historical telemetry data collected prior to any wind farm development and telemetry data collected in 2012 during the construction of one wind farm and the operation of another. Within an operational wind farm, there was a close-to-significant increase in seal usage compared to prior to wind farm development. However, the wind farm was at the edge of a large area of increased usage, so the presence of the wind farm was unlikely to be the cause. There was no significant displacement during construction as a whole. However, during piling, seal usage (abundance) was significantly reduced up to 25 km from the piling activity; within 25 km of the centre of the wind farm, there was a 19 to 83% (95% confidence intervals) decrease in usage compared to during breaks in piling, equating to a mean estimated displacement of 440 individuals. This amounts to significant displacement starting from predicted received levels of between 166 and 178 dB re 1 μPa(p-p). Displacement was limited to piling activity; within 2 h of cessation of pile driving, seals were distributed as per the non-piling scenario. Synthesis and applications. Our spatial and temporal quantification of avoidance of wind farms by harbour seals is critical to reduce uncertainty and increase robustness in environmental impact assessments of future developments. Specifically, the results will allow policymakers to produce industry guidance on the likelihood of displacement of seals in response to pile driving; the relationship between sound levels and avoidance rates; and the duration of any avoidance, thus allowing far more accurate environmental assessments to be carried out during the consenting process. Further, our results can be used to inform mitigation strategies in terms of both the sound levels likely to cause displacement and what temporal patterns of piling would minimize the magnitude of the energetic impacts of displacement. Wash_diagWash_diag.xlsx is the historic location data (pre windfarm construction) for the 19 individuals used in the analysis described in Russell et al.

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    ZENODO
    Dataset . 2017
    License: CC 0
    Data sources: ZENODO
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    B2FIND
    Dataset . 2016
    Data sources: B2FIND
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    EASY
    Dataset . 2016
    Data sources: EASY
    DRYAD
    Dataset . 2017
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2017
      License: CC 0
      Data sources: ZENODO
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      B2FIND
      Dataset . 2016
      Data sources: B2FIND
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      EASY
      Dataset . 2016
      Data sources: EASY
      DRYAD
      Dataset . 2017
      License: CC 0
      Data sources: Datacite
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Hedberg, Per; Sundquist, Göran;

    Oskarshamn is one of the municipalities being discussed as a possible site for disposal of nuclear waste from the Swedish nuclear power plants, and there has been inquiries made for a pilot study in the area. In view of this the local council of Oskarshamn appointed a ´Youth team´, consisting of ten young politicians from all political parties represented in the local council. The aim of the team was to inform and create debate among adolescents about how to store the radioactive waste from nuclear power plants. The purpose of this survey, addressed to young people in Oskarshamn, was to shed light on their opinion towards a pilot study and possible disposal of nuclear waste in Oskarshamn. The respondents had to answer questions about their opinion on the use of nuclear power in Sweden, if they believed nuclear power to be abolished by year 2010, and about their general interest in issues concerning energy and nuclear power. Other questions concerned risks associated with nuclear power, the influence different groups have/ought to have when it comes to disposal of nuclear waste, and if the respondent would accept a decision to dispose nuclear waste in Oskarshamn. A number of questions dealt with the suggested pilot study; if the respondent was for or against a pilot study; who should decide about the pilot study; if there had been enough information about the study; and if the respondent had attended any meeting, signed any petition, contacted any politician, contacted or participated in mass media, or tried to influence anyone´s opinion on any issue concerning the pilot study. The respondents also had to state the issues they considered to be important to study in a pilot study. Furthermore the respondents had to give their opinion about a number of risks discussed in connection with disposal of nuclear waste in Oskarshamn. Other questions concerned the influence on job opportunities and tourism. Demographic items include age, gender, marital status, children, education, occupation, and trade union membership. Oskarshamn is one of the municipalities being discussed as a possible site for disposal of nuclear waste from the Swedish nuclear power plants, and there has been inquiries made for a pilot study in the area. In view of this the local council of Oskarshamn appointed a 'Youth team', consisting of ten young politicians from all political parties represented in the local council. The aim of the team was to inform and create debate among adolescents about how to store the radioactive waste from nuclear power plants. The purpose of this survey, addressed to young people in Oskarshamn, was to shed light on their opinion towards a pilot study and possible disposal of nuclear waste in Oskarshamn. The respondents had to answer questions about their opinion on the use of nuclear power in Sweden, if they believed nuclear power to be abolished by year 2010, and about their general interest in issues concerning energy and nuclear power. Other questions concerned risks associated with nuclear power, the influence different groups have/ought to have when it comes to disposal of nuclear waste, and if the respondent would accept a decision to dispose nuclear waste in Oskarshamn. A number of questions dealt with the suggested pilot study; if the respondent was for or against a pilot study; who should decide about the pilot study; if there had been enough information about the study; and if the respondent had attended any meeting, signed any petition, contacted any politician, contacted or participated in mass media, or tried to influence anyone's opinion on any issue concerning the pilot study. The respondents also had to state the issues they considered to be important to study in a pilot study. Furthermore the respondents had to give their opinion about a number of risks discussed in connection with disposal of nuclear waste in Oskarshamn. Other questions concerned the influence on job opportunities and tourism. Demographic items include age, gender, marital status, children, education, occupation, and trade union membership.

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    Swedish National Data Service
    Dataset . 1998
    Data sources: Datacite
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    Swedish National Data Service
    Dataset . 1998
    Data sources: Datacite
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      Swedish National Data Service
      Dataset . 1998
      Data sources: Datacite
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      Swedish National Data Service
      Dataset . 1998
      Data sources: Datacite
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  • Authors: Tosato, Giacomo (ENEX); Artuso, Paolo (National Research Council, Construction Technologies Institute); Minetto, Silvia (National Research Council, Construction Technologies Institute); Rossetti, Antonio (National Research Council, Construction Technologies Institute); +2 Authors

    This dataset, in the context of the MultiPACK Project, describes the development of a CO2 air/water reversible heat pump, specifically investigating the domestic hot water (DHW) production operating mode. A dynamic model of the heat pump is developed with the software Simcenter Amesim. After validation against experimental data, the numerical model is utilized to predict the performance of the heat pump to varying hot water demand, evaporator air inlet conditions and high-pressure value, leading to the discussion of the optimal control strategy. A paper, based on this dataset, "Experimental and numerical investigation of a transcritical CO2 air/water reversible heat pump: analysis of domestic hot water production (14th Gustav Lorentzen Conference, Kyoto, Japan, 6th- 9th December 2020, DOI:10.18462/iir.gl.2020.1160).

    DataverseNOarrow_drop_down
    DataverseNO
    Dataset . 2021
    Data sources: Datacite
    B2FIND
    Dataset . 2021
    Data sources: B2FIND
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      Dataset . 2021
      Data sources: Datacite
      B2FIND
      Dataset . 2021
      Data sources: B2FIND
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    Authors: Minx, Jan C.; Lamb, William F.; Andrew, Robbie M.; Canadell, Josep G.; +13 Authors

    Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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    Outputs from the Network Innovation Allowance project "Control REACT" (workstream 2), sponsored by National Grid Electricity System Operator (NGESO). This deposit contains underlying data used in this project. The R code (Rmarkdown) and html renders of these workbooks are available in a separate deposit linked below. See description there for further details. In order to run the R scripts, data and code must be arranged in the directory structure given in "Directory Structure.pdf". Wind, solar and net-demand data are derived from raw data made available by Elexon and Solar Sheffield via public APIs. See respective websites for details, our processed (aggregated and cleaned) versions of this data are shared here under a CC-BY license. Weather forecast data are derived from historic operational forecasts from the ECMWF HRES model and are shared under a CC-BY licence. For details on how these were processed please see references. {"references": ["J. Browell and M. Fasiolo, \"Probabilistic Forecasting of regional net-load with conditional extremes and gridded NWP\", IEEE Transactions on Smart Grid, vol. 12, no, 6, pp. 5011-5019, 2021", "C. Gilbert \"Topics in high dimensional energy forecasting\", J. Browell & D. McMillan, degree supervisors; Centre for Doctoral Training in Wind and Marine Energy Systems; Department of Electronic and Electrical Engineering Thesis [PhD] 2021"]}

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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2022
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    Data sources: Datacite
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2022
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      ZENODO
      Dataset . 2022
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      Data sources: Datacite
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      ZENODO
      Dataset . 2022
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    Authors: Pullinger, Martin; Few, Jessica; McKenna, Eoghan; Elam, Simon; +2 Authors

    This is a set of aggregated data tables that underly the key figures in the SERL stats report "Smart Energy Research Lab: Energy use in GB domestic buildings 2021" (Volume 1). The report describes domestic gas and electricity energy use in Great Britain in 2021 based on data from the Smart Energy Research Lab (SERL) Observatory, which consists of smart meter and contextual data from approximately 13,000 homes that are broadly representative of the GB population in terms of region and Index of Multiple Deprivation (IMD) quintile. The report shows how residential energy use in GB varies over time (monthly over the year and half-hourly over the course of the day), with occupant characteristics (number of occupants, tenure), property characteristics (age, size, form, and Energy Performance Certificate (EPC)), by type of heating system, presence of solar panels and of electric vehicles, and by weather, region and IMD quintile.

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    UCL Discovery
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    UCL Discovery
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
    B2FIND
    Dataset . 2022
    Data sources: B2FIND
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      UCL Discovery
      Dataset . 2022
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      UCL Discovery
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
      B2FIND
      Dataset . 2022
      Data sources: B2FIND
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  • Authors: Soylemez, Engin (NTNU - Norwegian University of Science and Technology); Hafner, Armin (NTNU - Norwegian University of Science and Technology); Schlemminger. Christian (Sintef Energy Research); Kriezi, E., Ekaterini (Danfoss A/S); +1 Authors

    In this dataset, the field data from an integrated CO2 refrigeration system installed in a supermarket located north of the capital of Lisbon was shared. The scheme of the refrigerating system is provided in Figure 1-2, while the installed capacities and main components characteristics are listed in Table 1 and Table 2, respectively.The system can meet AC demand by direct evaporation in the air handling units (AHUs) units. Due to the summer season and warm ambient temperatures, AC is applied to meet the temperature set-point inside the shop (Figure 2). Moreover, compressor racks and AHU units are shown only with a single symbol. The system consists of the LT compressor rack (three semi-hermetic compressors), the MT compressor rack (four semi-hermetic compressors), and the parallel compressor (PC) rack (four semi-hermetic compressors) for AC; a gas cooler (GC); MEs; liquid receiver, MT suction line accumulator; LT and MT evaporators, expansion valves (EVs), oil recovery system, and two rooftop AHUs. For each compressor rack, one compressor is equipped with an inverter to allow smoother capacity modulation. The PCs are organized in such a way that they can manage different suction pressures according to heat pump functionality and/or PC. The ME blocks were sized for vapor pre-compression (HPE) according to the climate profile of the region and liquid return (LE) in the case of liquid leaving the MT evaporators. The AHU comprises two identical rooftop units. These units deliver the entire heating and cooling capacity of the supermarket. CO2 is directly applied inside the heating and cooling coils of the AHUs. SH demand can be covered seamlessly by means of a 3-way valve allowing high pressure CO2 gas supply to the heating coils in the AHU. An increasing high pressure and separate heat pump functionality can be utilized to cover high heating demands. In summertime, the AHU’s cooling coils can be operated in two different ways: the first alternative is the DX downstream of the GC, i.e., the refrigerant expands from the high-pressure side directly into the coils where it is evaporated and enters the liquid receiver. The second alternative is using a low-pressure lift high entrainment ratio ejector (AC ejector). The AC ejector sucks the whole vapor of the AC evaporators to compress it to the receiver pressure level. The first alternative was in operation during the time period analyzed in this study; thus, the effect of AC ejectors will not be mentioned in the following sections. High-pressure lift and liquid ejectors are applied to return both vapor and liquid from the suction line accumulator to the liquid receiver.

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    Dataset . 2022
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    B2FIND
    Dataset . 2022
    Data sources: B2FIND
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      Dataset . 2022
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      B2FIND
      Dataset . 2022
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    Authors: Mitchell, Rachel; Natarajan, Sukumar;

    This dataset consists of hourly internal and daily external temperature data from 82 certified Passivhaus dwellings in the UK. The data can be used for calculating overheating risk and guaging how comfortable a home would be in the summer. This data come from 16 different sites and includes houses and flats. Some of the data is from the living room only, for other dwellings there were sensors in muitple rooms and these are indicated. As this data was compared to CIBSE TM59 "Design methodology for the assessment of overheating risk in homes", there is a calculation of the running mean temperature and maximum temperature. The variables are Timestamp = time and date SiteID = Site number (1-16) DWType = dwelling type (House or Flat) HouseID = unique reference number for each dwelling in dataset Room = room type LR = living room , BR= bedroom, KI= Kitchen, BT= bathroom T.int = internal temperature (mean hourly) T.ext.daily = external temperature (mean daily) T.rm = running mean temperature calculated using the method described in CIBSE TM59 T.max = maximum daily intenral temperature calculated using the method described in CIBSE TM59 This data was provided by the Technology Stratergy Board Building Performance Evaluation Program, and is available from the digital catapault. Other data was provided by WARM low energy Consultancy and indidiual home owners. All data has been anonymised

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    University of Bath Research Data Archive
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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      University of Bath Research Data Archive
      Dataset . 2020
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