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14 results for “Heat Demand”
Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyvaskyla for 2030 and 2050
<p>******************* Please view the README.txt or README.md file for detailed documentation of data. ********************</p> <p>Title: Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyväskylä for 2030 and 2050</p> <p>Date of release: 25/11/2020</p> <p>Identifier: 10.5281/zenodo.4275759</p> <p>Permalink: http://dx.doi.org/10.5281/zenodo.4275759</p> <p>Associated publication: Hietaharju, P.; Louis, J.-N.; Pulkkinen, J.; Ruusunen, M. Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model, <strong><em>Under Review</em></strong>, 2020.</p> <p>Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README.txt and README.md files.</p> <p><br> Contact information: Jari Pulkkinen, University of Oulu, Oulu, Finland, jari.pulkkinen@oulu.fi; Jean-Nicolas Louis, University of Oulu, Oulu, Finland, jean-nicolas.louis@oulu.fi<br> </p> <p>Dates of data: 2030, 2050</p> <p>Type of data: Outdoor Temperature</p> <p>Geographic location: Jyväskylä</p> <p>Time resolution: hourly, full year</p> <p>Format: All data is stored in .csv files</p> <p>Number of files: 1 .zip --> 50 files + README.txt + README.md</p> <p>This directory contains the following datasets: A summary of all the files has been compiled and stored in the "README.txt" and "README.md" files</p> <p> </p> <p>Notifications:</p> <p>Contains modified Copernicus Climate Change Service (C3S) information [2018] and modified Finnish Meteorological Institute [2017,2019] information from etsin.fairdata.fi and from Open data repository (https://en.ilmatieteenlaitos.fi/open-data).</p> <p><br> Contains modified Climate One Building information [2019] (reference Lawrie L.K. and Crawley D.B. 2019) and Test Reference Year 2012 (TRY2012) information from Jylhä et al. [2011] and Jylhä et al. [2015] (Energy demand for the heating and cooling of residential houses in Finland in a changing climate).</p> <p>Contains modified Ruosteenoja et al. [2016] information.</p> <p>Other data and information sources are described in README.txt, README.md, references and on the associated publication.</p>
Simulated heating energy demand for two residential neighbourhoods
<p>The large-scale and comprehensive artificial dataset introduced in this research reflects the energy demands of two neighbourhoods and with some reasonable limitations mimics monitoring campaigns otherwise collected on-site from buildings in use. The monitoring campaigns are created using white-box simulation models for single-family houses representing typical neighbourhoods in Flanders. The datasets are generated using Dymola and the IDEAS package embedded in TEASER. Each house varies in geometry, size, envelope properties, occupancy schedules, and installed gas heating systems. In this research, two datasets are created, one reflecting the properties of a low-performing building stock dating before the introduction of the EPBD (2006), and the other reflecting properties of a well-performing stock built after 2006. The envelope properties for older houses are allocated using EPC data grouped in four construction periods, while for newly built houses the properties are based on EPB reports, both were collected in Flanders. The datasets include heavy-weight houses in a detached, semi-detached, or terraced typology. Furthermore, the houses are simulated as one or two-zone buildings, depending on the number of floors which range from one to three floors. In the simulations, a natural infiltration model is implemented as well as a stochastic occupant behaviour model mimicking gains from occupants and appliances. Due to the complexity of the large-scale simulation, the heating system is post-processed in a data-driven approach and the heat source for both datasets are gas-fired heating systems. In total six system configurations are considered including condensing and non-condensing boilers with three types of domestic hot water (DHW) sub-systems (no integrated DHW, direct and with a storage tank). For all configurations, a variable production efficiency is considered dependent on the load ratio. The urban-scale simulation is carried out at a 10-minute frequency for the weather data assuming the location of Heverlee (Belgium) in the year 2016.<br>The original purpose of this dataset was the development of statistical tools for the assessment of the heat loss coefficient of the building fabric. However, the generated artificial datasets provide a large spectre of usually difficult-to-measure inputs suitable to assess the importance of different components in the overall energy balance. Even though the original work looked into individual building behaviour, the datasets can be also used from an urban perspective for energy planning purposes.</p>
Dataset of future district heating energy demand in a Finnish municipality
<p>******************* Please view the README.md file for detailed documentation of data. ********************</p> <p>Title: Impact of climate change, energy efficiency and population on long-term heat demand scenarios in districts: Datasets and Supplementary Materials Version: 1.0</p> <p>Date of Release: 28/10/2020 Identifier: doi:10.5281/zenodo.4139299 Permalink: http://dx.doi.org/10.5281/zenodo.4139299</p> <p>Associated publication: Hietaharju, P.; Louis, J.-N.; Pulkkinen, J. & Ruusunen, M. Impact of climate change, energy efficiency and population on long-term heat demand scenarios in districts <em>Under Review, </em> <strong>2020</strong></p> <p>Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README file. Contact information: Jean-Nicolas Louis, University of Oulu, Oulu, Finland, jean-nicolas.louis@oulu.fi or jeannicolas.louis@gmail.com</p> <p>Dates of data modelisation: 2013 - 2030 - 2050</p> <p>Geographic location: Jyväskylä</p> <p>Time resolution: Hourly, heating season.</p> <p>Types: Input data (all input configuration data are freely available, but dataset related to the district heating network and buildings are not distributed and not shareable for copyright reasons), power, temperature</p> <p>Format: All data are stored in .mat file format (MatLab file). </p> <p>This directory contains the following datasets and supplementary materials: A summary of all the files has been compiled and stored in the "READ ME.md" or "READ ME.html" file</p>
Supplementary material to the manuscript: Regionalised Heat Demand and Power-To-Heat Capacities in Germany - An Open Data Set for Assessing Renewable Energy Integration
<p>This is the supplementary material for the manuscript:</p> <p>"Regionalised Heat Demand and Power-To-Heat Capacities in Germany - an Open Data Set for Assessing Renewable Energy Integration"</p> <p>Article DOI: <a href="https://doi.org/10.1016/j.apenergy.2019.114161">https://doi.org/10.1016/j.apenergy.2019.114161</a></p> <p>Open access preprint: <a href="https://arxiv.org/abs/1912.03763">https://arxiv.org/abs/1912.03763</a></p> <p> </p> <p><strong>DESCRIPTION OF THE DATASET AND LICENSES:</strong></p> <p>The subdirectory "04_results" contains the regionalised heat demand an power-to-heat capacity data on administrative district level (NUTS-3) for Germany. The subdirectories "01_census_special_evaluation_data" and "02_other_input_data" contain the utilised input data. The subdirectory "03_code" contains the developed and applied source code.</p> <p>The data in this repository are provided under open source licenses. For license information and other general information on the supplementary material, refer to the LICENSE files and README files in the respective subdirectories.</p> <p>For a detailed description of the approach developed by the author, the input data used and the generated results, refer to the manuscript "Regionalised Heat Demand and Power-To-Heat Capacities in Germany - an Open Data Set for Assessing Renewable Energy Integration".</p> <p><strong>METADATA:</strong></p> <p>Sector: Residential Buildings – Space Heating and Domestic Hot Water</p> <p>Geographical scope: Germany</p> <p>Geographical resolution: Administrative districts (NUTS-3)</p> <p>Temporal scope: 2011, three scenarios for 2030</p> <p>Temporal resolution: 15min</p> <p> </p> <p><strong>UNITS:</strong></p> <p>In the final results folders (04_results/01_installed_heating_p2h_capacity; 04_results/02_daily_time_series; 04_results/03_yearly_time_series) the units of the data are indicated in the file names or the column names, e.g. by "in_MW". In case of unit indication in the file name, the unit refers to all columns in the file.</p> <p>In the intermediate results folder (04_results/00_sql_tables_exported_to_csv) all units referring to power are "kW" and all units referring to energy are "kWh".</p> <p><strong>NEWS AND CONTACT:</strong></p> <p>This dataset will be used as part of the <a href="https://wiki.openmod-initiative.org/wiki/Region4FLEX">region4FLEX model</a>. We are currently enhancing the data by temporally and spatially resolved COP time series and determining load shifting potentials. If you wish to receive news or have general questions please contact: wilko.heitkoetter@dlr.de. </p>
2021 electricity and heat demand data for a city district (Belgium)
<p>Dataset of electricity and heat demand in a city district in Belgium. For the time period of the data, the district was still under construction and no full inhabitation of the buildings was present. Electricity data include electricity demand for:</p> <ul> <li>Individual households</li> <li>EV charging stations</li> <li>Decentralised waste water treatment</li> <li>Heat pump</li> <li>District heating pumps</li> <li>Vacuum network pumps</li> <li>Miscellaneous</li> </ul> <p>'Total' in the electricity dataset (ElectricPower) refers to the sum of the separate time series. 'Total measured' is a measurement of the total electricity use (in W). Data is averaged out over 15 minutes and expressed in kiloWatt before June 6, in Watt after that date. The electricity demand for the individual households (ElectricPowerPrivateUnits) is expressed in Watt for the complete period.</p> <p>The heat demand data (HeatDemand) describes the heat demand of the complete district, i.e. all private living units as well as common areas, office buildings, sports hall... Like electricity demand data, heat demand data is averaged out over 15 minutes and expressed in kiloWatt before June 6, in Watt after that date.</p> <p> </p> <div> </div>
PECD consistent heat demand and COP time series from when2heat
<p>This dataset uses the when2heat package to compute heat demand and COP time series. The data covers the years 1979 to 2019 and the sub-national level down to a resolution grouping Europe into 96 clusters based on ERA5 reanalysis data. Thanks to this temporal scope and spatial resolution, the data is consistent with the Pan-European Climatic Database (PECD) and enables multi-year analysis of multi-sector energy systems that include the heating sector.</p> <p>The "main data" folder provides the time series with COPs for various heat pump technologies and heat demand. The heat demand data deviates from the immediate when2heat outputs in two ways. First, it aggregates the demand for space heating and warm water in commercial and residential buildings into a single time series. The compositionHeatDemand.xlsx in the "background info" folder provides the weights assumed for this aggregation. Since the composition of heat demand changes in the future, the data includes a different time series for every fifth year from 2015 to 2050. Second, we normalize each profile over the historical years from 1979 to 2019 to its maximum value. Accordingly, multiplying the time series with maximum heat demand will give an absolute demand profile.</p> <p>A subfolder in "background_info" provides shape files describing the different spatial resolutions used. The "mappingRegions.csv" specifies which region names in this dataset correspond to region names in the PECD data. The "heating_thresholds.csv" in the "background info" folder provides the assumed heating thresholds to compute the heating demand.</p>
2022 electricity and heat demand data for a city district (Belgium)
<p>Dataset of electricity and heat demand in 2022 in a city district in Belgium (similar data for 2021 is available on Zenodo as well, https://doi.org/10.5281/zenodo.5155659). For the time period of the data, the district was still under construction and no full inhabitation of the buildings was present. Electricity data include electricity demand for:</p> <ul> <li>Individual households</li> <li>EV charging stations</li> <li>Decentralised waste water treatment</li> <li>Heat pump</li> <li>District heating pumps</li> <li>Vacuum network pumps</li> <li>Miscellaneous</li> </ul> <p>'Total' in the electricity dataset (ElectricPower) refers to the sum of the separate time series. 'Total measured' is a measurement of the total electricity use (in W). Data is averaged out over 15 minutes and expressed in Watt. </p> <p>The electricity demand for the individual households (ElectricPowerPrivateUnits) is expressed in Watt for the complete period. Column names have the form x.y in which x is a random number assigned to an apartment and y refers to the electricity consumption during the day (1) or at night (2).</p> <p>The heat demand data (HeatDemand) describes the heat demand of the complete district, i.e. all private living units as well as common areas, office buildings, sports hall... Like electricity demand data, heat demand data is averaged out over 15 minutes and expressed in Watt.</p> <div> </div>
openENTRANCE - Case Study 7 - Power-to-Heat Demand Flexibility: Results
<p>In case study 7 of the openENTRANCE project, Plan4EU, an electricity dispatch model for Europe, is soft-linked to the Flexibility Function, an indirect demand response model, via Frigg, a novel framework for integrating realistic demand response in energy system analysis. This modelling setup is applied to analyse the role of power-to-heat demand flexibility (end-consumer demand response and heat storage) in the Danish electricity system of 2050. This dataset contains results as presented in the related project report.</p>
Supplementary data: "Open modeling of electricity and heat demand curves for all residential buildings in Germany"
<p>This repository contains supplementary data for the paper <a href="https://doi.org/10.1186/s42162-022-00201-y"><em> "Open modeling of electricity and heat demand curves for all residential buildings in Germany"</em></a>.</p> <p>See <em>README.md</em> / <em>README.pdf</em> for further details.</p> <p><strong>Citing</strong></p> <p>Please cite as:</p> <p><em>Büttner, C., Amme, J., Endres, J. et al. Open modeling of electricity and heat demand curves for all residential buildings in Germany. Energy Inform 5 (Suppl 1), 21 (2022).</em></p> <p><strong>Funding</strong></p> <p>The authors thank the Federal Ministry for Economic Affairs and Climate Action for funding the research project eGon (funding code: 03EI1002).</p> <p> </p>
Global heat map of probable importance of terrestrial ecosystems on meeting local demand of freshwater services
<p>This map (raster dataset, single layer) uses existing datasets to map globally “How important point x is likely to be for meeting the demand of a reliable & useable source of water on a scale of 0 to 1?” This relatively simple approach uses estimated water demand in a given basin as weight to identify pressure for flow regulation and water provisioning services. Precipitation and land cover estimates are then combined with it to give some insight into the hydrologic attributes of “location” and “timing” of flow that the ecosystems may influence. The underlying assumption here is that undisturbed ecosystems everywhere are performing the ecohydrological functions leading to freshwater services. The question is more (at the global scale): how dependent are the populations in the basin on the continued functioning of these services.</p> <p><strong>Input datasets:</strong></p> <ol> <li>Annual surface & groundwater (“blue”) water consumption estimates. URL: <a href="http://waterfootprint.org/en/resources/water-footprint-statistics/">http://waterfootprint.org/en/resources/water-footprint-statistics/</a></li> <li>HydroBasins watershed outline.</li> <li>European Space Agency (ESA) global land cover 2015.</li> <li>WorldClim annual average precipitation (Version 2.0).</li> </ol> <p><strong>Process:</strong></p> <p>Step 1: Calculate average annual water consumption estimates over HydroBasin outlines. This step spreads the demand laterally (in case of small basins) and upstream to the headwaters from (typically) downstream consumer concentration.</p> <p>Step 2: Normalize the demand globally and map the normalized values on to “natural” land cover classes from the land cover dataset [forests, grasslands, etc].</p> <p>Step 3: Normalize annual precipitation layer within basins on the scale 0-1 where 1 is the maximum annual precipitation in that basin. This is also mapped on the “natural” land cover. Precipitation is thus acting as ‘weight’ for importance within the basin. Example, upland headwaters will typically receive more rainfall and can be argued to be important for the flow regulation in the basin.</p> <p>Step 4: Combine the layers from 2 and 3.</p> <p><strong>Caveats:</strong></p> <ol> <li>Identification of what constitutes a “natural” land cover is not trivial, especially from global land cover maps. Example: Forests and plantations are hard to distinguish from these products.</li> <li>Improvement of quality of water is assumed to be implicit for functioning ecosystems.</li> </ol>
Heat demand profiles from the When2Heat project repository
Open the record for dataset details and reuse information.
SPARCS_WP4_Leipzig_City_Maximum daily peak of heating demand
<p>The maximum daily peak of heating demand in the city of Leipzig measured in MW of all days with data captured annually covering the period between 2019 and 2023.</p>
SPARCS_WP4_Leipzig_City_Demand of heat
<p>The municipal demand of heat measured in MWh for the city of Leipzig with annually captured data covering the period between 2016 and 2020.</p>
Predicting Responses to Exhausting, Prolonged, And Repeated Exercise Demands (PREPARED) for Heat
ClinicalTrials.gov study NCT06475339. IPD Sharing: NO. Countries: 1. Publications: 0.
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