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17 results for “District heating”
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>
European cities with Geothermal District Heating and conventional District Heating - GeoDH project
<p>The dataset includes two shapefiles showing the location data for cities across Europe that use Geothermal District Heating and conventional District Heating. <br><br>This dataset was developed for assessing the potential of Geothermal District Heating in Europe as part of the <strong>GeoDH project</strong> (<a href="http://geodh.eu/" target="_new" rel="noopener">http://geodh.eu/</a>). Please note that this represents the<strong> state of the art as of 2014</strong> and that geological, technological, and regulatory developments may have occurred since its creation, and users should verify if more recent data is available for their purposes. <br><br></p>
District heating network data for the city of Flensburg from 2014-2016
<p>The data package contains flow temperatures and the overall heat load for the district heating network of Flensburg, Germany for the years 2014-2016.</p>
District Heating Study Results (TIMES Ireland Model) 2023
<p>This is the set of District Heating Results used in a study. The results are available to view on a web-based platform also: https://epmg.netlify.app/Jason_DH/results/ </p> <p>The results are in CSV format. </p>
Network Data of the District Heating System for the city of Sønderborg from 2016-2019
<p>The data set contains measurement data for heat load, as well as feed and return flow temperatures, from seven plants for the years 2016-2019 with a 15-minute time resolution. The heating plants belong to the district heating systems of Sønderborg, Denmark.</p>
Geological areas of interest for Geothermal District Heating utilization in Europe - GeoDH project
<p>The dataset includes four shapefiles showing the location data of geological areas of interest for Geothermal District Heating, including hot sedimentary aquifers and Neogene basins. The hot sedimentary aquifers layer represents areas where Neogene basin contours (sourced from the IGME Europe geological map at a scale of 1:5,000,000) overlap with regions where subsurface temperatures exceed 50°C at 1000m depth and/or 100°C at 2000m depth.<br><br>This dataset was developed for assessing the potential of Geothermal District Heating in Europe as part of the <strong>GeoDH project</strong> (<a href="http://geodh.eu/" target="_new" rel="noopener">http://geodh.eu/</a>). Please note that this represents the<strong> state of the art as of 2014</strong> and that geological, technological, and regulatory developments may have occurred since its creation, and users should verify if more recent data is available for their purposes.</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>
Economic potentials of district heating in Austria
<p>This dataset includes result data on assessing the economic potential of district heating potentials in Austria.</p> <p>Following elements are included:</p> <ul> <li>District_Heating_Grids.csv: Data on district heating grids in Austria by grid</li> <li>Industrial_waste_heat_potentials.csv: Data on industrial waste heat potentials in Austria for two different temperature levels (T1: >= 100°C, T2: < 100°C) by industrial site</li> <li>Power_Plants.csv: Data on thermal power plants and CHP plants in Austria by site</li> <li>Non_economic_dh_areas.csv: Share of district heating areas with higher costs for district heating than for the decentral heat supply mix; FP-financial perspective; EP-economic perspective</li> <li>Economic_DH-Pot.csv: Aggregated economic district heating potential compared to total space heating and hot water demand; FP-financial perspective; EP-economic perspective</li> <li>Heat_supply.csv: Aggregated economic heat supply by energy carrier year; FP-financial perspective; EP-economic perspective</li> <li>DH-Pot-Areas: spatial specification of the concrete district heating areas</li> <li>DH-Pot-AT_Readme.txt: Readme-file including</li> </ul>
District heating modelling data for the publication "Integration of feed flow temperatures in unit commitment models of future district heating systems"
<p>Modelling data for a district heating system model which has been used for the publication "Integration of feed flow temperatures in unit commitment models of future district heating systems" on the 4th Generation District Heating (4GDH) conference 2018.</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>
District Heating Model
<p>This dataset contains all the files used to run the District Heating Study in TIMES Ireland Model (TIM). </p>
Strategic Integration of Urban Excess Heat Sources in District Heating: A Spatio-Temporal Optimization Paradigm
<p>This data repository supports the techno-economic and geospatial optimization analysis of district heating systems (DHS) in Stockholm, with a particular focus on integrating Urban Excess Heat (UEH) sources. The dataset includes both input data and results, organized by the key analytical tools employed in the study: GIS, OSeMOSYS, and the Hotmaps Dispatch Model.</p> <p>The <strong>folder structure</strong> is organized as follows:</p> <p>/Data<br>│<br>├── Input_Data/<br>│ ├── GIS/<br>│ ├── OSeMOSYS/<br>│ ├── Hotmaps_Dispatch/<br>│<br>└── Results/<br> ├── GIS/<br> │ ├── Optimized DHS network extensions for UEH sources<br> │ ├── Long-term capacity and emissions projections for DHS<br> ├── Dispatch outputs for different electricity price scenarios<br><br></p> <h2>Dataset Details</h2> <p>1. Input Data Folder: This folder includes all data used as inputs across the three primary tools:<br> - GIS: Contains geospatial data on the DHS network paths, source locations, and spatial configurations for network optimization.<br> - OSeMOSYS: Provides techno-economic parameters for heat generation technologies, including capital and operational costs, fuel price projections, and emissions factors.<br> - Hotmaps Dispatch: Includes detailed demand profiles, projected electricity prices, and the supply temperature requirements essential for dispatch simulations.</p> <p>2. Results Folder: This folder contains the results generated by each tool after processing the input data:<br> - GIS: Outputs optimized paths for network expansion and geospatial mapping of UEH sources.<br> -OSeMOSYS: Contains projections for DHS technology adoption, emissions reduction, and cost optimization over a 23-year model period.<br> - Hotmaps Dispatch: Stores dispatch model results under various electricity pricing and temperature scenarios, capturing the optimal deployment of DHS resources.</p> <h2>Summary of Tools Used</h2> <p>- GIS (Geospatial Information System): Used to locate UEH sources and map their proximity to the existing DHS network, guiding the optimization of network expansions.<br>- OSeMOSYS (Open Source Energy Modelling System): Performs long-term optimization for the DHS, factoring in the addition of UEH sources, electricity price scenarios, and emissions constraints.<br>- Hotmaps Dispatch Model: Simulates operational dispatch of heat generation technologies in response to varying electricity prices and network temperatures, providing insights into real-time heat supply optimization.</p> <p>This repository thus provides a comprehensive dataset to support the techno-economic and spatial analysis of DHS, facilitating the integration of UEH sources in urban heating systems through scenario-based optimizations.</p> <p><br><br></p>
SPARCS_WP3_Espoo_City_District heating production by fuel in Espoo, Finland
<p>District heat production in Espoo, Finland, divided by fuel. Provided by the Helsinki Region Environmental Authority. 2000-2023</p>
Data set on district heating potentials in EU-27 countries
<p>The provided data set includes the input data for assessment of the district heating potential in EU-27 countries under evolving DH market shares and ambitious heat demand reduction scenario. The data set also includes the output data of the assessment obtained based on the input data from the following sources:</p> <ul> <li>Best-Case scenario from: <ul> <li>"<em>L. Kranzl et al., Renewable space heating under the revised Renewable Energy Directive:<br> ENER/C1/2018 494 : final report. LU: Publications Office of the European Union, 2022 [Online].<br> Available: https://data.europa.eu/doi/10.2833/525486. [Accessed: Jul. 29, 2022]</em>"</li> </ul> </li> <li>BL2050 scenario from sEEnergies project: <ul> <li>"<em>B. Möller, E. Wiechers, L. Sánchez-García, and U. Persson, ‘Spatial models and spatial analytics results’, Mar. 2022, doi: 10.5281/zenodo.6524594. [Online]. Available: https://zenodo.org/record/6524594. [Accessed: Dec. 14, 2022]</em>"</li> </ul> </li> </ul>
Results of article : "Prospective European District Heating Scenarios based on Geographical Analysis"
<p>Results of the paper "Prospective European District Heating scenarios based on geographical analysis".</p> <p>Three scenarios are generated : Ambitious, Circular, and Conservative. For each scenario, there is a gpkg file and an excel file. The gpkg file is the whole dataset of inputs and results, each row being a European city. The excel summarizes the results for each EU27+UK country.</p> <p> </p>
SPARCS_WP3_Espoo_Sello_Accuracy of Sello's district heating forecast (NRMSE)
<p>Accuracy of Sello's district heating load forecast measured with NRMSE</p>
domOS Aalborg Living Lab: Indoor Climate and District Heating Control Data
<p>This dataset has been collected in context of the domOS H2020 project (<a href="https://www.domos-project.eu/">https://www.domos-project.eu/</a>). The dataset includes data about 147 family homes of which 144 are flats in 12 building blocks and the rest are single family buildings. All buildings are owned by a building association, and are heated via the local district heating company. The buildings have been renovated and are now prepared for low temperature district heating. Within the area, new private homes (mostly blocks and flats) are being built or renovated. The District heating supply to the 12 building blocks is supplied from a local mixing loop separating the district heating transmission from the local distribution. The heating installations in each block can be controlled as well as the mixing loop to the area. Energy and indoor climate data is supplied from selected apartments and from each heating central. The district heating to the 3 single family buildings is not supplied with district heating from the local mixing loop, but from the transmission line directly. In each building, datalogging and control on the heating installation are possible. Furthermore, data from indoor climate sensors as well as sensors on doors and windows is available. The ongoing data collection has been started in several steps. On the three single family houses, data collection started in February 2020. For the apartment buildings data collection began in June 2021 and for the central mixing loop it began in march 2022.</p>
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