The ODHeatMap tool: Open data district heating tool for sustainable energy planning
Main Article Content
Abstract
Building footprints are a geographical indication of the spatial distribution of built-up infrastructure, thereby reflecting energy demand patterns, including heating requirements. Heating demands spatial distribution shown in heat atlases are primordial for evaluating district heating systems feasibility, which are a key decarbonizing technology that offers more sustainable heat supply in dense urban areas. Sustainable energy planning frameworks utilize district heating potentials as metrics for the formulation of alternate system configurations aimed at decarbonizing societies and creating an understanding of heating transition pathways. However, the availability and accessibility of the data needed for assessing these potentials is highly contextual and often challenges modelling processes. Simultaneously, there is a growing potential for open data and software mechanisms that could aid in addressing these challenges and create otherwise unavailable heat mapping resources. This paper describes the development of the ODHeatMap tool, a workflow built with open data in python functions that transform building footprints into a heat atlas. Ulaanbaatar city is used as a demonstration area for the tools functionalities, with the outputs being applied in a broader study aimed at developing strategies for Mongolia's heating sector. The tool is accessible through a fully cloud-based environment and can be used in any given geographical context.
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References
Lund H, Thellufsen JZ, Sorknæs P, Mathiesen BV, Chang M, Madsen PT, et al. Smart energy Denmark. A consistent and detailed strategy for a fully decarbonized society. Renewable and Sustainable Energy Reviews 2022;168. https://doi.org/10.1016/j.rser.2022.112777.
Williams JH, DeBenedictis A, Ghanadan R, Mahone A, Moore J, Morrow WR, et al. The technology path to deep greenhouse gas emissions cuts by 2050: The pivotal role of electricity. Science (1979) 2012;335. https://doi.org/10.1126/science.1208365.
Lund H, Østergaard PA, Connolly D, Mathiesen BV. Smart energy and smart energy systems. Energy 2017;137:556–65. https://doi.org/10.1016/j.energy.2017.05.123.
IPCC. Climate Change 2022: Mitigation of Climate Change Report. 2022.
Yuan M, Nielsen FD, Abid H, Nielsen S, Østergaard PA, Mathiesen B. Framework for Developing Sustainable Heating Roadmaps in Europe and Central Asia. 2022. https://doi.org/10.54337/aau645416523.
Lund H, Möller B, Mathiesen B V, Dyrelund A. The role of district heating in future renewable energy systems. Energy 2010;35:1381–90. https://doi.org/10.1016/j.energy.2009.11.023.
Boldrini A, Jiménez Navarro JP, Crijns-Graus WHJ, van den Broek MA. The role of district heating systems to provide balancing services in the European Union. Renewable and Sustainable Energy Reviews 2022;154. https://doi.org/10.1016/j.rser.2021.111853.
Münster M, Morthorst PE, Larsen H V., Bregnbæk L, Werling J, Lindboe HH, et al. The role of district heating in the future Danish energy system. Energy 2012;48:47–55. https://doi.org/10.1016/j.energy.2012.06.011.
Yuan M, Vad Mathiesen B, Schneider N, Xia J, Zheng W, Sorknæs P, et al. Renewable energy and waste heat recovery in district heating systems in China: A systematic review. Energy 2024;294. https://doi.org/10.1016/j.energy.2024.130788.
Yuan M, Thellufsen JZ, Sorknæs P, Lund H, Liang Y. District heating in 100% renewable energy systems: Combining industrial excess heat and heat pumps. Energy Convers Manag 2021;244. https://doi.org/10.1016/j.enconman.2021.114527.
Sorknæs P, Nielsen S, Lund H, Mathiesen BV, Moreno D, Thellufsen JZ. The benefits of 4th generation district heating and energy efficient datacentres. Energy 2022;260. https://doi.org/10.1016/j.energy.2022.125215.
Yuan M, Thellufsen JZ, Lund H, Liang Y. The first feasible step towards clean heating transition in urban agglomeration: A case study of Beijing-Tianjin-Hebei region. Energy Convers Manag 2020;223. https://doi.org/10.1016/j.enconman.2020.113282.
Interreg Central Europe. Heat Demand Estimation. Annex to D.T2.2. Planning Guidelines for Small District Heating v1. 2020.
Li W, Zhou Y, Cetin K, Eom J, Wang Y, Chen G, et al. Modeling urban building energy use: A review of modeling approaches and procedures. Energy 2017;141. https://doi.org/10.1016/j.energy.2017.11.071.
Wittchen KB, Aggerholm S. Calculation of building heating demand in EPIQR. Energy Build 2000;31:137–41. https://doi.org/10.1016/S0378-7788(99)00027-4.
Peeters L, de Dear R, Hensen J, D’haeseleer W. Thermal comfort in residential buildings: Comfort values and scales for building energy simulation. Appl Energy 2009. https://doi.org/10.1016/j.apenergy.2008.07.011.
Schüler N, Mastrucci A, Bertrand A, Page J, Maréchal F. Heat demand estimation for different building types at regional scale considering building parameters and urban topography. Energy Procedia, vol. 78, 2015. https://doi.org/10.1016/j.egypro.2015.11.758.
Edtmayer H, Fochler LM, Mach T, Fauster J, Schwab E, Hochenauer C. High-resolution, spatial thermal energy demand analysis and workflow for a city district. International Journal of Sustainable Energy Planning and Management 2023;38. https://doi.org/10.54337/ijsepm.7570.
Manz P, Fleiter T, Eichhammer W. The effect of low-carbon processes on industrial excess heat potentials for district heating in the EU: A GIS-based analysis. Smart Energy 2023;10. https://doi.org/10.1016/j.segy.2023.100103.
Delmastro C, Mutani G, Schranz L. The evaluation of buildings energy consumption and the optimization of district heating networks: a GIS-based model. International Journal of Energy and Environmental Engineering 2016;7. https://doi.org/10.1007/s40095-015-0161-5.
Gils HC, Cofala J, Wagner F, Schöpp W. GIS-based assessment of the district heating potential in the USA. Energy 2013;58. https://doi.org/10.1016/j.energy.2013.06.028.
Lumbreras M, Diarce G, Martin-Escudero K, Campos-Celador A, Larrinaga P. Design of district heating networks in built environments using GIS: A case study in Vitoria-Gasteiz, Spain. J Clean Prod 2022;349. https://doi.org/10.1016/j.jclepro.2022.131491.
Eslami S, Noorollahi Y, Marzband M, Anvari-Moghaddam A. District heating planning with focus on solar energy and heat pump using GIS and the supervised learning method: Case study of Gaziantep, Turkey. Energy Convers Manag 2022;269. https://doi.org/10.1016/j.enconman.2022.116131.
Pieper H, Lepiksaar K, Volkova A. GIS-based Approach to Identifying Potential Heat Sources for Heat Pumps and Chillers Providing District Heating and Cooling. International Journal of Sustainable Energy Planning and Management 2022;34. https://doi.org/10.54337/ijsepm.7021.
Möller B. A heat atlas for demand and supply management in Denmark. Management of Environmental Quality: An International Journal 2008;19. https://doi.org/10.1108/14777830810878650.
Persson U, Werner S. Heat distribution and the future competitiveness of district heating. Appl Energy 2011;88. https://doi.org/10.1016/j.apenergy.2010.09.020.
Wyrwa A, Chen YK. Mapping urban heat demand with the use of gis-based tools. Energies (Basel) 2017;10. https://doi.org/10.3390/en10050720.
Nuño-Villanueva N, Maté-González MÁ, Nieto IM, Blázquez CS, Martín AF, González-Aguilera D. GIS-based selection methodology for viable District Heating areas in Castilla y León, Spain. Geothermics 2023;113. https://doi.org/10.1016/j.geothermics.2023.102767.
Csontos C, Soha T, Harmat Á, Campos J, Csüllög G, Munkácsy B. Spatial analysis of renewable-based rural district heating possibilities – a case study from Hungary. International Journal of Sustainable Energy Planning and Management 2020;28. https://doi.org/10.5278/ijsepm.3661.
Nielsen S. A geographic method for high resolution spatial heat planning. Energy 2014;67. https://doi.org/10.1016/j.energy.2013.12.011.
Grundahl L, Nielsen S. Heat atlas accuracy compared to metered data. International Journal of Sustainable Energy Planning and Management 2019;23. https://doi.org/10.5278/ijsepm.3174.
Möller B, Nielsen S. High resolution heat atlases for demand and supply mapping. International Journal of Sustainable Energy Planning and Management 2014;1. https://doi.org/10.5278/ijsepm.2014.1.4.
Eslami S, Noorollahi Y, Marzband M, Anvari-Moghaddam A. Integrating heat pumps into district heating systems: A multi-criteria decision analysis framework incorporating heat density and renewable energy mapping. Sustain Cities Soc 2023;98. https://doi.org/10.1016/j.scs.2023.104785.
Paardekooper S, Lund H, Chang M, Nielsen S, Moreno D, Thellufsen JZ. Heat Roadmap Chile: A national district heating plan for air pollution decontamination and decarbonisation. J Clean Prod 2020;272:122744. https://doi.org/10.1016/J.JCLEPRO.2020.122744.
Semahi S, Benbouras MA, Mahar WA, Zemmouri N, Attia S. Development of spatial distribution maps for energy demand and thermal comfort estimation in Algeria. Sustainability (Switzerland) 2020;12. https://doi.org/10.3390/su12156066.
Su C, Madani H, Palm B. Heating solutions for residential buildings in China: Current status and future outlook. Energy Convers Manag 2018;177. https://doi.org/10.1016/j.enconman.2018.10.005.
Möller B, Wiechers E, Persson U, Grundahl L, Connolly D. Heat Roadmap Europe: Identifying local heat demand and supply areas with a European thermal atlas. Energy 2018;158:281–92. https://doi.org/10.1016/J.ENERGY.2018.06.025.
Pelda J, Holler S, Persson U. District heating atlas - Analysis of the German district heating sector. Energy 2021;233. https://doi.org/10.1016/j.energy.2021.121018.
Divkovic D, Knorr L, Meschede H. Design approach to extend and decarbonise existing district heating systems-case study for German cities. International Journal of Sustainable Energy Planning and Management 2023;38. https://doi.org/10.54337/ijsepm.7655.
Albert MDA, Bennett KO, Adams CA, Gluyas JG. Waste heat mapping: A UK study. Renewable and Sustainable Energy Reviews 2022;160. https://doi.org/10.1016/j.rser.2022.112230.
Lund R, Persson U. Mapping of potential heat sources for heat pumps for district heating in Denmark. Energy 2016;110. https://doi.org/10.1016/j.energy.2015.12.127.
Persson U, Wiechers E, Möller B, Werner S. Heat Roadmap Europe: Heat distribution costs. Energy 2019;176:604–22. https://doi.org/10.1016/J.ENERGY.2019.03.189.
Bjarne J, Johannes Z, Christian S, Oleg K, Janybek O, Ulrik J, et al. Covering District Heating Demand with Waste Heat from Data Centres – A Feasibility Study in Frankfurt, Germany. International Journal of Sustainable Energy Planning and Management 2024;41.
Sánchez-García L, Averfalk H, Möllerström E, Persson U. Understanding effective width for district heating. Energy 2023;277. https://doi.org/10.1016/j.energy.2023.127427.
Fallahnejad M, Kranzl L, Hummel M. District heating distribution grid costs: a comparison of two approaches. International Journal of Sustainable Energy Planning and Management 2022;34. https://doi.org/10.54337/ijsepm.7013.
Sorknæs P, Østergaard PA, Thellufsen JZ, Lund H, Nielsen S, Djørup S, et al. The benefits of 4th generation district heating in a 100% renewable energy system. Energy 2020;213:119030. https://doi.org/10.1016/j.energy.2020.119030.
Möller B, Lund H. Conversion of individual natural gas to district heating: Geographical studies of supply costs and consequences for the Danish energy system. Appl Energy 2010;87:1846–57. https://doi.org/10.1016/j.apenergy.2009.12.001.
Moreno D, Nielsen S, Sorknæs P, Lund H, Thellufsen JZ, Mathiesen BV. Exploring the location and use of baseload district heating supply. What can current heat sources tell us about future opportunities? Energy 2024;288:129642. https://doi.org/10.1016/J.ENERGY.2023.129642.
Lund H, Østergaard PA, Chang M, Werner S, Svendsen S, Sorknæs P, et al. The status of 4th generation district heating: Research and results. Energy 2018;164:147–59. https://doi.org/10.1016/j.energy.2018.08.206.
Pfenninger S, DeCarolis J, Hirth L, Quoilin S, Staffell I. The importance of open data and software: Is energy research lagging behind? Energy Policy 2017;101:211–5. https://doi.org/10.1016/j.enpol.2016.11.046.
Niet T, Shivakumar A, Gardumi F, Usher W, Williams E, Howells M. Developing a community of practice around an open source energy modelling tool. Energy Strategy Reviews 2021;35. https://doi.org/10.1016/j.esr.2021.100650.
HFT Stuttgart. SimStadt tool 2022. https://github.com/larswolter/simstadt (accessed May 3, 2023).
Rossknecht M, Airaksinen E. Concept and evaluation of heating demand prediction based on 3D city models and the CityGML energy ADE-case study Helsinki. ISPRS Int J Geoinf 2020;9. https://doi.org/10.3390/ijgi9100602.
Nouvel R, Zirak M, Coors V, Eicker U. The influence of data quality on urban heating demand modeling using 3D city models. Comput Environ Urban Syst 2017;64. https://doi.org/10.1016/j.compenvurbsys.2016.12.005.
ETH Zurich. CityEnergyAnalyst tool 2024. https://github.com/architecture-building-systems/CityEnergyAnalyst (accessed May 3, 2023).
Fonseca JA, Nguyen TA, Schlueter A, Marechal F. City Energy Analyst (CEA): Integrated framework for analysis and optimization of building energy systems in neighborhoods and city districts. Energy Build 2016;113. https://doi.org/10.1016/j.enbuild.2015.11.055.
Fraunhofer-Gesellschaft. DiGriPy tool 2022. https://github.com/lvorspel/DiGriPy (accessed May 3, 2023).
Vorspel L, Bücker J. District-heating-grid simulation in python: Digripy. Computation 2021;9. https://doi.org/10.3390/computation9060072.
Centre for Sustainable Energy. THERMOS tool 2021. https://github.com/cse-bristol/110-thermos-ui (accessed May 3, 2022).
EEG - TU Wien. HotMaps tool 2023. https://github.com/HotMaps (accessed May 3, 2023).
RINA. Planheat tool 2019. https://github.com/Planheat/Planheat-Tool (accessed May 3, 2022).
Flensburg University, Halmstad University, Aalborg University. Pan-European Thermal Atlas 4.5. Heat Roadmap Europe 4 2022. https://heatroadmap.eu/peta4/ (accessed May 14, 2020).
Grinberger AY, Minghini M, Juhász L, Yeboah G, Mooney P. OSM Science—The Academic Study of the OpenStreetMap Project, Data, Contributors, Community, and Applications. ISPRS Int J Geoinf 2022;11. https://doi.org/10.3390/ijgi11040230.
Teimoory N, Ali Abbaspour R, Chehreghan A. Reliability extracted from the history file as an intrinsic indicator for assessing the quality of OpenStreetMap. Earth Sci Inform 2021;14. https://doi.org/10.1007/s12145-021-00675-6.
Pfenninger S, Staffell I. Renewables.ninja 2021. https://www.renewables.ninja/about (accessed June 24, 2021).
Hersbach H, Bell B, Berrisford P, Biavati G, Horányi A, Muñoz Sabater J, et al. ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). 2018. https://doi.org/10.24381/cds.adbb2d47.
Episcope. Tabula 2020. https://episcope.eu/welcome/ (accessed October 20, 2020).
Gao Y, Janssen M, Zhang C. Understanding the evolution of open government data research: towards open data sustainability and smartness. International Review of Administrative Sciences 2023;89. https://doi.org/10.1177/00208523211009955.
Samsø Fibæk C, Laufer H, Keßler C, Jokar Arsanjani J. Geodata-driven approaches to financial inclusion – Addressing the challenge of proximity. International Journal of Applied Earth Observation and Geoinformation 2021;99. https://doi.org/10.1016/j.jag.2021.102325.
Pondi B, Appel M, Pebesma E. OpenEOcubes: an open-source and lightweight R-based RESTful web service for analyzing earth observation data cubes. Earth Sci Inform 2024;17. https://doi.org/10.1007/s12145-024-01249-y.
Yadav D, Kapoor K, Yadav AK, Kumar M, Jain A, Morato J. Satellite image classification using deep learning approach. Earth Sci Inform 2024. https://doi.org/10.1007/s12145-024-01301-x.
Du S, Zheng M, Guo L, Wu Y, Li Z, Liu P. Urban building function classification based on multisource geospatial data: a two-stage method combining unsupervised and supervised algorithms. Earth Sci Inform 2024;17. https://doi.org/10.1007/s12145-024-01250-5.
Davarpanah A, Babaie H, Dhakal N. Semantic modeling of climate change impacts on the implementation of the U.N. sustainable development goals related to poverty, hunger, water, and energy. Earth Sci Inform 2023;16. https://doi.org/10.1007/s12145-023-00941-9.
Tripathi AK, Agrawal S, Gupta RD. GeoCloud4SDI: a cloud enabled open framework for development of spatial data infrastructure at city level. Earth Sci Inform 2023;16. https://doi.org/10.1007/s12145-022-00893-6.
Noguer M, Arkell A, Yates A, Lloyd-Hughes B, Groom A. RE-SAT: Energy Analytics Platform Renewable Energy planning in Vanuatu. 2021.
Moreno D. ODHeatMap. GitHub Repository 2024. https://github.com/dismaps/ODHeatMap (accessed May 7, 2024).
IRENA. Renewable energy solutions for heating systems in Mongolia: Developing a Strategic heating plan. Abu Dhabi: 2023.
Farzaneh H, Dashti M, Zusman E, Lee SY, Dagvadorj D, Nie Z. Assessing the Environmental-Health-Economic Co-Benefits from Solar Electricity and Thermal Heating in Ulaanbaatar, Mongolia. Int J Environ Res Public Health 2022;19. https://doi.org/10.3390/ijerph19116931.
Batsumber Z, He J. Measurement of Indoor Thermal Environment and Analysis of Heating Energy Saving in Residential Buildings in Ulaanbaatar, Mongolia. Sustainability (Switzerland) 2023;15. https://doi.org/10.3390/su151310598.
OpenStreetMap Contributors. OpenStreetMap. Buildings. 2022. https://wiki.openstreetmap.org/wiki/Buildings (accessed November 3, 2022).
Microsoft. GlobalMLBuildingFootprints 2022. https://github.com/microsoft/GlobalMLBuildingFootprints/tree/main (accessed April 17, 2022).
Geofabrik. Map Compare. Geofabrik Tools. 2022. https://tools.geofabrik.de/mc/#15/49.0094/8.3902&num=4&mt0=mapnik&mt1=geofabrik-basic-colour&mt2=mapnik-german&mt3=here-map (accessed April 17, 2022).
Schiavina M, Melchiorri M, Pesaresi M, Politis P, Carneiro Freire S, Maffenini L, et al. GHSL Data Package 2023 2023. https://doi.org/10.2760/098587 (online).
Pesaresi M, Politis P. GHS-BUILT-H R2023A - GHS building height, derived from AW3D30, SRTM30, and Sentinel2 composite (2018) [Dataset]. European Commission, Joint Research Centre (JRC). 2023. https://doi.org/10.2905/85005901-3A49-48DD-9D19-6261354F56FE.
Pesaresi M, Corbane C, Ren C, Edward N. Generalized Vertical Components of built-up areas from global Digital Elevation Models by multi-scale linear regression modelling. PLoS One 2021;16. https://doi.org/10.1371/journal.pone.0244478.
Amber KP, Aslam MW, Ikram F, Kousar A, Ali HM, Akram N, et al. Heating and cooling degree-days maps of Pakistan. Energies (Basel) 2018;11. https://doi.org/10.3390/en11010094.
Shi Y, Gao X, Xu Y, Giorgi F, Chen D. Effects of climate change on heating and cooling degree days and potential energy demand in the household sector of China. Clim Res 2016;67. https://doi.org/10.3354/cr01360.
Spinoni J, Vogt J V., Barbosa P, Dosio A, McCormick N, Bigano A, et al. Changes of heating and cooling degree-days in Europe from 1981 to 2100. International Journal of Climatology 2018;38. https://doi.org/10.1002/joc.5362.
Andrade C, Mourato S, Ramos J. Heating and cooling degree-days climate change projections for Portugal. Atmosphere (Basel) 2021;12. https://doi.org/10.3390/atmos12060715.
European Commission Copernicus Programme. Climate Data Store (CDS) 2022. https://cds.climate.copernicus.eu/ (accessed January 21, 2023).
Hofmann F, Hampp J, Neumann F, Brown T, Hörsch J. atlite: A Lightweight Python Package for Calculating Renewable Power Potentials and Time Series. J Open Source Softw 2021;6. https://doi.org/10.21105/joss.03294.
Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz-Sabater J, et al. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 2020;146. https://doi.org/10.1002/qj.3803.
Raushan K, Ahern C, Norton B. Determining realistic U-values to substitute default U-values in EPC database to make more representative; a case-study in Ireland. Energy Build 2022;274. https://doi.org/10.1016/j.enbuild.2022.112358.
Mobaraki B, Castilla Pascual FJ, Lozano-Galant F, Lozano-Galant JA, Porras Soriano R. In situ U-value measurement of building envelopes through continuous low-cost monitoring. Case Studies in Thermal Engineering 2023;43. https://doi.org/10.1016/j.csite.2023.102778.
MindManager. MindManager Software 2023.
Jordahl K, den Bossche J Van, Fleischmann M, Wasserman J, McBride J, Gerard J, et al. Python library: geopandas/geopandas: v0.8.1 2020. https://doi.org/10.5281/zenodo.3946761.
Python library: pyrosm 2023. https://github.com/pyrosm/pyrosm (accessed October 2, 2024).
Python library: mercantile 2024. https://github.com/mapbox/mercantile (accessed October 2, 2024).
Huang X, Wang C, Li Z, Ning H. A 100 m population grid in the CONUS by disaggregating census data with open-source Microsoft building footprints. Big Earth Data 2021;5. https://doi.org/10.1080/20964471.2020.1776200.
ESRI. ArcGIS Pro. Version 3.1.0. Environmental Systems Research Institute, Inc 2023.
Python library: gdal 2024. https://github.com/OSGeo/gdal (accessed October 2, 2024).
Python library: rasterstats 2024. https://github.com/perrygeo/python-rasterstats (accessed October 2, 2024).
Python library: cdsapi 2024. https://github.com/ecmwf/cdsapi (accessed October 2, 2024).
Stryi-Hipp G, Triebel M-A, Eggers J-B, Jantsch M, Taani R, Behrens J. Energy Master Plan for Ulaanbaatar (Mongolia) Final Report Energy Master Plan for Ulaanbaatar (Mongolia) Final Report. 2018. https://doi.org/10.13140/RG.2.2.27560.29447.
INTEGRATION Umwelt and Energie GmbH and Ekodoma Ltd. Local Energy Efficiency Action Plan: Ulanbaatar. Ulanbaatar: 2020.
Chang M, Thellufsen JZ, Zakeri B, Pickering B, Pfenninger S, Lund H, et al. Trends in tools and approaches for modelling the energy transition. Appl Energy 2021;290. https://doi.org/10.1016/j.apenergy.2021.116731.
Chang M, Lund H, Thellufsen JZ, Østergaard PA. Perspectives on purpose-driven coupling of energy system models. Energy 2023;265. https://doi.org/10.1016/j.energy.2022.126335.
See L, Georgieva I, Duerauer M, Kemper T, Corbane C, Maffenini L, et al. A crowdsourced global data set for validating built-up surface layers. Sci Data 2022;9. https://doi.org/10.1038/s41597-021-01105-4.
Uhl JH, Leyk S. Spatially explicit accuracy assessment of deep learning-based, fine-resolution built-up land data in the United States. International Journal of Applied Earth Observation and Geoinformation 2023;123. https://doi.org/10.1016/j.jag.2023.103469.
Liu F, Wang S, Xu Y, Ying Q, Yang F, Qin Y. Accuracy assessment of Global Human Settlement Layer (GHSL) built-up products over China. PLoS One 2020;15. https://doi.org/10.1371/journal.pone.0233164.
Kuriyan K, Shah N. A combined spatial and technological model for the planning of district energy systems. International Journal of Sustainable Energy Planning and Management 2019;21:111–31. https://doi.org/10.5278/ijsepm.2019.21.8.
Persson U, Averfalk H, Nielsen S, Moreno D. Accessible urban waste heat 2020:168.
Paardekooper S, Lund RS, Mathiesen BV, Chang M, Petersen UR, Grundahl L, et al. Quantifying the Impact of Low-carbon Heating and Cooling Roadmaps. Heat Roadmap Europe, Deliverable 6.4; 2018.
Connolly D, Mathiesen BV, Lund H. Smart Energy Europe: From a Heat Roadmap to an Energy System Roadmap. Aalborg Universitet; 2015.
Möller B, Wiechers E, Persson U, Grundahl L, Lund RS, Mathiesen BV. Heat Roadmap Europe: Towards EU-Wide, local heat supply strategies. Energy 2019;177. https://doi.org/10.1016/j.energy.2019.04.098.