Heat Mapping, a Method for Enhancing the Sustainability of the Smart District Heat Networks

Vaisi, Salah; Mohammadi, Sardar; Habibi, Kyoumars

doi:10.3390/en14175462

Cited by 8 publications

(4 citation statements)

References 34 publications

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“…Usually, archetypes are selected by the type of use, geometry, materials, systems, and occupants' behavior [33,34]. The use of census and cadastral data has been adopted by many scientists to study urban energy-use, integrate other energy-related variables, and then improve energy modeling and strategies to reduce consumptions and greenhouse gas emissions [35,36]. The EUI of archetypes can be modeled in UBEM with process-driven tools (e.g., Design-Builder [37], EnergyPlus [29,34,38], IDA ICE [37]), or with data-driven analyses using measured data [38][39][40] or energy performance certificate (EPC) databases [37,41].…”

Section: Literature Reviewmentioning

confidence: 99%

Urban Building Energy Modeling to Support Climate-Sensitive Planning in the Suburban Areas of Santiago de Chile

Mutani,

Alehasin,

Yang

et al. 2024

Buildings

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Greenhouse gas emissions depend on natural and anthropic phenomena; however, to reduce emissions, we can only intervene in terms of anthropic causes. Human activity is very different in various countries and cities. This is mainly due to differences in the type of urban environment, climatic conditions, socioeconomic context, government stability, and other aspects. Urban building energy modeling (UBEM), with a GIS-based approach, allows the evaluation of all the specific characteristics of buildings, population, and urban context that can describe energy use and its spatial distribution within a city. In this paper, a UBEM is developed using the characteristics and consumption of eight typical buildings (archetypes) in the climate zone of Santiago de Chile. The archetype-based UBEM is then applied to the commune of Renca, a critical suburb of Santiago, with the use of QGIS to analyze the energy demand for space heating and the potential for energy saving after four retrofitting interventions. Knowing the costs of the retrofitting interventions and the energy price, the simple payback time was evaluated with the reduction in GHG emissions. Starting from the actual building stock, the results show that the most effective retrofitting intervention for the commune of Renca is the thermal insulation of walls and roofs; due to the type of dwellings, this particular intervention could be more convenient if associated with the installation of solar technologies. This methodology can be replicated with the data used by urban planners and public administrations available for many Chilean cities and in other countries.

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Section: Literature Reviewmentioning

confidence: 99%

Urban Building Energy Modeling to Support Climate-Sensitive Planning in the Suburban Areas of Santiago de Chile

Mutani,

Alehasin,

Yang

et al. 2024

Buildings

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“…The ninth paper by Vaisi and et al [9] presents an innovative monthly thermal energy mapping method to calculate and visualize heat demand accurately for various types of buildings. The method includes three consecutive phases: (i) calculating energy loss, (ii) completing a dataset that includes energy and building information, and (iii) generating the monthly heat demand maps for the community.…”

Section: Overview Of the Articles In This Special Issuementioning

confidence: 99%

GIS and Remote Sensing for Renewable Energy Assessment and Maps

Nastasi

Nezhad

2021

Energies

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“…A profound literature review of HD maps until 2019 has been compiled by [30]. A detailed analysis is usually done at the city or district level, aiming at evaluating the (pre-) feasibility of district heating systems or regional development plans [31][32][33][34]. The traditional approach to developing HD maps is to use a geographic information system such as QGIS or ArcGIS [22,35,36].…”

Section: Introductionmentioning

confidence: 99%

Unveiling the Spatial Distribution of Heat Demand in North-West-Europe Compiled with National Heat Consumption Data

Jüstel,

Humm,

Herbst

et al. 2024

Energies

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Space and water heating for residential and commercial buildings amount to a third of the European Union’s total final energy consumption. Approximately 75% of the primary energy is still produced by burning fossil fuels, leading to high greenhouse gas emissions in the heating sector. Therefore, policymakers increasingly strive to trigger investments in sustainable and low-emission heating systems. This study forms part of the “Roll-out of Deep Geothermal Energy in North-West-Europe”-project and aims at quantifying the spatial heat demand distribution in the Interreg North-West-Europe region. An open-source geographic information system and selected Python packages for advanced geospatial processing, analysis, and visualization are utilized to constrain the maps. These were combined, streamlined, and optimized within the open-source Python package PyHeatDemand. Based on national and regional heat demand input data, three maps are developed to better constrain heat demand at a high spatial resolution of 100 m × 100 m (=1 ha) for the residential and commercial sectors, and for both together (in total). The developed methodology can not only be applied to transnational heat demand mapping but also on various scales ranging from city district level to states and countries. In addition, the workflow is highly flexible working with raster data, vector data, and tabular data. The results reveal a total heat demand of the Interreg North-West-Europe region of around 1700 TWh. The spatial distribution of the heat demand follows specific patterns, where heat demand peaks are usually in metropolitan regions like for the city of Paris (1400 MWh/ha), the city of Brussels (1300 MWh/ha), the London metropolitan area (520 MWh/ha), and the Rhine-Ruhr region (500 MWh/ha). The developed maps are compared with two international projects, Hotmaps and Heat Roadmap Europe’s Pan European Thermal Atlas. The average total heat demand difference from values obtained in this study to Hotmaps and Heat Roadmap Europe is 24 MWh/ha and 84 MWh/ha, respectively. Assuming the implementation of real consumption data, an enhancement in spatial predictability is expected. The heat demand maps are therefore predestined to provide a conceptual first overview for decision-makers and market investors. The developed methods will further allow for anticipated mandatory municipal heat demand analyses.

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Heat Mapping, a Method for Enhancing the Sustainability of the Smart District Heat Networks

Cited by 8 publications

References 34 publications

Urban Building Energy Modeling to Support Climate-Sensitive Planning in the Suburban Areas of Santiago de Chile

Urban Building Energy Modeling to Support Climate-Sensitive Planning in the Suburban Areas of Santiago de Chile

GIS and Remote Sensing for Renewable Energy Assessment and Maps

Unveiling the Spatial Distribution of Heat Demand in North-West-Europe Compiled with National Heat Consumption Data

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