A stochastic dynamic building stock model for determining long-term district heating demand under future climate change

Hietaharju, Petri; Pulkkinen, Jari; Ruusunen, Mika; Louis, Jean-Nicolas

doi:10.1016/j.apenergy.2021.116962

Cited by 17 publications

(3 citation statements)

References 33 publications

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“…The key difference between statistically based building stock models and computationally based models is that statistically based models use historical data and aggregated information to make generalizations about building stocks, while computationally based models rely on detailed data and physicsbased simulations to model individual buildings or building archetypes, providing a more accurate and granular representation. Within the computationally based models, there are subtypes, such as the agent-based model [26], the artificial neural network model [27], and the system dynamics model [28]. Because of rapid improvements in computational power, the computationally based models demonstrate an advantage in modeling, predicting, and analyzing large building stocks.…”

Section: Bottom-up Approachmentioning

confidence: 99%

Building Stock Models for Embodied Carbon Emissions—A Review of a Nascent Field

Hu,

Ghorbany

2024

Sustainability

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Building stock modeling emerges as a critical tool in the strategic reduction of embodied carbon emissions, which is pivotal in reshaping the evolving construction sector. This review provides an overall view of modern methodologies in building stock modeling, homing in on the nuances of embodied carbon analysis in construction. Examining 23 seminal papers, our study delineates two primary modeling paradigms—top-down and bottom-up—each further compartmentalized into five innovative methods. This study points out the challenges of data scarcity and computational demands, advocating for methodological advancements that promise to refine the precision of building stock models. A groundbreaking trend in recent research is the incorporation of machine learning algorithms, which have demonstrated remarkable capacity, improving stock classification accuracy by 25% and urban material quantification by 40%. Furthermore, the application of remote sensing has revolutionized data acquisition, enhancing data richness by a factor of five. This review offers a critical examination of current practices and charts a course toward an environmentally prudent future. It underscores the transformative impact of building stock modeling in driving ecological stewardship in the construction industry, positioning it as a cornerstone in the quest for sustainability and its significant contribution toward the grand vision of an eco-efficient built environment.

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Section: Bottom-up Approachmentioning

confidence: 99%

Building Stock Models for Embodied Carbon Emissions—A Review of a Nascent Field

Hu,

Ghorbany

2024

Sustainability

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“…Based on literature (EASAC, 2021;Göswein et al, 2021;Hietaharju et al, 2021;Roca-Puigròs et al, 2020;Röck et al, 2021) and relevant Dutch policies (CE Delft, 2020;Dutch government, 2019;Rijksoverheid, 2018a;Teun Johannes Verhagen et al, 2021), this study involves several main decarbonization strategies and defines two scenarios, i.e. reference scenario and ambitious scenario (see Table 1).…”

Section: Decarbonization Strategies and Scenario Definitionmentioning

confidence: 99%

Key strategies for decarbonizing the residential building stock: Results from a spatiotemporal model for Leiden, the Netherlands

Yang

Zhang

et al. 2022

Resources, Conservation and Recycling

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“…When examining the economic viability of district heating networks, building renovation measures must also be taken into account [23]. Recently, the results in [24] show that a 2 − 3% building renovation rate per year results in a 19 − 28% decrease of the long-term district heating demand, which consequently also reduces the heat densities of district heating networks. However, studies show that a reduction in heat density is not necessarily a barrier to district heating networks [25].…”

Section: Genementioning

confidence: 99%