Data-driven strategic planning of building energy retrofitting: the case of Stockholm
Open accessAbstract
Limiting global warming to 1.5 °C requires a substantial decrease in the average carbon intensity of buildings, which implies a need for decision-support systems to enable large-scale energy efficiency improvements in existing building stock. This paper presents a novel data-driven approach to strategic planning of building energy retrofitting. The approach is based on the urban building energy model (UBEM), using data about actual building heat energy consumption, energy performance certificates and reference databases. Aggregated projections of the energy performance of each building are used for holistic city-level analysis of retrofitting strategies considering multiple objectives, such as energy saving, emissions reduction and required social investment. The approach is illustrated by the case of Stockholm, where three retrofitting packages (heat recovery ventilation; energy-efficient windows; and a combination of these) were considered for multi-family residential buildings constructed 1946–1975. This identified potential for decreasing heat demand by 334 GWh (18%) and consequent emissions reduction by 19.6 kt-CO2 per year. The proposed method allows the change in total energy demand from large-scale retrofitting to be assessed and explores its impact on the supply side. It thus enables more precisely targeted and better coordinated energy efficiency programmes. The case of Stockholm demonstrates the potential of rich urban energy datasets and data science techniques for better decision making and strategic planning.
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Pasichnyi et al. propose a novel data-driven smart approach to the strategic planning of retrofitting building energy that allows a holistic city-level analysis and assesses change in total energy demand from large-scale retrofitting.
“Pasichnyi et al. [153] propose, as part of case study research, a novel data-driven smart approach to the strategic planning of retrofitting building energy that allows a holistic city-level analysis and assesses change in total energy demand from large-scale retrofitting.”
From Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2024) · cites this paper as [153]
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- Authors
- Pasichnyi, O., Levihn, F., Shahrokni, H., Wallin, J., & Kordas, O.
- Affiliations
- KTH Royal Institute of Technology (SE)
- Corresponding author
- Pasichnyi, Oleksii
- Venue
- Journal of Cleaner Production
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 233
- Pages
- 546-560
- ISSN
- 0959-6526 (ISSN-L)
- OpenAlex
- W2948828060
- Cited by
- 92
- References
- 74
- Citations by year
- 2026: 14 · 2025: 4 · 2024: 16 · 2023: 14 · 2022: 12 · 2021: 19 · 2020: 11 · 2019: 2
- FWCI
- 7.36
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Cleaner Production (publishedVersion, cc-by-nc-nd)
- Funders
- Energimyndigheten
- Topics
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Building and Construction › Building energy efficiency and sustainability
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
- Keywords
- building energy retrofitting, strategic planning, urban building energy models, heating demand reduction, CO2 emission reduction, energy performance certificates, heat recovery ventilation, energy-efficient windows, multi-family housing, decision support system, energy efficiency programs, Stockholm
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