A systematic review of explainable artificial intelligence in urban building energy modeling: methods, applications, and future directions
Open accessAbstract
Urban Building Energy Modeling (UBEM) has emerged as a critical tool for addressing the growing energy demands of cities and promoting sustainability by optimizing energy use, reducing emissions, and improving efficiency at the urban scale. Machine learning (ML) techniques have transformed UBEM applications by enabling energy consumption prediction, demand optimization, and retrofitting strategies. However, the complexity of many ML models limits their interpretability and can undermine stakeholder trust in decision-making. Explainable Artificial Intelligence (XAI) addresses these issues by increasing transparency and making ML models more understandable and actionable. Given the multidisciplinary nature of urban energy planning, XAI enables policymakers, urban planners, engineers, and building managers to extract relevant insights tailored to their needs. By translating complex ML predictions into understandable explanations, XAI fosters trust and supports inclusive decision-making. This systematic review examines the role of XAI in UBEM and presents a taxonomy of XAI methodologies based on explainability approach, explanation scope, and model applicability. It highlights how XAI methods contribute to key UBEM applications, including energy forecasting, retrofitting, clustering, and occupancy modeling. Despite recent advances, several challenges remain, such as the computational cost of post-hoc techniques at city scale and balancing interpretability with predictive performance. The review also identifies underexplored areas, including interpretable clustering and occupancy behavior modeling, alongside the limited adoption of model-specific explainability methods. To address these gaps, future research should explore the scalability and usability of XAI methods in UBEM, investigate model-specific techniques for deep and graph-based models, and develop approaches for evaluating the effectiveness of XAI in real-world UBEM contexts.
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Statistical decomposition is often used as a preprocessing step in hybrid AI pipelines for UBEM tasks, improving efficiency of downstream neural networks by isolating deterministic patterns from stochastic residuals.
“Moreover, statistical decomposition is often used as a preprocessing step in hybrid AI pipelines for UBEM tasks [36], thereby improving the efficiency of downstream neural networks by isolating deterministic patterns from stochastic residuals.”
From Explainable AI and Multi-Agent Systems for Energy Management in IoT-Edge Environments: A State of the Art Review (2026) · cites this paper as [36]
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The Cost of Understanding—XAI Algorithms towards Sustainable ML in the View of Computational Cost
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A New Perspective on Evaluation Methods for Explainable Artificial Intelligence (XAI)
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Does Retrofitting Pay Off? An Analysis of German Multifamily Building Data
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Interrelationships between electricity, gas, and water consumption in large‐scale buildings
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An Explainable Evaluation Model for Building Thermal Comfort in China
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Canaydin, A., Fu, C., Bálint, A., Khalil, M., Miller, C. C., & Kazmi, H. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.122741 · Cited by 20 · see citing works
An Overview of Explainable AI Methods, Forms and Frameworks
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Na, W., & Liu, S. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114581 · Cited by 17 · see citing works
Predicting Danish residential heating energy use from publicly available building characteristics
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Interpretable Forecasting of Energy Demand in the Residential Sector
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Li, Z., Ma, J., & Jiang, F. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.110827 · Cited by 14 · see citing works
Examining Correlation Between Trust and Transparency with Explainable Artificial Intelligence
Kartikeya, A. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-10464-0_23 · Cited by 13 · see citing works
Prediction model establishment for residential community occupancy considering urban environment
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Explainable Occupancy Prediction Using QLattice
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ExplainerX: An Integrated and Explainable AI Framework for Nearly Zero-Energy Buildings
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Review of archetype development strategies for energy assessment at urban scale
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On interpretable and explainable machine learning for urban building energy modeling
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On the causality of data-driven building thermal models
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Towards Rule Extraction for Sector-Coupled Energy Systems Based on Optimization Models
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Identifying drivers of residential energy consumption by explainable energy demand forecasting
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- Authors
- Darvishvand, L., Kamkari, B., Huang, M. J., & Hewitt, N. J.
- Affiliations
- University of Ulster (GB)
- Corresponding author
- Darvishvand, Leila
- Venue
- Sustainable Cities and Society
- Publisher
- Elsevier BV
- Published
- 2025-06
- Volume
- 128
- Pages
- 106492
- ISSN
- 2210-6707 (ISSN-L)
- OpenAlex
- W4410790394
- Cited by
- 42
- References
- 143
- Citations by year
- 2026: 34 · 2025: 8
- FWCI
- 4.03
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Sustainable Cities and Society (publishedVersion, cc-by)
- Ulster University Research Portal (Ulster University) (publishedVersion, cc-by)
- Funders
- Department for the Economy
- Department for the Economy in Northern Ireland (USI 167)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Keywords
- explainable AI, urban building energy modeling, building energy consumption prediction, building retrofit, interpretable clustering, post-hoc explanation, urban energy planning
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