Modelling the complexity of interconnected energy systems at different urban scales: a critical review
Bibliographic record
Abstract
The urgent need to reverse global climate change necessitates rethinking the design and operation of human-made systems. Urban energy systems, a major source of greenhouse gas emissions, are a key focus to enhance sustainability. Addressing challenges such as renewable energy integration, energy storage, reducing building energy consumption, innovative mobility systems, and improving energy infrastructure flexibility drives the development of reliable, multi-scale models capable of capturing complex dynamics. This review evaluates the current state of urban energy modelling from a novel perspective, focusing on interactions across different scales: end-users, buildings, and districts/cities. It critically assesses existing models' strengths and limitations in addressing the complexity of urban energy systems, identifying gaps in the literature and highlighting emerging trends. The review underscores a paradigm shift towards more end-user-centric modelling approaches, which aim to better capture human behaviour and its impact on energy use. Additionally, it stresses the growing demand for integrated, interdisciplinary simulation tools to address challenges such as demand flexibility. The findings advocate for next-generation urban energy models to move beyond building-focused perspectives, adopting approaches that emphasise end-users and their interactions with clean, affordable energy hubs. The review outlines future directions to improve model accuracy and scalability, supporting the transition to sustainable and resilient urban energy systems. • Comparative review of energy modeling methods for end-user, building, and district scales. • Examines accuracy, complexity, and data requirements across scales for practical applications. • Highlights strengths and limitations of scale-specific models for real-world energy planning. • Advocates multi-scale integration to improve urban energy prediction and decision-making. • Calls for people-centric models enabling multi-domain urban energy system analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".