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AI-driven whole-life costing for decision-ready UK public building retrofits

Abstract Public-sector building retrofits in the United Kingdom require decisions that balance whole-life cost, carbon reduction, service continuity and auditability under limited data and constrained capital budgets. This paper develops a decision-ready whole-life costing (WLC) workflow that integrates environmental, social and governance (ESG) priorities with uncertainty-aware artificial intelligence (AI) for public-building retrofit appraisal. The workflow combines national non-domestic Energy Performance Cer…

By the Truvace desk1 min read1 sourcePeer-reviewedTRV-2026-1729· cite & verify
AI-driven whole-life costing for decision-ready UK public building retrofits
Building sector electricity consumption per capita by region in 2018 through 2050 (48946550613) by U.S. Energy Information Administration. Public domain

In brief

Researchers built a decision-support workflow for retrofitting UK public buildings. It combines national energy certificate data, public-sector decarbonisation delivery evidence and stakeholder input, with a reinforcement learning planner whose objectives are weighted by stakeholder priorities. A Bayesian neural surrogate supplies energy, carbon and cost trajectories with uncertainty bounds.

The approach matters because public bodies must weigh whole-life cost, carbon cuts and service continuity on tight capital budgets and limited data. The results are promising, but they rest on public datasets and scenario-level evidence rather than live projects, so real-world performance remains untested.

Main points

  1. The workflow merges whole-life costing with environmental, social and governance priorities for early-stage retrofit appraisal.
  2. Stakeholder interviews, a questionnaire and a co-design workshop were turned into transparent weights using fuzzy analytic hierarchy process.
  3. The trained deep Q-network search was 30.03 times faster than scalar feasible-sequence enumeration across 1000 matched archetype-budget cases.
  4. It outperformed whole-life-cost-only, cost-per-tonne-of-carbon and static multi-criteria decision analysis baselines on composite ESG performance.

The gain

A reinforcement learning planner paired with an uncertainty-aware surrogate model produced auditable retrofit sequences, scored better on composite ESG performance than conventional baselines, and searched decisions far faster in matched test cases.

The rundown

The inputs include national non-domestic Energy Performance Certificate and Display Energy Certificate data and Public Sector Decarbonisation Scheme delivery evidence. These were used to build a scenario library of public-building archetypes, retrofit actions and appraisal constraints.

A Bayesian convolutional neural network and long short-term memory surrogate provides uncertainty-bounded energy, carbon and cost trajectories. This lets the planner evaluate policies repeatedly without expensive detailed simulation.

The authors frame the contribution as an integrated, explainable workflow for early-stage decision support. They propose future work using project-specific metered data, digital twins and live portfolio deployment.

Sources

  1. Peer-reviewedComplex & Intelligent Systems2026-10-16

The debate