Amrith, Shyam; Korolija, Ivan; Fennell, Pamela; Rovas, Dimitrios; Ruyssevelt, Paul; (2025) Optimising building stock retrofits for urban-scale action planning: improving the feasibility without losing information. Journal of Building Performance Simulation 10.1080/19401493.2025.2472306. (In press).

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Abstract

Optimisation, as applied to physics-based urban building energy models, is vastly under-researched despite its immense potential in providing vital decision support to local authorities facing the challenge of decarbonising their building stocks. One primary barrier is the vast parameter space that arises at the stock level; this challenges all optimisation algorithms, and ineffective parameter space exploration ultimately leads to poor quality results. A parameter decomposition methodology is presented, which can significantly reduce the parameter space of a stock-level optimisation problem without changing the structure. We show that the separability of a stock-level optimisation problem depends on physical and input factors, including restrictions imposed by the real-world project. The methodology is demonstrated in optimising 53 buildings using dynamic thermal simulation. This split approach yielded a vast improvement in the quality of results compared to a naïve approach, with final hypervolume values of 0.630 and 0.525, respectively.

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