Introduction to Building Stock Optimization and Sequential Pareto Optimization

Building stock optimization aims to find the design or refurbishment solutions that lie on the trade-off between conflicting design objectives (such as between carbon emissions and the capital cost of refurbishment). Each solution on the trade-off consists of several houses with each of the houses being associated with one combination of refurbishment options – the number of refurbished houses, their archetype, and the refurbishment option applied, vary along the trade-off. Stock-level trade-offs are useful in policy development, in that an analysis of the trade-off provides information on, say, what is the minimum cost to achieve a target reduction in energy demand. The analysis also provides information on the number and type of houses to be targeted for refurbishment, and which refurbishment strategies should be applied.

The simultaneous optimization of many buildings results in an exceptionally large and difficult optimization problem to solve. Our research has led to a sequential approach that solves the problem and provides information about the trade-off’s achievable for individual houses as well as the building stock. This repository provides an introduction to the research in the form of: a short video; a copy of the slides used in the video; and a brief written summary of the optimization concepts.