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Buildings have a significant role in the total carbon emissions in the UK. Retrofitting the existing building stock can be an effective solution to reduce GHG emissions. Large scale retrofit interventions, however, can drastically affect the social and economic balance of communities. The objective of this study is to investigate how incorporating sociodemographic derived limitations in the cost-effective retrofit optimization of energy efficiency measures (EEM) distribution, across a building stock, can alter the ability of communities to achieve their emissions reduction targets, as well as the distribution of those EEM across the stock.

The English Housing Survey (EHS) was used to populate a parametric model using Grasshopper for Rhino and Energy Plus as a dynamic simulation engine. The behaviour of 36 representative building archetypes, for the East Midlands, was simulated when EEM were applied. The entire stock was then optimized using the NSGA-II genetic algorithm, incorporating new constraints to tackle the risk of fuel-poverty.