ANNEXE: An open-source building energy design optimisation framework using artificial neural networks and genetic algorithms

abstract

© 2022 Elsevier LtdIt is expected that the building sector will be a major contributor to greenhouse gas emissions by 2050, as end-use services such as cooling demand is expected to rise significantly. Building energy design optimisation, applied either for new buildings or refurbishments, is a critical approach to achieve sustainable and adaptable cities, reducing sectoral emissions and increasing occupant's life quality. However, the design optimisation process has been characterised by following outdated approaches, mostly using physics-based thermal models and stochastic optimisation tools with large computational requirements. In addition, physics-based simulation tools still lack appropriate thermodynamic analysis which limits the potential to reduce actual energy inefficiencies in building energy systems. This paper introduces the ANNEXE (Artificial Neural Network (ANN) and EXErgy-based surrogate modelling) building design optimisation framework. Presented as an open-source tool, the framework proposes the integration of exergy analysis to improve building energy efficiency, combined with an efficient two-stage optimisation process allowing to initially optimise hyperparameters from data-driven ANN surrogate models using an Auto Machine Learning (AutoML) approach. Later, the near optimal building design parameters can be found in the second optimisation stage. A house building case study is presented to illustrate the applicability and validity of the proposed framework. The application of the framework and the obtained results have been compared to outputs from a typical optimisation approach based on physics-based energy simulations. It was found that the proposed approach was able to find similar Pareto solutions while reducing computational times by 98%. Compared to the baseline, the optimal design resulted in an increase of 27% in life cycle cost (50 years) but improving thermodynamic efficiency by reducing 26% of exergy destructions while decreasing annual thermal discomfort from 17% to about 5% annually. The results reveal that the proposed method provides a robust surrogate-based optimisation framework for designers and decision makers that could be applied to other energy research areas.