A machine-learning-based method for thermal design optimization of residential buildings in highly urbanized areas of Turkey

Abstract

In 2008, the Turkish government legislated a new regulation mandating the retrofit of existing buildings and construction of energy-efficient buildings. Owing to the requirements of the legislated regulations and environmental concerns, constructing energy-efficient buildings has become a necessity. Researchers working in this field have proposed various optimization techniques to determine energy-optimal design solutions. However, the proposed techniques require a large number of simulations thereby reducing the practicality and inhibiting the wider diffusion of the technique into the industry. Recent studies have demonstrated that the use of surrogate models as alternatives to commercial energy simulation tools significantly reduces the time required for optimization, and a large number of buildings can be optimized in a shorter period. In this study, we aimed at developing a surrogate model-based integrated optimization system to obtain energy-optimal thermal designs for residential buildings in the most urbanized cities in Turkey under different levels of budget constraints. For this purpose, an integrated system consisting of a genetic algorithm optimization technique and gradient boosting machine based surrogate model was developed. The results indicate that the proposed surrogate model-based optimization system is an effective method of overcoming the difficulties of simulation-based optimization methods and it can be integrated into the daily workflow of designers. This study reveals the performance of the scarcely used gradient boosting machine based surrogate model in predicting the thermal loads of buildings. The outcomes of this research will guide designers in finding energy-optimal design alternatives for residential buildings.