Optimizing Daylight and Energy Consumption for Climate Change Adaptation: Integrating an Artificial Neural Network Model with a Multi-Objective Optimization Approach

Abstract

Machine learning models have been proven for their capability to improve the computational efficiency of building performance simulations. However, studies on their reliability to produce Pareto front solutions for multi-objective optimization are limited, particularly for climate adaptation studies. This study proposed a dependable workflow through which to integrate an artificial neural network (ANN) model with energy consumption and daylight multi-objective optimization for climate change adaptation. The trained ANN model attained high R2 scores with RMSE scores of 2.23 and 4.52 for UDI and cooling EUI, respectively. Statistical hypothesis analysis of the Pareto front solutions produced via conventional simulation-based and ANN-based optimization shows that the two models have no significant difference, indicating the reliability of the proposed workflow.

Keywords

• neural network
• multi-objective optimization
• climate change
• daylight
• energy