Artificial Neural Network–Based Generative Design Optimization of the Energy Performance of Florida Single-Family Houses

Abstract

The energy efficiency optimization of Florida’s residential buildings is essential for the reduction of fossil fuel consumption and greenhouse gas emissions. This optimization requires designers to accurately calculate the building energy loads at the design stage to efficiently design the cooling and heating systems. However, due to time and cost constraints, designers usually explore only a few design alternatives and simulate their energy use. This study developed an artificial-neural-network (ANN)-based generative design (GD) framework to automate the design process of detached residences while optimizing their energy performance. The ANN model was developed using the machine learning platform TensorFlow and some Python-based Keras libraries based on a big data set of about 17,000 newly constructed detached residences in Florida between the years 2009 and 2021. The GD framework was established using Autodesk Dynamo and the Autodesk Revit GD add-on that uses the nondominated sorting genetic algorithm (NSGA-II), in which multiple design parameters mainly relating to the house geometry and its energy performance were incorporated. Considering 10 independent variables, including the total wall area, roof area, floor area, and the windows’ U-value, the ANN model predicted the required capacities of the cooling and heating systems in detached houses, with R2 values of 0.955 and 0.904, respectively. The ANN then was integrated within the GD framework for performance evaluation purposes. The findings of this study resulted in a fully automated 3-min design process of an energy-efficient detached house envelope, maximizing the productivity of designers and developers and greatly reducing the financial strain and time consumed using traditional techniques.