Modeling of heat gain through green roofs utilizing artificial intelligence techniques

Abstract

Green roofs have recently been popular in buildings for saving energy. A reliable model is required to include green roofs' role in the thermal performance of buildings. This study applies artificial neural networks (ANN) to estimate the buildings’ heat gain through green roofs. By comparing different ANN architectures (feedforward, recurrent, and cascade), the best model to estimate heat flux from four significant design factors (plant height, leaf area index of plant, soil depth, and overall heat transfer coefficient of support layer) is determined. The relevance test clarifies that the overall heat transfer coefficient of the support layer and the leaf area index of the plant have the highest correlation with the target variable. The modeling results prove that the multilayer perceptron (MLP) neural network with 4-5-1 topology and the logarithm sigmoid and liner activation functions in hidden and output layers has the best accuracy for the given task. This model predicts 2700 literature data with the regression coefficient = 0.9999, mean absolute errors = 0.025, and root mean squared errors = 0.035. Our topology-engineered MLP can analyze the effect of design factors on the heat flux through green roofs and help quickly determine its impact on building thermal performance.