Building Energy Use Intensity Prediction with Artificial Neural Networks

Abstract

In this paper the authors propose the construction and examine the performance of the artificial neural network for energy use intensity prediction for residential buildings. The network’s type is the standard multilayer perceptron and its training dataset contains the data of 768 residential buildings where the training pattern for an individual building consists of 8 parameters describing the building’s geometry along with its lighting and glazing conditions while the only output value is the building’s actual energy use intensity characteristics. Experimental study shows that the mean absolute percentage error of prediction of the energy use intensity evaluated for buildings data present in the network’s test set does not exceed 1.8%, what might be considered a highly satisfactory result.