•  
  •  
 

Keywords

Green Building, Energy Modeling, Artificial Neural Network, Machine Learning

Disciplines

Architecture | Engineering

Abstract

This paper presents a Smart Building Energy Model of Residential Building using Artificial Neural Network model (ANN) to assist architects and engineers in selecting the optimum alternative design of building envelope parameters such that external wall and roof insulation material types and window types that minimizes the cost of energy consumption of a residential building to transform it to a green building.

Up to 1540 Simulations using different material thickness and conductivity values of material insulation properties and windows types are carried out in eQuest software for simulation.. The simulations results are implemented to create an artificial neural network inverse model (ANN) with Matlab/Simulink and the performance is investigated. The results from the artificial neural network outputs and the corresponding eQuest simulation outputs were found very close. In addition, the Mean absolute percentage error (MAPE) is equal to 0.49% , demonstrating a best correlation between outputs and target value, the results show a great solution with good accuracy to predict the energy consumption of residential building for several other building envelope optimization parameters.

.

ISSN

2706-784X

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.