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


Architecture | Engineering


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.






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