Modelling the energy demand of a residential building using an artificial neural network (ANN) approach

Abstract

The consumption of fossil fuels accelerates and accentuates the formation and development of the climate change phenomenon. Understanding the energy demand in the early-stage design could lead to more energy savings. There are several methods for predicting buildings energy demand and they differ overall in terms of prediction quality. The multicollinearity of variables, linearity, and other conditions limit the predictive quality of standard models such as linear regression modeling.  This paper is interested in developing an energy demand prediction tool based on artificial neural network modeling ANN to test its limitations and its predictive quality. For this purpose, and based on the scientific literature, a panel of parameters often used by architects at the time of architectural design was selected, which are, the thermal resistance of the external walls, the type, and rate of glazing, the orientation, the shading devices the set point cooling PMV and natural ventilation rate schedule. A campaign of 600 dynamic thermal simulations is then run under energy plus using the Latin Hypercube Sampling (LHS) approach. The best ANN model obtained after testing several activating functions gave a prediction potential of over 99.7%. The model also ranks each parameter according to its importance in the equation identifying the energy demand. It can therefore be assumed that the artificial neural network technique is effective and the ANN outperforms the other prediction methods.