Forecasting the heating and cooling load of residential buildings by using a learning algorithm “gradient descent”, Morocco

• Published in: Case studies in thermal engineering Vol. 12; pp. 85 - 93
• Main Authors: Jihad, Alaoui Sosse, Tahiri, Mohamed
• Format: Journal Article
• Language: English
• Published: Elsevier 01.09.2018
• ISSN: 2214-157X; 2214-157X
• DOI: 10.1016/j.csite.2018.03.006

In this study, the main objective is to predict the energy needs of residential buildings in the climate zone of Agadir, Morocco, benefitting from orientation, relative compactness, glazing rate, wall surface area, the height and the surface area of the building by using artificial neural networks (ANN) as a learning algorithm. The training data of the neural network were produced using parametric analysis giving rise to 5625 samples in accordance with the mode of construction and use of residential buildings. For each building, it is assumed that the angles of orientation of the samples vary from 0° to 180°, the glazing rates were chosen between 5% and 45%, the heights between 3.5 and 17.5 m and with 25 possible building areas. Three residential buildings ''Economic Villa, Economical Construction and Medium Class building'' were selected as test data for the neural network model. The Design Builder tool was used for energy demand calculations and a computer program written in Python is used for predictions. As a conclusion; When comparing the calculated values with the outputs of the network, it is proved that the ANN gives satisfactory results with an accuracy of 98.7% and 97.6% for the prediction and test data respectively. Keywords: Energy demand of buildings, Heating load, Cooling load, Learning algorithm, Residential buildings