Optimizing energy consumption in the building sector using neural networks and PSO algorithms (Case study: Bandar Abbas city)

Energy consumption in buildings accounts for one third of the country's annual energy consumption, so it is important to provide solutions that can reduce energy consumption in this sector.

Using questionnaires and experts’ opinions, effective parameters in energy optimization in Construction Engineering Organization of Bandar Abbas were identified. Variables such as wall and ceiling material, area and type of windows, wall and ceiling insulation thickness were selected. Different modes were investigated with Design Builder software. By training two separate neural networks, how the inputs are connected to two important outputs, which is the amount of energy and carbon dioxide, was obtained. And optimization was performed using the PSO algorithm.

In the obtained model, brick wall with insulation thickness of 5cm, beam roof with insulation thickness of 5cm, triple glazing, ratio of north and east windows to wall in the same direction 70%, ratio of south window to south wall between 41 to 43 percent and the ratio of the west window to the west wall is between 65 to 67 percent, in which the amount of energy and carbon dioxide is the minimum.

If the energy is selected as target function, the results obtained from the PSO are closely consistent with the optimization results for when the target function is the amount of carbon dioxide. These two functions are in line with each other, and optimizing one will lead to optimizing the other.