A multi‐objective optimization approach to designing window and shading systems considering building energy consumption and occupant comfort

Abstract

The energy performance of a building has a significant impact on the environment as well as the comfort of its occupants. Given that window systems and control elements such as shading devices play a critical role in a building's energy consumption and comfort, this article presents an investigation into the optimization of windows in four different orientations, along the four cardinal directions, for a typical office room in Tehran. The objective is to reduce cooling, heating, and lighting energy consumption while improving occupant comfort simultaneously, using the NSGA‐II algorithm as a multi‐objective optimization method. To this end, first, an investigation into the effects of various parameters on total building energy consumption and occupant comfort is conducted. Subsequently, each parameter is evaluated in detail concerning its impact on each façade. As the final step, for each façade, the optimal solutions and the most undesirable scenarios are determined. The results, in which the possibility of glare is also considered, can be used by architects and designers for making informed design decisions. The results show that, in all orientations, the number of slats and their distance from the wall are the most effective parameters of shading configurations. Although the eastern and western windows are larger in this study, visual discomfort can be controlled using suitable shadings to achieve acceptable levels of visual comfort. Furthermore, north façades provide the least amount of thermal comfort and consume the least amount of energy while experiencing glare for longer periods.

A Q-learning Algorithm for Two-Stage Hybrid Flow Shop Scheduling

“…The objective function (3) aims to minimize the total completion time for all batches of jobs completed in the first stage; Constraint (4) indicates that each batch of jobs can only be assigned to a machine 𝑀 𝑖1 ,𝑖 ∈ ℕ + 𝑚 in the first stage for processing; Constraint (5) specifies that the total completion time of all batches is not less than 𝐶 𝑚𝑎𝑥 ; Constraint (6) stipulates that within the time interval [0, 𝐻 𝑖𝑚(𝑖) ], the average production rate of 𝑀 𝑖1 is greater than or equal to 1∕𝑚𝜓, ensuring the rolling production of a two-stage hybrid flow shop; Constraint (7) represents that when the product batch does not meet the conditions for continuous production, additional inventory 𝛿 𝑖 can be added to ensure continuous production in the two-stage hybrid flow shop; Constraint (8) means that the amount of inventory allocated for all machines in the initial state is 𝑟; and Constraints ( 9) and ( 10) are the range of values for decision variables.…”

Optimal operation and control of smart energy systems

“…The fifth article, entitled “A multi‐objective optimization approach to designing window and shading systems considering building energy consumption and occupant comfort” by Nazari et al, 5 presents a novel optimization method for windows that takes into account both building energy consumption and occupant comfort. The results show that north facades provide the lowest amount of thermal comfort and consume the least amount of energy.…”