Indoor Environmental Quality (IEQ) is a very important aspect in the design of spaces. It depends on fundamental requirements, and it aims to improve living quality and ensure high well-being for the occupants. Poor IEQ has significant health consequences as people spend a considerable portion of their time indoors. The restrictions imposed by the current SARS-CoV-2 pandemic have worsened this situation by requiring people to spend more time at home and adopt home-working as a primary work model. Such a situation requires individuals to reconfigure their interior home spaces for adapting to new emerging needs. This paper proposes an approach based on reinforcement learning to support the rearrangement of indoor spaces by maximizing the indoor environmental quality index in terms of thermal, acoustic and visual comfort in the new furniture layout scheme
An RL-based approach for IEQ optimization in reorganizing interior spaces for home-working
Ribino Patrizia;
2021
Abstract
Indoor Environmental Quality (IEQ) is a very important aspect in the design of spaces. It depends on fundamental requirements, and it aims to improve living quality and ensure high well-being for the occupants. Poor IEQ has significant health consequences as people spend a considerable portion of their time indoors. The restrictions imposed by the current SARS-CoV-2 pandemic have worsened this situation by requiring people to spend more time at home and adopt home-working as a primary work model. Such a situation requires individuals to reconfigure their interior home spaces for adapting to new emerging needs. This paper proposes an approach based on reinforcement learning to support the rearrangement of indoor spaces by maximizing the indoor environmental quality index in terms of thermal, acoustic and visual comfort in the new furniture layout schemeI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
