Title: Eco-friendly interior design mechanism with AI and big data analysis

Authors: Jing Wang

Addresses: Henan Institute of Technology, Xinxiang, Henan, China

Abstract: The interior design practices can promote overall sustainability in urban environments by leveraging Artificial Intelligence (AI) and advanced data analytics techniques. In this paper, a smart eco-friendly interior design optimisation approach is proposed based on wearable sensor data analysis. Meanwhile, real-time analysis of user behaviour is performed using sensor data from wearable devices. The gated recurrent unit identifies user behaviour, establishes a correlation between user behaviour and comfort requirements, updates user comfort preferences and dynamically estimates model parameters based on environmental sensor data. Subsequently, a model predictive control-based solution method for optimising energy efficiency in smart interior design is introduced. Experiments are conducted involving four typical user behaviour scenarios, demonstrating improvements in the economic and comfort aspects of smart, eco-friendly interior design. Adopting model predictive control-based optimisation decisions in eco-friendly interior design solutions within smart cities offers numerous benefits. It enhances energy efficiency, promotes sustainability, enhances user comfort and well-being, reduces costs and facilitates data-driven decision-making. By incorporating these optimisation techniques into the design and operation of interior spaces, smart cities can advance their eco-friendly initiatives and create more sustainable built environments.

Keywords: eco-friendly interior design; artificial intelligence; big data; model predictive control.

DOI: 10.1504/IJCAT.2025.149373

International Journal of Computer Applications in Technology, 2025 Vol.76 No.3/4, pp.265 - 274

Received: 17 Oct 2024
Accepted: 24 May 2025
Published online: 27 Oct 2025 *

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