What question did this study set out to answer?

This research aims to develop a machine learning-based control system for indoor environments to reduce energy consumption and enhance user comfort.

June 1, 2026

Discussion on indoor environment optimisation and low-carbon energy-saving strategies based on machine learning

Key Points

This research aims to develop a machine learning-based control system for indoor environments to reduce energy consumption and enhance user comfort.
Developed the machine learning-based indoor environment control system (MIECS) using reinforcement learning, deep learning, and edge computing.
Utilized heterogeneous graphs to model device-environment interactions within a three-layer architecture.
Conducted experimental validations comparing MIECS to conventional control methods.
Achieved a 23.7% reduction in energy consumption compared to conventional methods (p<0.05).
Increased user comfort by 18.5% relative to traditional approaches.
Demonstrated improved sample efficiency and convergence speed in system performance.

Abstract

Building energy consumption represents a significant portion of global energy use, demanding efficient control strategies. Traditional rule-based methods lack adaptability, while data-driven approaches struggle with generalisation. This study presents the machine learning-based indoor environment control system (MIECS), which integrates reinforcement learning, deep learning, and edge computing within a three-layer architecture. By modelling device-environment interactions with heterogeneous graphs, MIECS improves sample efficiency and convergence speed. Experimental results show a 23.7% reduction in energy consumption and an 18.5% increase in user comfort compared to conventional methods, providing a scalable, adaptive solution for intelligent building management.

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Discussion on indoor environment optimisation and low-carbon energy-saving strategies based on machine learning

Key Points

Abstract

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