ArticleScientific reports2025
A sensor-fused BIM-based ıntelligent control system for energy-efficient ındoor environmental regulation using deep actor-critic reinforcement learning (DACRL).
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The rising energy demand of the building sector, coupled with the need for high indoor environmental quality (IEQ), necessitates intelligent control strategies. However, traditional methods like rule-based control (RBC) lack adaptability, while model predictive control (MPC) suffers from model dependency. Hence, this study designed and implemented an intelligent indoor environmental control system based on multi-sensor fusion and Building Information Modeling (BIM). At the algorithmic level, this study employed a deep fusion actor-critic reinforcement learning (DACRL) algorithm to achieve energy-efficient dynamic environmental control. The system employs a four-layer architecture with cloud-edge-end collaboration. Layer 1: The perception layer utilizes a multi-modal sensor network built on an STM32L0 low-power micro-controller and an SX1276 LoRa communication module. This architecture enables distributed collection and pre-processing of parameters such as temperature, humidity, CO
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