Optimizing Indoor Lighting With CNN and LSTM Enhancing Comfort and Efficiency

• Published in: International journal of ambient computing and intelligence Vol. 15; no. 1; pp. 1 - 23
• Main Authors: Yu, Kun, Dong, Guangda, Li, Xuesi
• Format: Journal Article
• Language: English
• Published: Hershey IGI Global 22.07.2025
• Subjects: Accuracy; Artificial intelligence; Artificial neural networks; Back propagation; Back propagation networks; Business metrics; Communication; Deep learning; Energy consumption; Energy efficiency; Flexibility; Internet of Things; Light; Lighting; Lighting systems; Machine learning; Neural networks; Optimization; Predictions; Signal to noise ratio; Smart houses; User behavior; User needs
• ISSN: 1941-6237; 1941-6245
• DOI: 10.4018/IJACI.386084

This paper presents an intelligent indoor lighting control system based on deep learning. The system employs a convolutional neural network to optimize the layout and positioning accuracy of indoor visible light communication sources and integrates a long short-term memory network with a backpropagation neural network to build a smart lighting prediction module. Experimental results demonstrate that the proposed system reduces indoor lighting parameter failure rates to below 12%, expands the effective area of signal-to-noise ratio, and lowers personnel positioning error by up to 18%. Furthermore, the model achieves high prediction accuracy when trained on historical lighting behavior data, with predicted lighting states closely matching actual user preferences. These improvements enhance user comfort and enable more personalized and energy-efficient lighting control.