A novel multi-zone uniform lighting control strategy for buildings based on SSA and ELM

Uniform indoor lighting is essential for enhancing visual comfort, reducing glare and shadows, and improving occupant productivity. However, achieving consistent illumination across multiple zones is challenging due to the dynamic interplay between daylight and artificial lighting, as well as the mutual influence among distributed lighting units. This study proposes a novel multi-zone uniform lighting control strategy based on a modular and scalable distributed framework. The system integrates both daylight and electric lighting control: a fuzzy logic controller regulates venetian blinds in response to changing daylight conditions, while a deterministic policy iteration algorithm, built upon an improved Extreme Learning Machine (ELM), manages artificial lighting. To enhance the generalization and adaptability of the ELM, the Sparrow Search Algorithm (SSA) is employed for hyperparameter optimization. A prototype system incorporating multiple luminaires and shading devices was developed and tested under real-world dynamic lighting conditions. Experimental results demonstrate that the proposed method effectively maintains uniform illumination across multiple areas, with a maximum relative error of less than 10% between actual and target illumination levels. The system exhibits strong real-time performance, stability, and adaptability to environmental changes. This research provides a practical, learning-based control solution suitable for complex indoor environments, and offers additional insights into the design of intelligent lighting systems for modern buildings.