A novel blinds control approach based on dynamic radiance and solar radiation energy prediction

Abstract

Automatic shading devices are widely employed in office buildings to enhance daylight comfort and reduce electricity consumption. However, conventional blinds control methods rely heavily on numerous sensors to monitor indoor daylight conditions, posing challenges in implementing automated control systems. A novel blinds control approach based on dynamic radiance and solar radiation energy prediction is proposed to address this issue. Instead of relying on illumination sensors, the method utilizes the Bidirectional Scattering Distribution Function (BSDF) and a sky model-based three-phase approach to calculate indoor illumination. Artificial neural networks are employed to predict transmitted solar radiation energy, thereby minimizing energy consumption. Furthermore, the multiple criteria decision-making model is applied to determine the optimal angle for the blinds. Simulation experiments demonstrated that this approach achieved a significant reduction of approximately 17% in energy consumption compared to a fixed angle of 90° in the cooling season. And the average illumination of the indoor work plane can be effectively maintained at the recommended level, as ensures improving occupants’ comfort. Practical Application: The proposed blinds control approach has practical applications in building automation control systems. By effectively reducing building energy consumption, it offers an efficient alternative to traditional control methods. Notably, this approach minimizes reliance on sensors, making it a cost-effective and sustainable solution for optimizing building energy usage.