Model predictive lighting control for a factory building using a deep deterministic policy gradient. Issue 2 (4th March 2022)

• Record Type: Journal Article
• Authors: Kim, Young Sub; Shin, Han Sol; Park, Cheol Soo
• Is Part Of: Journal of building performance simulation. Volume 15:Issue 2(2022)
• Page Start: 174
• Page End: 193
• Publication Date: 2022-03-04
• Subjects: Daylight responsive lighting control -- lighting simulation -- model predictive control -- reinforcement learning -- deep deterministic policy gradient (DDPG); 690.0113
• DOI: 10.1080/19401493.2021.2019310
• Languages: English
• ISSNs: 1940-1493

Abstract

Abstract : This paper presents an integrated lighting control that employs a daylit illuminance prediction model for a large, open-spaced factory building in which interactions between multiple luminaires and points of the workplane exist. The prediction model was developed with Radiance and consists of daylight and electric lighting prediction components. Both components showed reliable accuracy after calibration with measured illuminance (daylighting: MBE = 4.9%, CVRMSE = 24%; electric lighting: MBE = 3.7%, CVRMSE = 7.7%). An optimal policy trained by the deep deterministic policy gradient determines the dimming levels of multiple luminaire groups. The policy is developed with an artificial neural network model whose input is the current state (daylight distribution) and whose output is an action (lighting control variables). The model could provide an appropriate amount of electric lighting that meets the target illuminance and uniformity, while only 54% of the average consumption power (7, 292 W) was needed compared with that of the existing rule-based control (13, 629 W).