Near-optimal adaptive predictive control model study for roller shades in office spaces

Abstract

As improved building energy efficiency becomes mandatory, dynamic shading systems are expected to reduce building energy demand because their control mechanisms are increasingly self-adaptable to changes in the environment. Various shading control strategies had been proposed and compared in existing literature, but their energy performance can be further improved with a more comprehensive consideration of environmental changes and indoor demand. This study develops and validates an adaptive predictive control (APC) model for roller shades that combines simulation-based optimization of shading control with machine learning. The model predicts the appropriate position of roller shades based on multiple environmental parameters, minimizing the building energy demand. EnergyPlus and Python provide continuous energy simulation and data processing. Using pre-simulated datasets, machine learning algorithms develop APC models. The study scenario is a typical office space in Guangzhou, China. Quantitative analysis of the simulation results elucidates the effectiveness of the method. The performance of the model was evaluated using historical meteorological data from 2020. The proposed control model was able to achieve nearly optimal energy efficiency, reducing energy demand by 38.3% while reaching annual Useful Daylight Illuminances of 72.9%.