Linking dataset quality and MPC in buildings: impact of temporal resolution

Abstract

Abstract This study aims at assessing the impact of dataset quality on the performance of Model Predictive Control (MPC). The dataset feature, which is the target of this study, is temporal resolution, which applies to both data logging and the controller time step. A high temporal resolution might result in a more accurate predictive model, but it increases the need for data storage as well as the computational load on the model training. From the controller side, increasing the temporal resolution might lead to better control performance, but it sabotages the real-time response of the system. First, predictive models are developed based on datasets with different temporal resolutions. Subsequently, these predictive models are implemented within an MPC. Results reveal that decreasing the time step lower than 1 hour does not significantly improve the performance of the MPC. However, increasing the time step of MPC above 1 hour deteriorates its performance. Real-time response of the controller is a crucial criterion which deteriorates as the time step shortens. Hence, a suitable choice of temporal resolution is essential for developing a predictive model and MPC. In our case, a resolution of 1 hour is enough to guarantee a good performance of the controller.