Optimal control of TABS using a combined method of MPC and heat load prediction

Abstract

Thermally activated building systems (TABS), which use the building frame as a radiative and thermal storage site, have attracted attention as a means of achieving a thermal environment in office spaces that combines comfort and energy conservation. However, TABS have a large thermal capacity and a slow thermal response, making it desirable to introduce a control method based on model predictive control (MPC). There are problems with this method, such as its inability to predict periodic fluctuations in thermal load. Therefore, in this study, we propose a control method combining MPC and heat load prediction using a Neural Network, a type of supervised learning in machine learning, for an office space with TABS. The method was validated by co-simulation using CFD and MATLAB/Simulink on a one-span (7.20 × 7.20 × 3.87 m) TABS model. As a result, the proposed method reduced the integrated water flow rate and improved the energy performance while improving the control performance.