Paper Conference

Proceedings of Building Simulation 2023: 18th Conference of IBPSA

     

Field study of data-driven model predictive control for GEOTABS in an ultra-low energy office building

Sunghwan Lim 1,2, Xu Han 1,2, Elence Xinzhu Chen 1,2, Ali Malkawi 1,2
1 Graduate School of Design, Harvard University
2 Center for Green Buildings and Cities, Harvard University


DOI: https://doi.org/10.26868/25222708.2023.1428
Abstract: The combination of thermally activated building system (TABS) and ground source heat pump (GSHP), which is known as GEOTABS, has been demonstrated to be a promising technology to improve building energy efficiency and thermal comfort. However, the control of such systems is challenging due to the high thermal inertia and slow thermal dynamics. This paper proposes a novel data-driven model predictive control method and demonstrates it in a ultra-low energy office building called HouseZero. First, the thermal dynamics of the target zone was modeled with data-driven model based on historical operational data. Second, a multi-objective model predictive control optimization problem was formatted to maximize the energy efficiency subject to maintaining the thermal comfort. Then, the implemented control algorithm was deployed to the building systems through the Internet of Things (IoT) infrastructure of the building. The experiment result concluded that the data-driven model predictive control saved 13.78% of energy compared to the rule-based control and number of the unmet hours in therms of thermal comfort during the occupied hours decreased by 80%.
Keywords: MPC, data-driven, GEOTABS, optimization, thermal comfort
Pages: 3391 - 3397
Paper:
bs2023_1428