Model predictive control of a building renewable energy system based on a long short-term hybrid model

Considering solar photovoltaic (PV) usage in building energy systems (BES), energy systems that combine batteries and solar PV (PVB) have been widely used in buildings. Ensuring the safety of the battery and the operation requirements of other types of equipment to the extent possible while achieving the optimization goal has become an important challenge in the implementation of this system. In this case, model predictive control (MPC) has gained increasing attention in the operation control of BES owing to its strong fitting ability. However, the prediction models at the core of MPC are not adequately designed for the BES optimization problem, and numerous studies have not yet been modeled and tested in real buildings. In this study, we used a long- and short-term hybrid prediction model in the MPC framework. The proposed prediction models and MPC framework were validated using a measured dataset from a real office building in Japan. The results show that, compared with the currently used baseline control logic, the proposed hybrid prediction MPC framework can improve the battery safety index by 81.6%, while the combined heat and power continuous operation index can also be improved by 36.4%. Compared with the conventional MPC framework, the proposed hybrid prediction MPC framework improves the optimization of off-grid operation by approximately 69% while maintaining the remaining optimization objectives. •Off-grid oriented operation and equipment safety are considered at the same time.•Hybrid prediction model is designed for model predictive control.•Modeling and algorithm are based on the measured data of real existing buildings.