This paper proposes an optimal Energy Storage System (ESS) scheduling algorithm Building Energy Management System (BEMS). In particular, the focus is placed on how to reduce the peak load using ESS and load forecast. To this end, first, an existing deep learning-based load forecast method is applied to a real building energy prediction and it is shown that the deep learning-based method leads to an accuracy-enhanced load forecast. Second, an optimization problem is formulated in order to devise an ESS scheduling. In the optimization problem, the objective function and constraints are defined such that the peak load is reduced; the cost for electricity is minimized; and the ESS’s lifetime is elongated considering the accuracy-enhanced load forecast, real-time electricity price, and the state-of-charge of the ESS. For the purpose of demonstrating the effectiveness of the proposed ESS scheduling method, it is implemented using a real building load power and temperature data. The simulation results show that the proposed method can reduce the peak load and results in smooth charging and discharging, which is important for the ESS lifetime.

This paper proposes a scheduling method of dual ESSs (Energy Storage Systems) for the purpose of reducing the peak load when there are sudden loads or generation changes during the on-peak time. The first ESS is scheduled once a day based on a day-ahead load prediction, and the second ESS is scheduled every 15 min during on-peak time based on a short-term load prediction by LSTM (Long Short-Term Memory). Special attention is paid to training the LSTM for the short-term load prediction by using the augmented past load data which is generated by adding possible uncertainties to the past load and temperature data. Based on the load forecast, optimization problems for the scheduling are formulated. The proposed scheduling method is validated using load and temperature data from a real building. In other words, when the proposed method is applied to the real building energy data in the case study, it not only shaves the peak load during on-peak time interval effectively but also results in lower electricity price although there are sudden load or temperature changes during the time interval.