Scheduling-based real time energy flow control strategy for building energy management system

• Published in: Energy and buildings Vol. 75; pp. 239 - 248
• Main Authors: Kang, Shin Jae, Park, Jungsung, Oh, Ki-Yong, Noh, Jae Gu, Park, Hyunggon
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier B.V 01-06-2014; Elsevier
• Subjects: Applied sciences; BEMS; Building technical equipments; Buildings; Buildings. Public works; Computation methods. Tables. Charts; Energy management and energy conservation in building; Environmental engineering; Exact sciences and technology; Optimal energy flow; Real-time control; Scheduler; Structural analysis. Stresses; Real-time control; Optimal energy flow; BEMS; Scheduler; DR; Consumption; Performance evaluation; Energy flow; Costs; Buildings; Scheduling; Forecast model; Real time; Long term; Modeling; Optimization; Simulation; Strategy; Energy management; Comparative study
• ISSN: 0378-7788
• DOI: 10.1016/j.enbuild.2014.02.008

We propose the energy flow (of components within a building) control strategy for BEMS. The strategy includes prediction, long-term scheduling, and real time control (RTC). For every service interval, the procedure from prediction to RTC is repeated. [Display omitted]
•We propose the energy flow (of components within a building) strategy for BEMS.•The strategy includes prediction, long-term scheduling, and real time control (RTC).•For every service interval, the procedure from prediction to RTC is repeated.•Potential cost savings will be 10–20% compared to a conventional RTC scheme.
We propose a novel strategy for BEMS (Building Energy Management System), which efficiently controls energy flows in a building so as to minimize the total cost of energy for a finite period. We also consider Demand Response (DR) events during the period. The proposed strategy includes prediction, long-term scheduling, and real-time control (RTC) of components within a building. During the period, the process from prediction to RTC is iterated in every time unit when the system status is changed by a dynamic environment. The scheduler determines the optimal energy flows based on the prediction, and RTC utilizes the scheduling result so that the energy flow can be adaptively controlled in a dynamic environment. Finally, the system status change information is fed back for the next iteration. Simulation results indicate potential cost savings that are approximately 10–20% compared to a typical BEMS with a conventional RTC scheme.