2022/01/21 by Hossein Kalantar-Neyestanaki, Kalantar-Neyestanaki, Hossein, M. Soltani +2
Computer Science · Engineering · #Advanced Control Systems Optimization #Building Energy and Comfort Optimization #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Smart Grid Energy Management #Systems and Control (eess.SY) #cs.LG #cs.SY #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2201.08909
Index terms: Stochastic Model predictive control (MPC), building energy management systems (BEMSs), renewable energy resources (RES), thermal energy storage system (TESS), Mixed-integer linear stochastic optimization
arxiv created 2022/01/21 · openalex publication_date 2022/01/21 · arxiv updated 2022/01/25 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
The building sector accounts for almost 40 percent of the global energy consumption. This reveals a great opportunity to exploit renewable energy resources in buildings to achieve the climate target. In this context, this paper offers a building energy system embracing a heat pump, a thermal energy storage system along with grid-connected photovoltaic thermal (PVT) collectors to supply both electric and thermal energy demands of the building with minimum operating cost. To this end, the paper develops a stochastic model predictive control (MPC) strategy to optimally determine the set-point of the whole building energy system while accounting for the uncertainties associated with the PVT energy generation. This system enables the building to 1-shift its electric demand from high-peak to off-peak hours and 2- sell electricity to the grid to make energy arbitrage.