Smart building energy management strategy based on stochastic model predictive control

doi:10.3969/j.issn.2097-0706.2022.10.011

Abstract

Abstract:

Buildings are major energy consumers and carbon emission sources in China.Building energy management systems are vital to improve the energy utilization efficiency，save energy consumption and reduce carbon emissions.However，the prediction uncertainties of ambient temperature，solar irradiation and other environmental factors are detrimental to the economy of the whole system.Accordingly，a smart building energy management method based on chance-constrained stochastic model predictive control considering the characteristics of buildings'heat storage is proposed.By introducing the building thermal dynamic characteristics into the building energy system model，the proposed method descripts the probability of the state constraint violation caused by the prediction deviation of outdoor temperature and solar irradiation.Combining the chance constraint and affine disturbance feedback，the probability constraint can be transformed into deterministic constraint.Then，a smart building energy management model based on chance-constrained stochastic model predictive control is constructed，to realize the optimal operation of each energy-consuming equipment.The simulation results have shown that the proposed method can effectively reduce the operating costs caused by the uncertainty prediction on environmental factors and improve the comfortableness and robustness of the overall system.

Key words: smart building, HVAC system, stochastic model predictive control, optimal scheduling, thermal comfort, carbon emissions, geothermal heat pump

CLC Number:

TK02

ZHANG Yi, FANG Fang. Smart building energy management strategy based on stochastic model predictive control[J]. Integrated Intelligent Energy, 2022, 44(10): 83-90.

Figures/Tables 10

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控制器	J_eco
控制器	β=10	β=100	β=200	β=500
DeMPC	99.4	394.8	723.0	1707.7
SMPC	77.8	94.0	121.9	145.2
PBMPC	65.4	65.4	65.4	65.4

控制器	DV
控制器	β=10	β=100	β=200	β=500
DeMPC	6.2	6.2	6.2	6.2
SMPC	0.55	0.57	0.67	0.04
PBMPC	0	0	0	0