Evaluation of multiple predictive control strategies to optimally use building thermal mass to reduce annual operation costs and associated GHG emissions

Over the past few decades, building energy operations in response to extreme weather events have been one of the major causes of increasing utility costs and GHG emissions. Placing large amounts of thermal mass within a building has been shown to be effective in reducing energy loads from both economic and environmental perspectives. In addition, predictive control strategies that control pre-heating or pre-cooling of the building thermal mass can highly support the reductions in heating and cooling energy demand. However, it remains unclear which predictive control strategies are most effective in reducing building operation costs and associated GHG emissions. Research on how to increase the efficiency of the structural thermal mass of residential and office buildings located in cold climates is relatively scarce. Thus, this study addresses the existing research gap on how predictive control strategies can improve the operation cost and GHG emission saving effects when applied to various types of building thermal mass. Three predictive control strategies have been compared: night ventilation, off-peak pricing, and PV systems. The results demonstrate that night ventilation control schemes are highly effective in reducing electricity costs and GHG emissions associated with space cooling. Moreover, the predictive control strategy using solar power generation was found to be most effective in saving costs and GHG emissions for both heating and cooling. In conclusion, scientists and building engineers should strive to develop predictive control strategies that can maximize the synergy effects of multiple control schemes with different peak-load shifting effects.