Key points are not available for this paper at this time.
Heating, ventilation, and air conditioning (HVAC) systems are the primary energy consumers in modern office buildings, with chillers consuming the most energy. As critical components of building air conditioning, the effective functioning of HVAC systems holds substantial importance for energy preservation and emission mitigation. To enhance the operational performance of HVAC systems and accomplish energy conservation objectives, precise cooling load forecasting is essential. This research employs an office facility in Binzhou City, Shandong Province, as a case investigation and presents a day-ahead scheduling-based model predictive control (MPC) approach for HVAC systems, which targets minimizing the overall system power utilization. An attention mechanism-based long short-term memory (LSTM) neural network forecasting model is developed to predict the building’s cooling demand for the subsequent 24 h. Based on the forecasting outcomes, the MPC controller adopts the supply–demand equilibrium between cooling capacity and cooling demand as the central constraint and utilizes the particle swarm optimization (PSO) algorithm for rolling optimization to establish the optimal configuration approach for the chiller flow rate and temperature, thereby realizing the dynamic control of the HVAC system. To verify the efficacy of this approach, simulation analysis was performed using the TRNSYS simulation platform founded on the actual operational data and meteorological parameters of the building. The findings indicate that compared with the conventional proportional–integral–derivative (PID) control approach, the proposed day-ahead scheduling-based MPC strategy can attain an average energy conservation rate of 9.23% over a one-week operational period and achieve an energy-saving rate of 8.25% over a one-month period, demonstrating its notable advantages in diminishing building energy consumption.
Zhou et al. (Fri,) studied this question.