A method for energy consumption optimization of air conditioning systems based on load prediction and energy flexibility

A new method for heating ventilation and air conditioning (HVAC) energy consumption optimization based on load prediction and energy flexibility is proposed. First, the energy consumption prediction of the chillers and air conditioning terminals is made. Then, an optimal chiller loading (OCL) equation is built, and is new in the following aspects: the electricity consumption of air conditioning terminals is included and amended by a penalty coefficient to consider thermal comfort. This penalty coefficient is calculated based on energy flexibility. The prediction results are used as constraints of the OCL equation. Next, the sensitiveness of the system's energy consumption with different penalty coefficients and different settled comfort air temperatures are tested. All cases are solved by the particle swarm optimization (PSO) algorithm and validated by the genetic algorithm (GA). Finally, economic analyses are made. The results show that the comprehensive energy-saving ratio is about 10%, and the discounted payback value is 5.8 years. The penalty coefficient is more sensitive than the settled comfort air temperature for the system's energy saving. This proposed method is significant for improving the reliability of the feedforward control strategy and reducing the response time of the feedback control strategy.