Improvement of Energy Efficiency of Markov ACMV Systems based on PTS Information of Occupants

This study proposes a novel method of energy efficiency improvement in Air-Conditioning and Mechanical Ventilation (ACMV) systems on the basis of occupant thermal states (ComforyDiscomfort) evaluated by Predictive Thermal State (PTS) models. An ACMV Operating State (OS) algorithm is proposed and integrated with PTS models under assumptions of Markov Decision Process (MDP). The ACMV OS algorithm and the developed PTS models are applied in our thermal laboratory. The results show that NN based PTS models perform better than ELM based ones in terms of energy saving in our case studies. The optimal sampling time of the applied ACMV OS algorithm is 10 mins without the issues of system lagging and losing sharpness of tracking thermal states of occupants. The experimental results show that the proposed ACMV OS algorithm can significantly reduce around 10 kWh out of 74 kWh (about 13.5% energy saving) daily in laboratory conditions without compromising the thermal comfort level of occupants.