The　dynamic　fluctuations　in　occupant　flow　within　airport　terminals　contribute　to　delays　in　the　system　＇　s　response　under　traditional　feedback　control.　This　imbalance　between　supply　and　demand　can　result　in　discomfort　and　unnecessary　energy　consumption.　While　Occupant-Based　Model　Predictive　Control　(OBMPC)　has　gained　research　interest　as　it　leverages　occupant　forecasts　to　enhance　control　performance,　there　is　limited　knowledge　regarding　its　application　in　the　air-conditioning　systems　of　airport　terminals,　mainly　due　to　the　intricate　dynamics　of　occupant　flow　in　terminal　buildings.　In　this　study,　we　proposed　a　model　predictive　control　strategy　based　on　dynamic　occupant　flow　with　the　goal　of　fast　responding　to　occupant　flow　and　reducing　energy　consumption.　We　integrated　mathematical　formulas　and　the　Anylogic　simulation　software　to　predict　occupant　variations　in　the　baggage　claim　hall.　The　model　predictive　control　strategy　of　the　air　conditioning　system　was　proposed　with　the　predicted　occupant　flow.　The　proposed　strategy　was　evaluated　throughout　the　whole　cooling　season　in　the　validated　simulation　model　of　the　air　handling　unit　system　of　the　baggage　claim　hall　at　an　airport　terminal.　The　results　demonstrated　that　the　proposed　strategy　could　effectively　respond　to　variations　in　occupancy　and　achieve　10%　energy　savings　throughout　the　entire　cooling　season,　compared　to　conventional　feedback　control.　Our　work　presents　a　viable　solution　to　address　system　regulation　lag　and　reduce　energy　consumption　without　jeopardizing　thermal　comfort.