To　ease　the　Predicted　Percentage　of　Dissatisfied　(PPD)　measurement　and　calculation　and　consider　the　adaptive　behaviors　of　residents,　the　present　research　proposed　an　adaptive　thermal　discomfort　evaluation　model:　Air-conditioner　based　Adaptive　Predicted　Percentage　of　Dissatisfied　(aaPPD).　First,　the　indoor　temperature,　outdoor　temperature,　and　humidity　data　of　the　residential　buildings　in　five　cities　within　three　climate　regions　in　China　were　collected.　Second,　through　the　air-conditioner-turning-on　(ATO)　judgment　algorithm,　the　data　from　when　the　air　conditioner　was　turned　on　could　be　extracted　from　the　original　data,　and　then　transformed　via　the　Monte　Carlo　sampling　method　to　obtain　a　dataset　of　the　ATO　percentage　of　residents　within　specific　indoor　and　outdoor　environments.　Finally,　a　nonlinear　model　was　built　according　to　this　dataset.　The　final　R-2　of　this　model　was　0.833.　This　model　utilized　data　from　resident　ATO　behaviors　as　the　basis　for　determining　the　thermal　discomfort　and　avoiding　the　psychological　impact　on　the　subjects　when　filling　out　the　thermal　sensation　vote　questionnaire.　Moreover,　when　compared　with　the　PPD　model,　the　aaPPD　model　simplified　the　variables　to　obtain　the　calculation　parameters　more　conveniently　and　ease　the　thermal　discomfort　testing　and　predictions,　which　could　allow　for　better　adaptation　to　the　early　architectural　design　stage　working　characteristics.