During　technical　evaluations　of　cooling　systems　in　residential　buildings,　it　is　necessary　to　consider　the　influence　of　the　household　air-conditioning　(AC)　use　modes.　In　other　words,　how　the　occupants　control　the　AC,　for　instance,　when　it　is　turned　on,　what　the　temperature　setting　is,　and　how　long　it　is　used.　Field　measurements　and　spot　interviews　indicate　that　AC　usage　by　residents　should　be　environmental,　event　and　random　related.　A　reduced-order　AC　conditional　probability　(CP)　model　was　developed　in　this　study　to　describe　AC　usage.　The　AC　CP　model　was　integrated　with　a　building　energy　modeling　program　(BEMP)　to　reflect　the　interaction　of　the　AC　operation　and　the　indoor　environment.　With　consideration　of　stochastic　AC　use　modes,　the　uncertainty　of　user　compositions　was　studied.　Additionally,　simulation　results　revealed　that　AC　use　modes　and　user　compositions　can　cause　up　to　a　4.5-fold　difference　in　the　system　efficiency　of　district　cooling　systems.　The　Lorenz　curve　and　Gini　coefficient　were　applied　in　this　study　to　describe　the　load　distribution　in　a　quantitative　manner.　Through　a　comparison　with　the　constant　schedule　definition　model,　the　study　also　identified　inclusion　of　the　stochastic　feature　of　AC　use　modes　and　their　compositions　in　simulations　as　being　important　to　the　technical　evaluation　of　district　cooling　systems.