Accurately　predicting　the　refrigerant　mass　flow　rate　through　the　electronic　expansion　valve　(EEV)　is　crucial　for　improving　the　system＇s　performance　and　achieving　intelligent　control.　However,　the　refrigerant　mass　flow　rate　model　applicable　to　the　direct-expansion　ice　thermal　storage　(DX-ITS)　system　for　a　wide　range　of　flow　rates　is　rare　in　the　open　literature.　In　this　study,　Buckingham-pi　theorem　and　artificial　neural　network　(ANN)　are　adopted　to　predict　the　refrigerant　mass　flow　rate　through　the　EEV　of　the　DX-ITS　system　using　R134a.　The　dimensionless　pi-groups　and　optimal　number　of　neurons　in　hidden　layers　of　ANN　model　are　obtained.　The　obtained　ANN　model　shows　good　accuracy,　and　over　95.7　%　of　the　predicted　data　are　within　the　15　%　error　band.　Results　indicate　that　the　EEV　outlet　pressure　has　a　significant　impact　on　the　refrigerant　mass　flow　rate　compared　with　the　inlet　pressure,　the　average　increase　in　refrigerant　mass　flow　rate　is　43.76　kg/h　with　an　outlet　pressure　increase　of　0.05　MPa.　Moreover,　the　refrigerant　mass　flow　rate　gently　rises　with　the　increase　of　superheat　temperature　under　fixed　EEV　inlet　and　outlet　pressure.　On　average,　every　2　degrees　C　increase　in　superheat　temperature　leads　to　an　approximately　3.98　kg/h　increase　in　refrigerant　mass　flow　rate.