Accurate　and　reliable　modeling　of　photovoltaic　(PV)　modules　is　significant　for　optimal　design,　operation　and　evaluation　of　PV　systems.　PV　models　can　be　classified　into　equivalent　circuit-based　white　box　models　and　data-driven　black　box　models.　Due　to　the　difficulty　to　obtain　the　ground　true　model　parameters　and　the　limitation　posed　by　the　predetermined　model　structure,　white-box　modeling　methods　generally　suffer　relatively　low　accuracy　and　generalization　performance　for　arbitrary　operating　conditions.　In　addition,　reported　black-box　models　are　based　on　the　conventional　artificial　neural　networks　(ANN)　that　are　efficient　but　have　limited　performance.　In　this　study,　motivated　by　the　high　performance　of　fast　developing　deep　learning　techniques,　we　propose　a　novel　black-box　modeling　method　for　the　PV　modules　using　a　new　modified　one-dimensional　deep　residual　network　(1-D　ResNet)　and　measured　I-V　characteristic　curves,　which　can　predict　a　whole　I-V　curve　at　a　time　for　arbitrary　operating　conditions.　To　alleviate　the　overfitting　issue　caused　by　imbalanced　data,　original　I-V　curve　datasets　with　highly　non-uniform　operating　conditions　are　resampled　by　a　grid　sampling　approach　to　obtain　the　datasets　with　relatively　uniform　conditions　for　the　subsequent　modeling.　The　proposed　1-D　ResNet　based　model　is　comprehensively　verified　and　compared　with　a　proposed　single-diode　based　white-box　model　and　three　other　conventional　ANN　based　black-box　models,　using　large　datasets　of　measured　I-V　characteristic　curves　from　the　National　Renewable　Energy　Laboratory　(NREL).　Experimental　results　indicate　that　black-box　models　are　generally　better　than　the　white-box　model.　Especially,　the　proposed　1-D　ResNet　based　PV　model　is　obviously　superior　to　other　three　conventional　ANN　based　black-box　models,　in　terms　of　accuracy,　generalization　performance　and　reliability.