Efficient　and　accurate　photovoltaic　(PV)　modeling　plays　an　important　role　in　optimal　evaluation　and　operation　of　PV　power　systems.　Using　current?voltage　(I-V)　curves　measured　at　different　operating　conditions,　a　novel　extreme　learning　machine　(ELM)　based　modeling　method　is　proposed　for　characterizing　the　electrical　behavior　of　PV　modules,　which　features　high　training　speed　and　generalization　performance.　Firstly,　a　voltage-current　grid　based　method　is　used　to　resample　each　raw　measured　I-V　curve　for　reducing　data　redundancy　of　I-V　curves,　a　slope　change　based　detection　method　is　proposed　to　exclude　the　abnormal　I-V　curves　for　improving　the　data　quality,　and　an　irradiance-temperature　grid　based　method　is　applied　to　downsample　the　dataset.　Secondly,　a　single　hidden-layer　feedforward　neural　network　(SLFN)　is　proposed　as　the　model　structure,　which　is　then　trained　by　the　ELM　learning　algorithm.　Particularly,　the　configuration　of　the　ELM　is　optimized　by　cross-validation.　Finally,　the　proposed　ELM　based　PV　modeling　method　is　verified　and　tested　on　the　preprocessed　large　datasets　of　I-V　curves　of　six　PV　modules　from　the　National　Renewable　Energy　Laboratory.　Moreover,　in　order　to　verify　the　advantage,　the　ELM　based　PV　modeling　is　further　compared　with　some　commonly　used　machine　learning　based　methods.　Experimental　results　demonstrate　that　the　proposed　ELM　based　PV　modeling　method　features　fast　training,　high　accuracy　and　generalization　performance.　The　comparison　results　further　indicate　that　the　ELM　based　model　is　greatly　superior　in　terms　of　the　training　time,　and　is　slightly　better　than　other　algorithms　in　terms　of　the　model　accuracy　and　generalization　performance.