Building　flexible　energy　systems　(BFES)　can　be　enhanced　by　introducing　storage　batteries.　Providing　timely　scheduling　strategies　for　flexible　resources　can　improve　the　system＇s　energy　utilization.　BFES＇s　scheduling　strategies　are　often　adjusted　based　on　Intra-hour　photovoltaic(PV)　output.　Intra-hour　PV　power　generation　can　be　predicted　by　analyzing　cloud　imagery　data;　however,　this　method　does　not　meet　the　economic　requirements　of　BFES　due　to　its　cost　and　instrumentation.　Therefore,　this　study　proposes　a　low-cost　method　for　intra-hour　PV　power　generation　prediction　(IHP)　for　BFES　and　explores　the　impact　of　integrating　this　approach　into　BFES　on　the　rate　of　renewable　energy　consumption.　This　method　combined　low-quality　sky　images　captured　using　fisheye　cameras　installed　above　buildings　with　historical　electricity　generation　data　and　employed　convolutional　neural　networks.　The　feasibility　of　the　IHP　method　and　the　advantages　of　incorporating　it　into　BFES　were　verified　by　applying　it　to　a　building　equipped　with　PV　devices　in　Changping,　Beijing.　The　performance　of　the　proposed　model　algorithm　was　compared　with　those　of　existing　models.　The　proposed　method　achieved　average　prediction　accuracy　improvements　of　25.1　and　12.5　%　compared　with　existing　models　under　sunny　and　cloudy　conditions,　respectively.　Under　clear　conditions,　the　model　could　predict　the　PV　power　generation　within　the　next　25　min,　whereas　under　cloudy　conditions,　the　model　could　predict　the　power　generation　within　10　min.　In　addition,　integrating　IHP　into　the　scheduling　strategy　of　BFES　can　improve　the　renewable　energy　consumption　rate　by　44.4　%　on　the　original　basis.