The　power　generation　of　distributed　photovoltaic　(PV)　systems　often　suffers　interference　due　to　shadows　cast　by　surrounding　buildings.　To　improve　the　accuracy　of　PV　power　forecasts,　this　paper　presents　a　PV　power　prediction　method　that　takes　shadow　effects　into　consideration.　Firstly,　a　convenient　PV　shadow　model　was　formulated　for　predicting　the　proportion　of　PV　shaded　(PPS),　using　theoretical　derivation　and　a　zoning　shading　judgment　strategy.　Subsequently,　a　PV　power　prediction　method　was　proposed　based　on　PV　shadow　forecasting　and　the　convolutional　deep　neural　network　algorithm.　Finally,　this　method　was　applied　to　a　carport　PV　system　in　a　building　in　Beijing,　China,　and　SHAP　analysis　was　utilized　for　the　interpretation.　The　results　show　that　the　proposed　method　can　automatically　recognize　shadow　conditions,　and　significantly　improve　the　predictive　accuracy　of　PV　power,　reducing　the　MAE　by　10.1　%　and　increasing　the　R2　value　from　0.91　to　0.94.　The　ranking　of　feature　importance　to　the　PV　power　prediction　model　is　as　follows:　solar　radiation,　hour,　ambient　temperature,　PPS,　and　relative　humidity.　This　study　offers　a　feasible　solution　for　predicting　power　generation　of　PV　systems　that　are　subject　to　shadow　shading　from　buildings.　©　2025