The　relationship　between　environmental　factors　with　the　power　output　of　photovoltaic　(PV)　stations　is　unclear　due　to　the　non-linear　characteristics　of　PV　systems,　which　is　challenging　for　PV　power　forecasting　technology.　To　cope　with　these　challenges,　a　hybrid　forecasting　approach　called　hybrid　K-Means++　and　Deep　Neural　Network　with　input　and　output　adjusting　structures　(K-IAOA-DNN)　is　proposed　to　accurately　predict　PV　power　output.　The　proposed　forecasting　approach　designs　several　features　for　K-Means++　to　search　for　similar　samples,　reducing　the　complexity　of　the　following　forecasting　model.　Due　to　changeable　environmental　conditions　and　characteristics　of　PV　systems　like　degradation,　the　prediction　result　of　a　forecasting　model　may　deviate　from　the　expected　one.　Therefore,　IAOA-DNN　model　is　developed　by　using　a　DNN　adopting　the　two　proposed　structures　e.　g.,　the　input　adjusting　structure　and　the　output　adjusting　structure.　The　input　adjusting　structure　uses　several　trainable　parameters　to　determine　the　importance　of　each　meteorological　input　for　further　feature　extracting　by　DNN.　The　output　structure　in　the　prediction　model　is　used　for　analyzing　features　extracted　from　DNN　to　generate　a　scalar　factor　fine-tuning　the　output　of　DNN.　Additionally,　the　proposed　method　provides　more　accurate　forecasting　results　with　average　RMSE,　NRMSE,　and　MAE　values　of　14.43,　0.048,　and　9.53　kW,　respectively.