A　novel　intelligent　hybrid　model　is　proposed　in　this　paper　for　accurate　prediction　and　modeling　of　solar　power　plants　using　the　wavelet　transform　package　(WTP)　and　generative　adversarial　networks　(GANs).　In　the　proposed　model,　the　wavelet　transform　is　deployed　for　decomposing　the　solar　energy　signal　into　the　sub-harmonics　followed　by　the　statistical　feature　selection　analyses.　The　GAN　model　as　a　deep　learning　approach　is　proposed　for　learning　each　sub-frequency　and　predicting　the　future　of　the　solar　energy　in　the　short-time　window.　Due　to　the　high　complexity　of　the　solar　irradiance　data　when　training,　an　evolutionary　algorithm　based　on　dragonfly　algorithm　(DA)　is　suggested　to　train　the　generative　and　discriminator　networks　in　the　GAN.　Moreover,　a　three-phase　adaptive　modification　is　suggested　to　enhance　the　search　capabilities　of　the　DA　optimization.　The　efficiency　and　appropriate　performance　of　the　proposed　model　is　examined　and　compared　with　the　most　successful　models　such　as　artificial　neural　networks　(ANNs),　support　vector　machine　(SVM),　time　series,　auto-regressive　moving　average　(ARMA),　and　original　GAN　on　several　benchmarks　for　varied　forecast　time　horizons.　The　simulation　results　on　the　datasets　of　two　regions　show　the　mean　absolute　percentage　error　(MAPE)　of　0.0282　and　0.0262,　when　1-pace　forecast　horizon,　for　the　two　regions　which　increase　up　to　0.0531　and　0.0631　for　6-pace　forecast　horizon.　Moreover,　the　root　mean　absolute　error　(RMSE)　is　0.0473　and　0.0479　for　the　two　regions　at　1-pace　forecast　horizon　which　increased　up　to　0.0895　and　0.0946　for　6-pace　forecast　horizon.　These　results　show　the　more　precise　performance　of　the　proposed　forecast　deep　learning　as　well　as　the　more　optimal　performance　of　the　modified　DA　over　the　other　algorithms　shown　in　the　results.　(C)　2021　The　Author(s).　Published　by　Elsevier　Ltd.