The　purpose　of　renewable　energy　to　generate　power　and　change　the　composition　of　the　energy　mix　is　becoming　more　popular　in　today＇s　world.　The　competition　of　solar　power　plants　in　the　energy　market　and　decreasing　reliance　on　fossil　fuels　for　socio　andeconomic　growth　are　both　facilitated　by　solar　energy　forecasting,　which　is　a　critical　component.　To　overcome　this　issue,　we　presented　an　Adaptive　sea　lion　optimized　genetic　adversarial　network　(ASLO-GAN)　method.　The　purpose　of　the　ASLO-GAN　method　is　to　predict　the　renewable　energy　sources.　The　dataset　of　renewable　energy　sources　(RESs)　is　collected　from　kaggleand　then　the　collected　dataset　is　preprocessed　using　decimal　scale　normalization.　When　extracting　the　data,　the　spearman　rank-order　correlation　(SROC)　method　is　utilized　so　undesirable　data　can　be　omitted　from　the　process.　The　simulation　results　include　RMSE,　MAPE,　R-square,　CV(RMSE)　and　NMBE　that　are　evaluated.　The　main　key　findings　of　NMBE　were　compared　to　other　traditional　approaches　in　order　to　show　the　validity　and　repeatability　of　the　inquiry.